Final Report

Development of Oil Slick Thickness Sensors

Imad H. Elhajj, Daniel Asmar, Mahdi Saleh, and Ghassan Oueidat

American University of Beirut – Lebanon

Prepared for:

U.S. Department of the Interior, Bureau of Safety and Environmental Enforcement

Versions:

Version Reason for Change Date Changed by

1.0 Updated mechanical work 15 January 2018 G. Oueidat

1.1 Updated instrumentation and 

testing. Compiled the report 17 January 2018 M. Saleh

1.2 Reviewed the complete report 20 January 2018 D. Asmar

1.3 Included additions 20 January 2018 M. Saleh

1.4 Review and additions 21 January 2018 I. Elhajj

1.5 Additions and corrections 22 January 2018 M. Saleh

1.6 Final review 22 January 2018 I. Elhajj

1.6 Review 1 February 2018 K. McKinney

1.7 Additions and Corrections 12 February 2018 M. Saleh

1.8 Final review 14 February 2018 I. Elhajj

1.9 Final Approval 27 February 2018 K. McKinney

Submitted to:

Name Affiliation Authority

Kristi McKinney Bureau of Safety and 

Environmental Enforcement

Contract Officer 

Representative (COR)

This final report has been reviewed by the BSEE and approved for publication. Approval 

does not signify that the contents necessarily reflect the views and policies of the BSEE, 

nor does mention of the trade names or commercial products constitute endorsement or 

recommendation for use.

This study was funded by the Bureau of Safety and Environmental Enforcement (BSEE), 

U.S. Department of the Interior, Washington, D.C., under Contract E17PC00001.
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Executive Summary

The Bureau of Safety and Environmental Enforcement’s (BSEE) Oil Spill Preparedness Division 

(OSPD) contracted the American University of Beirut (AUB) to develop two oil thickness sensors under 

BSEE Award #E17PC00001. These sensors are designed to measure the thickness of various crude and 

refined oils on water, and wirelessly communicate thickness information in near real-time. While the 

first sensor (referred to as Capacitive and shown in Figure 1) is intended to measure thickness in the 

range of 3mm and above, the second (referred to as Spectro) is intended for thicknesses below 3mm. 

The Capacitive sensor relies on measuring the capacitance of the oil/water/air that it contacts and uses

this data to estimate the locations of the oil-air and oil-water interfaces. Determining instantaneous 

interface locations of each fluid along the probe provides the data necessary to calculate the thickness 

of the oil layer. The Capacitive sensor is designed to vertically mount to a skimmer, boom, or floating 

buoy and provide thickness readings remotely or to be hand held.

Figure 1: Capacitive Oil Thickness Sensor

The Capacitive sensor was designed to operate in a dynamic environment, where it travels through 

waves and measures thickness through varying water conditions. The Capacitive sensor will be used in

oil spills to provide slick thickness estimates, in order to guide response operators during oil recovery 

operations. The Spectro sensor, shown in Figure 2, is a free-floating sensor that uses LED-based 

spectrometry to measure very thin layers of oil with high resolution (up to a thickness of a few 

millimeters). This sensor is used to determine the presence of a thin oil sheen.

This report details the main milestones of the project and is not meant to be a comprehensive log of all 

tasks carried out. The report discusses preliminary designs of the sensors including initial testing, 

experimental testbeds developed, design refinement and testing, and Ohmsett testing. The tests 

conducted at Ohmsett were performed as per a predefined test matrix (with exceptions); assisted by the 

team at Ohmsett, AUB engineers tested both Capacitive and Spectro sensors’ operations and 

measurements. Testing of both sensors took place between November 27 and December 1, 2017.
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Figure 2: LED-based sensor

The testing at Ohmsett for the capacitive sensor included (1) indoor dipping tests in the small tank using 

four test oils, (2) outdoor dragging tests at three speeds using the movable bridge mounted over the 

main tank in calm water conditions using two test oils, (3) outdoor wave testing with the sensor attached 

to commercially available oil skimmer in the main tank and subjected to multiple wave conditions using 

one test oil. The testing at Ohmsett for the spectro sensor included (1) indoor free-floating tests in a 

small tank using two test oils, (2) outdoor free-floating tests in the main tank and subjected to multiple 

wave conditions using one test oil. The experimental results are discussed in detail in the Chapter 7. In 

general, the capacitive sensor showed very good accuracy during the indoor testing, where the average 

absolute error ranged from less than 1mm to around 5mm in the worst case dynamic-scenarios. Also, 

the sensor showed a good accuracy during the bridge-mounted dragging tests where the average 

absolute error was around 6mm. During the free-floating tests with waves, the capacitive sensor showed 

an acceptable accuracy in most of the cases where the absolute average error was less than 10mm. As 

expected, it was observed that the capacitive sensor accuracy dropped while testing against aggressive 

waves (Harbor chop), and heavy oils types, due to the significant oil-fouling. Based on the experimental 

results, several improvements for the sensor design are suggested at the end of this report, to enhance 

the sensor performance during dynamic-liquid conditions, and to further decrease the oil-fouling effect 

on the sensor accuracy.

For the spectro sensor, a very good accuracy was obtained during the indoor tests, especially while 

dealing with transparent oil sheens such as diesel, with an average absolute error of less than 400μm. 

During the free-floating tests, despite the high standard deviation values of the spectro measurements 

caused by waves, the sensor overall accuracy was also acceptable, with an average absolute error of 

around 500μm. However, it was observed that the sensing range of the spectro sensor was decreased to 

less than 1mm when dealing with opaque oil types such as HOOPS (fresh). Also, oil-fouling of the 

sensor lenses impacted the accuracy of the spectro device, especially at very thin oil thicknesses. It is 

important to note that the spectro experiments encountered a main limitation in validating the groundtruth thickness, since it was hard to obtain uniformly-distributed oil sheens at thin oil thicknesses (below 

500μm). Based on the experimental results, and aiming to enhance the performance of the spectro sensor 

device, several suggestions are included at the end of the report for future development.
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Table of Contents

ACRONYMS.......................................................................................................................................16

CHAPTER 1- INTRODUCTION................................................................................................17

1.1 Background..........................................................................................................................17

1.2 Related Work.......................................................................................................................17

Remote Sensing Techniques ...........................................................................................17

Contact-based Methods...................................................................................................18

1.3 Selected Sensors Description...............................................................................................19

Capacitive Array Sensor..................................................................................................19

Spectro-based Sensor ......................................................................................................19

CHAPTER 2- LED-BASED SENSOR DESIGN AND INITIAL TESTING...........................20

2.1 Sensor Design......................................................................................................................20

2.2 Sensor Components.............................................................................................................20

Light-to-Frequency Converter (TSL230BR)...................................................................20

GPS Module – SkyNav SKM53......................................................................................22

Processing Unit – Arduino Nano ....................................................................................23

Wireless Transceiver – TB394 2.4 GHz .........................................................................23

Wireless Receiver............................................................................................................24

Light Emitter Module......................................................................................................24

2.3 Circuit Design......................................................................................................................24

2.4 Hardware Implementation ...................................................................................................25

2.5 Initial Experiments ..............................................................................................................26

LED-based Sensor Initial Experiment – Light Oil..........................................................26

LED-based Sensor Initial Experiment – Heavy Oil ........................................................27

2.6 LED-based Sensor Mechanical Design ...............................................................................28

CHAPTER 3- CAPACITIVE SENSOR DESIGN AND INITIAL TESTING ........................31

3.1 Sensor Design......................................................................................................................31

3.2 Capacitive Sensor Initial Implementation ...........................................................................32

Capacitive Sensing Unit..................................................................................................32

Capacitive Processing Unit .............................................................................................33

Capacitive Touch Controllers (MPR121)........................................................................34

Initial Capacitive Sensor Prototype.................................................................................36

3.3 Capacitive Sensor Initial Mechanical Design......................................................................38

3.4 Capacitive Sensor Initial Testing.........................................................................................42

Oil Types Used in Initial Testing Experiments...............................................................42

Preliminary Capacitive Sensor Experiments – Light Oil ................................................43

Preliminary Capacitive Sensor Experiments – Heavy Oil ..............................................46

Preliminary Capacitive Sensor Experiments - Environmental Conditions .....................49

3.5 Preliminary Algorithms for Estimating Oil Thickness........................................................49

Non-supervised Clustering Algorithm (K-Means)..........................................................50

Thickness Estimation Based on Interfaces Detection .....................................................51

CHAPTER 4- EXPERIMENTAL SETUPS ...............................................................................53
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4.1 LED-based Sensor Testbed .................................................................................................53

4.2 Capacitive Sensor Indoors Lab Testbed ..............................................................................55

4.3 Capacitive Sensor Outdoors Lab Testbed............................................................................57

CHAPTER 5- LED-BASED SENSOR DESIGN EVALUATION AND REFINEMENT ......61

5.1 Experiments for Tuning Distance........................................................................................61

Distance Tuning Experiment 1 (5.8/ 6.5/ 7.4 cm - sensitivity (x100) - scaling (100)) ...61

Distance Tuning Experiment 2 (7.4/8.5 cm - sensitivity (x1) - scaling (100).................63

Distance Tuning Experiment 3 (6.5/10 cm - sensitivity (x10) - scaling (10)..................64

Diesel Experiment 4 (10 cm - sensitivity (x10) - scaling (10)).......................................66

5.2 Oil-Thickness Calibration and Measurement ......................................................................67

5.3 Bouy Design ........................................................................................................................68

5.4 Bouy Experiments...............................................................................................................69

CHAPTER 6- CAPACITIVE SENSOR DESIGN EVALUATION AND REFINEMENT....71

6.1 Performance Testing............................................................................................................71

6.2 Evaluating the Impact of Different Power Sources on the Sensor Performance .................73

6.3 Sensor Design Refinement ..................................................................................................74

6.4 Optimized Sensor Prototype................................................................................................76

6.5 Capacitive Sensor Tuning Experiments...............................................................................78

Current Tuning Experiments...........................................................................................78

Time Tuning Experiments...............................................................................................80

Combined Tuning Experiments ......................................................................................82

6.6 Capacitive Sensor Enhanced Multi-Layer Design with Pins...............................................84

6.7 Additional Attempts to Reduce the Oil-fouling Effect........................................................88

Adding High-Frequency Vibration Mechanism to the Sensor........................................88

Preliminary Work Done on Coating with Nanoprotech Material ...................................90

Preliminary Work Done on Coating with Ultra-ever dry Material .................................93

6.8 Battery Testing ....................................................................................................................98

6.9 Wireless Transmission Testing..........................................................................................100

6.10 Enhanced Algorithms Based on Sensor Movement Tracking...........................................101

Movement Tracking in Dynamic Liquid Conditions................................................101

Overall Thickness Measurement Algorithm.............................................................101

6.11 Mechanical Design Refinement and Optimization............................................................108

Packaging Design .....................................................................................................108

Attachments for Final Testing ..................................................................................111

CHAPTER 7- OHMSETT TESTING.......................................................................................117

7.1 Dipping Tests (sensor #1 - Capacitive Sensor)..................................................................117

Dipping Tests (1-8) - Diesel..........................................................................................119

Dipping Tests (9-16) – Hoops (weathered)...................................................................121

Dipping Tests (18-25) – Hydrocal 300..........................................................................125

Dipping Tests (26-33) – Calsol 8240 ............................................................................126

7.2 Outdoor Bridge Mounted Tests (sensor #1 - Capacitive Sensor)......................................128

Dynamic Tests (34, 60) – Hydrocal 300 .......................................................................131

Dynamic Tests (73, 89) – Hoops (weathered)...............................................................136

7.3 Skimmer Mounted Tests in Waves (sensor #1 - Capacitive Sensor).................................136
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Skimmer-mounted Tests - Test No. 90 .........................................................................137

Skimmer-mounted Tests - Test No. 91 .........................................................................138

Skimmer-mounted Tests - Test No. 92 .........................................................................139

7.4 Slick Thickness Testing (sensor #2 – Spectro Sensor)......................................................141

Spectro Sensor Tank Test - Diesel ................................................................................141

Spectro Sensor Tank Test – Hoops (fresh)....................................................................142

LED-based Waves Test - Diesel ...................................................................................144

CHAPTER 8- CONCLUSION, RECOMMENDATIONS AND FUTURE DEVELOPMENT

147

REFERENCES..................................................................................................................................149

APPENDIX........................................................................................................................................151
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List of Figures

Figure 1: Capacitive Oil Thickness Sensor ................................................................................... 2

Figure 2: LED-based sensor.......................................................................................................... 3

Figure 3: LED-based Sensor - Block Diagram............................................................................ 20

Figure 4: TSL 230BR Package (Top View)................................................................................. 21

Figure 5: Light-to-Frequency Click (Mikroelektronika)............................................................. 21

Figure 6: Connection Schematic (Light-to-Frequency Click - Mikroelektronika)...................... 22

Figure 7: SKM53 GPS Module ................................................................................................... 22

Figure 8: Arduino Nano Board.................................................................................................... 23

Figure 9: Wireless Transceiver Module (RF).............................................................................. 23

Figure 10: Wireless-to-USB receiver module ............................................................................. 24

Figure 11: LED-based spectrometer control circuit schematic ................................................... 25

Figure 12: LED-based spectrometer PCB................................................................................... 25

Figure 13: LED-based Sensor Sensing Section (Side View)....................................................... 26

Figure 14: Output Frequency vs. Oil Thickness Plot (LED-based Light Oil Experiment) ......... 27

Figure 15: Output Frequency vs. Oil Thickness (LED-based Sensor - Heavy Oil Experiment). 28

Figure 16: a) Light shield and cap, b) Light shield and floater, c) LED light channel and electronics 

holder.................................................................................................................................... 29

Figure 17: a) Electronics case, b) Light shield, c) Floater, d) Cable channel.............................. 29

Figure 18: Maximum shading iteration (isometric view and front section plane view).............. 30

Figure 19: Capacitive Sensor Design .......................................................................................... 31

Figure 20: Illustration of vertical capacitive array at different positions .................................... 32

Figure 21: Initial sensing unit prototypes.................................................................................... 33

Figure 22: Initial Capacitive Sensor Design................................................................................ 33

Figure 23: Capacitive Processing Unit - Block Diagram ............................................................ 34

Figure 24: MPR121 Breakout Board........................................................................................... 34

Figure 25: MPR121 Voltage Measurement Cycle....................................................................... 35

Figure 26: Capacitive Sensor - Control Circuit Schematic ......................................................... 37
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Figure 27: Capacitive Sensor PCBs ............................................................................................ 38

Figure 28: Capacitive Sensor Packaging - Prototype 1............................................................... 39

Figure 29: Capacitive Sensor Packaging - Iteration 1................................................................. 39

Figure 30: Capacitive Sensor Alpha Iteration (adopted for Prototype 2 machining) .................. 40

Figure 31: a) Capacitive card, b) Control board, c) Spring reinforced hasp lock........................ 40

Figure 32: Alpha package - Bottom view.................................................................................... 41

Figure 33: Alpha package and extension front view ................................................................... 41

Figure 34: Alpha package and extension right view ................................................................... 41

Figure 35: Experiment 1: Sensor in Air....................................................................................... 43

Figure 36: Experiment 2 - Light Oil / Water ............................................................................... 44

Figure 37: Experiment 3 (Air/Light Oil)..................................................................................... 46

Figure 38: Fuel-oil experiment 1 – Locating oil/water interface................................................. 47

Figure 39: Capacitive Sensor - Initial Experiment (Fuel Oil) - Experimental Setup .................. 48

Figure 40: Fuel oil experiment 2 – Locating air/oil interface...................................................... 49

Figure 41: C vs. ΔCt.................................................................................................................... 50

Figure 42: K-means algorithm results......................................................................................... 50

Figure 43: Interfaces detection algorithm – Ratios ..................................................................... 51

Figure 44: Exploded view of assembly fit for the spectrometer case.......................................... 53

Figure 45: Experimental Spectrometer package assembly (Left) and exploded view (right) ..... 54

Figure 46: Sensor/battery box (left: lid off, right: lid on)............................................................ 54

Figure 47: complete setup (left: lid off, right: lid on).................................................................. 55

Figure 48: Capacitive Sensor Experimental Testbed - Lab tank ................................................. 55

Figure 49: overall Simulink block diagram................................................................................. 56

Figure 50: Motor performance .................................................................................................... 57

Figure 51: Large tank Galvanized steel SolidWorks design........................................................ 58

Figure 52: Abyzz 200 controllable pump .................................................................................... 58

Figure 53: Pump specifications................................................................................................... 58
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Figure 54: Wave tank................................................................................................................... 59

Figure 55: Linear actuator........................................................................................................... 59

Figure 56: Control panel.............................................................................................................. 60

Figure 57: 3D printed extension installed on rail ........................................................................ 60

Figure 58: Photo of Capacitive sensor mounted on the large tank actuator while dragging ....... 60

Figure 59: Experimental setup for LED-based sensor tuning experiments................................. 61

Figure 60: LED-based Sensor Tuning – Distance: 5.8 cm, Sensitivity: 100, Scaling: 100......... 62

Figure 61: LED-based Sensor Tuning – Distance: 6.5 cm, Sensitivity: 100, Scaling: 100......... 62

Figure 62: LED-based Sensor Tuning – Distance: 7.4 cm, Sensitivity: 100, Scaling: 100......... 62

Figure 63: LED-based Sensor Tuning – Experiment 2 (Graph).................................................. 63

Figure 64: Distance tuning experiment 2- Comparison (graph).................................................. 64

Figure 65: LED-based Sensor Tuning – Experiment 3 (Graph).................................................. 65

Figure 66: Distance Tuning Experiment 3 - Comparison (Graph).............................................. 66

Figure 67: LED-based Sensor Tuning – Experiment 4 (Graph).................................................. 67

Figure 68: Spectro-based sensor - Implemented Buoy................................................................ 68

Figure 69: LED-based Buoy - Experimental Setup..................................................................... 69

Figure 70: Buoy Experiment - Frequency response vs. Oil Thickness (Graph).......................... 69

Figure 71: Sensor prototypes for repeatability testing ................................................................ 71

Figure 72: Capacitive Sensor Performance Experiment - Oil Case ............................................ 72

Figure 73: Voltages measured by E1 in Battery-powered and USB-powered trials.................... 74

Figure 74: Capacitance Measurement Schematic - Final Design................................................ 75

Figure 75: Processing and Communication Unit......................................................................... 75

Figure 76: Optimized Sensor Prototype ...................................................................................... 77

Figure 77: Experimental Installation - Tuning Experiments....................................................... 78

Figure 78: Current tuning – Graph (Voltage(ADC) vs. Current(μA))......................................... 80

Figure 79: Time tuning - Graph (Voltage (ADC) vs. Time(0.5-32 μs)) ...................................... 82

Figure 80: Combined Tuning Graph (delta vs time and current) ................................................ 84
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Figure 81: Photo showing oil-fouled electrodes.......................................................................... 85

Figure 82: Enhanced sensor design with pins ............................................................................. 86

Figure 83: Enhanced sensor design with pins ............................................................................. 86

Figure 84: Pins Experiment (Voltage(ADC) vs. Time (sec))....................................................... 87

Figure 85: Photo showing pins penetrating the oil-fouling layer................................................ 87

Figure 86: Capacitive sensor with vibration mechanism ............................................................ 88

Figure 87: E1 voltage-drop, before & after installing the vibrator (Voltage (ADC) vs. Time (sec))

.............................................................................................................................................. 89

Figure 88: E2 voltage drop before & after installing the vibrator (Voltage (ADC) vs. Time (sec)90

Figure 89: Nanoprotech – Liquid Electrical Insulation............................................................... 91

Figure 90: Nanoprotech Coating - Assessment Experiment (Voltage (ADC) vs. Time (sec)...... 91

Figure 91: Voltages (ADC) measured before and after coating with Ultra-ever dry (Air) - Graph94

Figure 92: Comparison between relative differences in Air/Oil/Water cases before and after coating

.............................................................................................................................................. 97

Figure 93: 9V Battery Pack - AA Batteries................................................................................. 99

Figure 94: Voltage vs. Time (Graph) - Battery Experiment ...................................................... 100

Figure 95: AA Rechargeable Batteries (2500 mAh).................................................................. 100

Figure 96: Relative differences converted to grey scale image ................................................. 101

Figure 97: MatLab GUI showing detected interfaces ............................................................... 102

Figure 98: Algorithm flowchart................................................................................................. 107

Figure 99: 7.2V 3800mAh Ni-MH (left), 2500mAh 1.2V 'AA' Ni-MH (right)........................ 108

Figure 100: Batteries flat side to side package.......................................................................... 108

Figure 101: package for batteries on one side and sensor on the other..................................... 109

Figure 102: Package of sensor powered by 'AA' Ni-MH.......................................................... 109

Figure 103: Comparison of final packaging designs for capacitive sensor............................... 110

Figure 104: Final package with printed modular extension mount and instrumentation .......... 110

Figure 105: Final capacitive sensor prototype with packaging and instrumentation ................ 111

Figure 106: Tow point mount drawing (dimensions in inches)................................................. 112
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Figure 107: Spring lock attachment computer-aided design ..................................................... 112

Figure 108: Retracted attachment (right), Released attachment (left) computer aided design.. 113

Figure 109: Manufactured hinged mount.................................................................................. 113

Figure 110: Dimensions after manufacturing............................................................................ 114

Figure 111: Metal beam mount computer-aided design ............................................................ 115

Figure 112: Aluminum extrusion mount computer-aided design .............................................. 115

Figure 113: Manufactured skimmer mount sensor holder......................................................... 116

Figure 114: Manufactured skimmer mount right angle slider................................................... 116

Figure 115: Manufactured skimmer mount horizontal fixture .................................................. 116

Figure 116: Experimental setup for dipping tests (Ohmsett)..................................................... 118

Figure 117: Adding controlled amount of oil - Graduated Cylinders........................................ 119

Figure 118: Dipping Tests (1-8) / Diesel (Static & Dynamic Curves) ...................................... 121

Figure 119: Coating of the sensor body under slick - Hoops (weathered)................................ 122

Figure 120: Dipping Tests (9-16) / Hoops (weathered) (Static & Dynamic Curves)................ 123

Figure 121: Test 9 (Static) – Hoops (weathered) (3.175mm).................................................... 124

Figure 122: Dipping Tests (17-25) / Hydrocal (Static & Dynamic Curves).............................. 126

Figure 123: Dipping Tests (26-33) / Calsol (Static & Dynamic Curves) .................................. 128

Figure 124: Capacitive Sensor – Bridge-mounted Experimental setup .................................... 129

Figure 125: Contained oil in the channel - Wind Effect............................................................ 130

Figure 126: Bridge-mounted Sensor while dragging (Snapshot from video# 00131)............... 131

Figure 127: Photo showing boom bellying outward ................................................................. 131

Figure 128: Tests 34 - 40 / Thickness: 6,35mm / Speed: 0.5-1-2 knots .................................... 133

Figure 129: Tests 41 - 46 / Thickness: 12.7mm / Speed: 0.5-1-2 knots .................................... 133

Figure 130: Tests 47 - 52 / Thickness 25.4mm / Speed: 0.5-1-2 knots ..................................... 134

Figure 131: Tests 53 - 60 / Thickness 50.08mm / Speed: 0.5-1-2 knots ................................... 134

Figure 132: Dynamic Tests (34-35) – Hydrocal........................................................................ 135

Figure 133: Sensor #1 Mounted to Desmi Termite Skimmer.................................................... 136
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Figure 134: Capacitive Sensor Waves Test (90) – Hydrocal 300.............................................. 138

Figure 135: Capacitive Sensor Waves Test (91) – Hydrocal 300.............................................. 139

Figure 136: Capacitive Sensor Waves Test (92) - Hydrocal 300............................................... 140

Figure 137: Spectro-based Sensor - Diesel / Tank Experiment (graph) .................................... 142

Figure 138: LED-based Sensor - Diesel Test (Tank)................................................................. 142

Figure 139: LED-based Sensor – Hoops (fresh) / Tank Experiment (Graph) ........................... 143

Figure 140: Lens fouling – Hoops (fresh)................................................................................. 144

Figure 141: Non-uniform Distribution (Hoops)........................................................................ 144

Figure 142: Spectro-based Sensor - Test 92 - Waves (1, 2)....................................................... 144

Figure 143: Spectro-based Sensor - Test 92 - Average graph.................................................... 145

Figure 144: Experimental area - Test 92 ................................................................................... 146

Figure 145: Capacitive sensor package iteration 1.................................................................... 151

Figure 146: Alpha capacitive sensor package (assembly)......................................................... 151

Figure 147: Alpha capacitive sensor package (controller board compartment) ........................ 152

Figure 148: Alpha capacitive sensor package (sensor card lip seal) ......................................... 152

Figure 149: LED based sensor top cap...................................................................................... 153

Figure 150: Spectro sensor holder............................................................................................. 153

Figure 151: LED case................................................................................................................ 154

Figure 152: LED case cap ......................................................................................................... 154

Figure 153: Outdoors lab tank................................................................................................... 155
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List of Tables

Table 1: Sensitivity & Frequency Scaling for TSL230BR device............................................... 21

Table 2: GPS SKM 53 – Technical Specifications...................................................................... 22

Table 3: Arduino Nano Module – Technical Specifications........................................................ 23

Table 4: Wireless Transceiver Module – Technical Specifications............................................. 24

Table 5: LED-based Sensor - Light Oil - Experiment Results.................................................... 27

Table 6: LED-based Sensor - Heavy Oil - Experiment Results .................................................. 28

Table 7: Measured Viscosity (Fuel Oil)....................................................................................... 42

Table 8: Viscosity of light and medium oil samples.................................................................... 43

Table 9: Experiment 1 (Air) – Numerical Values........................................................................ 43

Table 10: Experiment 2 (Light Oil/Water) – ADC Voltage Values.............................................. 44

Table 11: Experiment 3 (Air/Oil) – ADC Voltage Values............................................................ 45

Table 12: Fuel oil experiment 1- ADC Voltage Values ............................................................... 46

Table 13: Fuel oil experiment 2- ADC Voltage Values................................................................ 48

Table 14: Static Experimental Results (Case 1) .......................................................................... 52

Table 15: Static Experimental Results (Case 2) .......................................................................... 52

Table 16: LED-based - Experimental Results - Distance tuning experiment 2........................... 63

Table 17: Distance Tuning Experiment 2 - Comparison ............................................................. 64

Table 18: LED-based - Experimental Results - Distance tuning experiment 3........................... 65

Table 19: Distance Tuning Experiment 3 - Comparison ............................................................. 66

Table 20: LED-based - Experimental Results - Distance tuning experiment 4........................... 67

Table 21: Results of thickness measurement experiment - LED-based sensor ........................... 68

Table 22: Bouy Experiment - Frequency response vs. Oil Thickness......................................... 69

Table 23: Floating Buoy Exp. 1 (500 μm)................................................................................... 70

Table 24: Floating Buoy Exp. 2 (1000μm).................................................................................. 70

Table 25: Voltage (ADC) - Battery vs. USB-port Power Sources............................................... 73

Table 26: Current tuning experiment (Voltage (ADC) vs. Current(1-62μA))............................. 79
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Table 27: Hex values representing different charge time configurations.................................... 80

Table 28: Time tuning experiment (Voltage(ADC) vs. Time (0.5-32 μs)) .................................. 81

Table 29: Combined tuning - Numerical results.......................................................................... 82

Table 30: Vibrator Experiment (Voltage (ADC) vs. Time (sec))................................................. 89

Table 31: Voltages in air & water, before and after coating with Nanoprotech insulation material92

Table 32: Relative differences (%) in Oil case, before and after coating with Nanoprotech insulation

.............................................................................................................................................. 92

Table 33: Relative differences (%) and Voltages (ADC), before and after coating with Ultra-ever dry

.............................................................................................................................................. 93

Table 34: Non-coated - Voltage (ADC) & R.D (%). - Partially immersed in oil/water mixture . 95

Table 35: Coated (Ultra-ever dry) - Voltage (ADC) & R.D (%). - Partially immersed in oil/water96

Table 36: Comparison between relative differences in Air/Oil/Water cases before and after coating

.............................................................................................................................................. 97

Table 37: Voltage vs. Time - Battery Experiment........................................................................ 99

Table 38: Statistical analysis of thickness measured using relative algorithm (static).............. 102

Table 39: Pre-Test Lab Analysis (Ohmsett)............................................................................... 117

Table 40: Dipping tests - Target thicknesses and Volumes........................................................ 119

Table 41: Capacitive Sensor Dipping Tests (1-8) – Diesel / Static............................................ 120

Table 42: Capacitive Sensor Dipping Tests (1-8) – Diesel / Dynamic ...................................... 120

Table 43: Capacitive Sensor Dipping Tests (9-16) – Hoops (weathered) / Static ..................... 122

Table 44: Capacitive Sensor Dipping Tests (9-16) – Hoops (weathered) / Dynamic................ 123

Table 45: Capacitive Sensor Dipping Tests (17-25) – Hydrocal 300 / Static ............................ 125

Table 46: Capacitive Sensor Dipping Tests (17-25) – Hydrocal 300 / Dynamic ...................... 125

Table 47: Capacitive Sensor Dipping Tests (26-33) – Calsol 8240 / Static............................... 126

Table 48: Capacitive Sensor Dipping Tests (26-33) – Calsol 8240 / Dynamic ......................... 127

Table 49: Ambient weather conditions - Tests (34-60).............................................................. 132

Table 50: Capacitive Sensor Dynamic Tests (34-60) – Hydrocal 300....................................... 134

Table 51: Ambient weather conditions - Tests (90-92).............................................................. 137

Table 52: Skimmer-mounted tests - Wave conditions............................................................... 137
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Table 53: Results for Skimmer-mounted (Capacitive - Test 90) ............................................... 138

Table 54: Results for Skimmer-mounted (Capacitive - Test 91) ............................................... 139

Table 55: Results for Skimmer-mounted (Capacitive - Test 92) ............................................... 140

Table 56: LED-based Sensor - Diesel / Tank Experiment (Measured Thicknesses)................. 141

Table 57: LED-based Sensor - Hoops / Tank Experiment (Measured Thicknesses)................. 143

Table 58: Spectro-based Sensor - Diesel / Waves Experiment.................................................. 145
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Acronyms

BSEE Bureau of Safety and Environmental Enforcement

RRB Response Research Brand

DOI Department of Interior

Ohmsett Oil Spill Response Research and Renewable Energy Test Facility

AUB American University of Beirut 

GPS Global Positioning System

LED Light-emitting diode

RF Radio Frequency 

DC Direct Current

USB Universal Serial Bus

TTL Transistor-Transistor Logic

UART Universal Asynchronous Receiver-Transmitter

I2C Inter-Integrated Circuit

GUI Graphical User Interface 

A/D Analog to Digital

PCB Printed Circuit Board
Page 17

Chapter 1- Introduction

This report presents the design, prototyping, and testing of two low-cost floating oil thickness 

measurement instruments, that provide near real-time measurements for cleanup operations during oil 

spill incidents. The first sensor is designed for skimming operations, while the second is more suited 

for oil spill tracking, and oil volume estimation. The first sensor is designed to be installed on a skimmer 

(ex. TERMITE skimmer [28]), a boom or a floating buoy to help direct cleaning operation; the second 

sensor is designed to be mounted on a free-floating buoy. The presented experimental work and testing 

demonstrates the accuracy and repeatability of the proposed instruments under different environmental 

conditions.

1.1 Background

Sea routes are used for the transportation of oil across different countries all over the world, using large 

oil tankers. Given the high reliance of the world on oil and gas, and the associated high volume of traffic 

at sea, it is not uncommon to witness accidental oil spills[1]. Also, oil spills occur due to the release of 

different oil types (crude/refined) from offshore platforms, oil wells, and pipelines. These accidents 

have long-term hazardous effects on the environment, especially on living organisms such as birds, 

mammals, and fish.

Studies have revealed that in an oil spill, roughly ninety percent of the oil volume is located in ten 

percent of the oil spill area[2]. In addition, the appearance and thickness of slick changes with respect 

to time and weather conditions. Although remediation options are available during a response to an oil 

spill, one of the key features impacting the effectiveness of the cleaning techniques is knowing the 

actual thickness of oil in a slick area. For instance, when at least 1 mm of oil thickness is detected, insitu burning is an option as a method to remove it. For denser and more emulsified oils, burning is 

possible only at thickness ranging from three to ten millimeters. Skimmers are widely used in oil spill 

response to recover floating oil; unlike other chemical based techniques such as dispersants, skimmers 

perform physical oil/water separation by using oil-attracting and gravity-based techniques. In addition 

to the sea state, presence of ice, and other environmental conditions, oil spill thickness is one of the 

most important factors that affects the effectiveness of skimmers[3].

To conclude, oil thickness is a critical parameter that influences the effectiveness of several oil 

remediation techniques, and its knowledge is essential in directing the cleanup platforms. The sensors 

we are presenting here, contribute to enhancing the efficiency of the currently used cleanup processes, 

by providing oil situational awareness, to guide cleanup crews towards the needed areas.

1.2 Related Work

Techniques used to estimate oil thickness or detect its presence can be based on either remote 

measurements or contact-based measurement. In what follows, we will discuss both approaches.

Remote Sensing Techniques

Remote sensing techniques, such as visual imaging and visual observation, are widely used for assessing 

oil spills. By utilizing airborne vehicles, visual observation—where an expert provides an estimate of 

the slick thickness based on color observations—is one of the simplest methods used to locate and

estimate oil spill. However, due to haze and light reflection from the sea, in some cases visual inspection

may be affected [4]. Furthermore, the success of visual imaging techniques is affected by the sea 
Page 18

conditions, as well the temperature conditions. For instance, it can be difficult to see less buoyant oil 

types while being swamped by waves or collecting into unpredictable shapes during rough sea surface 

conditions or in cold water. In the case of reflecting sunlight, ocean water may appear very bright and 

it may be difficult, using traditional imaging techniques, to detect thin oil films floating on the water

surface.

Hyperspectral imaging from airborne vehicles [5], is another type of remote sensing technique that is

widely used to detect and monitor oil spills, where experiments revealed a high correlation between oilspill spectral reflectance values and oil-spill thickness measurements. The main limitation in 

hyperspectral imaging was in applying it from airborne or satellite platforms, because oil spectral 

measurements are highly affected by cloud coverage, lighting conditions, and sea state.

Microwave passive imaging is another technique for measuring oil thickness from airborne or satellite 

platforms and is somewhat unaffected by cloud coverage and adverse weather conditions. For instance,

in the work of Calla et al.[6], oil spill locations were detected through temperature measurements, 

collected from a Special Sensor Microwave Imager (SSM/I), and an Advanced Microwave Scanning 

Radiometer for EOS (AMSR-E) satellite carrying passive microwave sensors. The analysis of the 

obtained satellite images revealed that the temperature of the oil spill area was greater than that of the 

surrounding unpolluted areas. Therefore, real-time monitoring for a sudden or abnormal shift in 

temperature within the same area may be used for detecting an oil spill accident. However, oil thickness 

estimation based on temperature variations could not be easily performed because the oil temperature 

is affected by several factors other than its thickness, such as its dielectric constant[7]. 

To summarize, although airborne and space borne remote sensing techniques are helpful in providing a 

relatively global assessment of the oil thickness, they are affected by lighting and/or atmospheric 

conditions, and they are relatively expensive compared to contact-based sensing techniques.

Contact-based Methods

Contact-based oil-thickness measurement methods refer to the more traditional instrumentation 

approaches, such as conductivity, capacitance, light arrays, electromagnetic, and vision[8] [9]. For 

example, in the work of Denkilkian et al. [8] conductivity and LED arrays were used in implementing 

a wireless oil-thickness measurement sensor; it relied on a blue LED-array to detect the variation in 

received blue light intensity as it propagates through oil or water. Also, a metallic array was used to 

detect the oil thickness based on electric conductivity; such techniques rely on the fact that different 

aqueous solutions feature different conductive properties. The main factor that affects the water 

conductivity is the concentration of dissolved salts and other chemicals in it. In contrast to seawater, 

which is highly conductive because of its sodium ions, oil possesses low electric conductivity. Variation 

in the received voltage by the conductive plates was used in estimating oil thickness. Although the 

implemented prototype provided acceptable results, it suffered from problems of corrosion, as well as 

a low resolution in the order of several centimeters.

Capacitive sensing is widely used for liquid level detection[10] [11]. The continuous capacitive 

measurement techniques are used mainly in measuring liquid levels in storage tanks, where accurate 

calibration is needed before operation [12]. Unlike the conductivity-based sensors, the capacitive 

sensing approach does not require direct contact between the sensor conductive plates and the examined 

liquid (Oil/Water). As a consequence, by avoiding direct contact electrical corrosion is also avoided. In 
Page 19

comparison to other contact-based oil thickness estimation techniques, capacitive-based sensing

promises to be more reliable, and has a longer operating lifetime.

1.3 Selected Sensors Description

In this project, two sensors (Capacitive and Spectro) were implemented, targeting different resolutions 

and applications. The sensing concept used in each sensor is described briefly in the sections below. 

The other chapters of this report describe the detailed implementation and testing of each of the two 

sensors.

Capacitive Array Sensor

The capacitive liquid-level measurement technique is used to monitor, with high resolution, rapid 

changes in liquid amounts. Traditionally, this technique is used to detect fluid variations, by using 

capacitive sensors composed of a couple of conductive plates. The conductive plates are separated by a 

constant distance of dielectric material. The capacitance value measured by the sensor without the liquid 

is called the Net Capacitance `Cn’. After adding liquid into the container, the capacitance changes 

depending on the liquid amount and type, as C = Ԑr x Cn, where Ԑr is the relative permittivity of the 

liquid. The capacitive sensor plates could be shaped in different designs, including planar plates, 

cylindrical probes, and wires.

The capacitive sensor proposed here offers a novel design, measuring oil thickness based on a 

geometrical array of conductive electrodes mounted on a low-cost PCB board. This sensing 

methodology, which we refer to as geometrical capacitive sensing, relies on discrete values reported by 

an array of adjacent strips. This is in contrast to absolute measurements used in traditional capacitive 

liquid-level sensing applications. Oil thickness calculation is then estimated based on the geometrical 

dimensions of the sensor after detecting the number of electrodes immersed in oil. The change in 

capacitance at each electrode, caused by the change in the dielectric constant of the surrounding 

material, is measured independently in order to detect the type of environment (air/oil/water) it is in. 

The electric field formed by the electrodes is affected by the different types of environments

surrounding the sensor, thus permitting the differentiation between them based on the change in the 

corresponding dielectric constant.

Spectro-based Sensor

The Spectro-based sensor is based on the phenomenon of light absorption to determine oil thickness. 

The sensor is designed for oil thickness measurements at relatively high resolutions. Oil thickness is 

estimated by analyzing the intensity of absorbed light passing through the examined liquid. Previous

work done on the spectral analysis in[5] revealed that the correlation between oil thickness and spectral 

reflectance was highly affected by external lightening conditions. To address this problem, the LEDbased sensor is packaged inside an enclosure to isolate the examined liquid from daylight. Our proposed 

design is inspired by that of that of Yeh and Tseng[13].
Page 20

Chapter 2- LED-based Sensor Design and Initial Testing

2.1 Sensor Design

The LED-based sensor is composed of three main units: Processing and Communication Unit (PCU), 

Light Source Unit (LSU), and the Light Receiver Unit (LRU). The PCU calculates the oil thickness by 

analyzing the intensity of the received light signal. It acquires the location from a GPS module and 

sends the data to the base station through a wireless transceiver module. The overall block diagram 

describing the main design of the sensor is shown in Figure 3.

Figure 3: LED-based Sensor - Block Diagram

2.2 Sensor Components

Light-to-Frequency Converter (TSL230BR)

The main component of the LRU is the “TSL230BR-LF” (Figure 4) module provided by Texas 

Advanced Optoelectronics Solutions (TAOS) [14]. This module performs conversion of light intensity 

to frequency, at high-resolution. It was chosen due to its change its sensitivity programmatically, its 

low-power consumption, and its ability to communicate directly to a microcontroller. The sensitivity of 

the photodiode can be selected as one of three options (High, Medium, and Low) and the output 

frequency can be scaled to one of four values. The device responds over the light range of 320nm to 

1050nm. 

Figure 4 shows the TSL230BR package and its pinout description. The recommended operating 

conditions for this device include a supply voltage of 5V and a free-air temperature range from -25oC 

to +70oC. The three sensitivity modes (1x, 10x, and 100x) are selected using two logic inputs S0 and 

S1, allowing the optimization of the device response based on the intended application. The output 

frequency can be scaled by using two logic inputs (S2, S3), where the output is connected internally to 

a series of frequency dividers; divide-by 1, 2, 10, and 100 (Table 1).

Processing and Communication Unit

Microcontroller (Arduino Nano)

Wireless (RF) GPS (SKM 53)

Light Source Unit

(LED + Power 

Supply)

Light Receiver Unit

(Light Sensor +

Configuration unit)

Control

Commands

Output

Frequency
Page 21

Figure 4: TSL 230BR Package (Top View)

Table 1: Sensitivity & Frequency Scaling for TSL230BR device

S1 S0 Sensitivity S3 S2 Frequency Division Factor

L L Power down L L 1

L H 1x L H 2

H L 10x H L 10

H H 100x H H 100

Instead of building a custom circuit to interface the TSL230BR module with the microcontroller board, 

the additional board “Light-to-Frequency Click” provided by MikroElektronika[15] was used. The 

board has a 2x5 connector (2.54mm separated pins), which simplifies its connection with any PCB or 

test boards[16]. Figure 5 shows the Light-to-Frequency Click module and Figure 6 shows its 

connections schematic.

Figure 5: Light-to-Frequency Click (Mikroelektronika)

8 S3

7 S2

6 OUT

5 VDD

Frequency Scaling (S2, S3)

Output Frequency

Sensitivity Adjustment (S0, S1)

FO Enable (Active Low)

S0 1

S1 2

OE 3

GND 4
Page 22

Figure 6: Connection Schematic (Light-to-Frequency Click - Mikroelektronika)

GPS Module – SkyNav SKM53

The “SkyNav SKM53” GPS module (Figure 7) provided by Skylab [17], is based on the MediaTek 

3329 single-chip architecture, has an embedded antenna, and a 6-pin UART interface. This module is 

characterized by its high sensitivity (-165dBm), which enables GPS tracking in harsh environments. In 

comparison with other available GPS modules in terms of cost, power consumption, and sensitivity, the 

SKM53 was found to be preferable. Table 2, shows the main technical specifications of the SKM53 

GPS module.

Figure 7: SKM53 GPS Module

Table 2: GPS SKM 53 – Technical Specifications

Receiver Type L1 frequency band

Sensitivity Tracking: -165 dbM, Acquisition: -148dbM

Accuracy Position: 3.0m CEP50 without SA (Open Sky)

Acquisition Time Cold/Warm Start: 36s/33s, Hot Start: 1s, Re-Acquisition: < 1s

Dimensions 30mm x 20mm x 8.5mm

Weight 10g

VCC 5V +/-5%

Current 50 mA (typical)

Operating Temp. -40°C ~+85°C

Humidity < 95%
Page 23

Processing Unit – Arduino Nano

The Arduino Nano development board (Figure 8) is an open-source prototyping platform based on the 

ATmega328 microcontroller [18]. This module was selected due to its low-cost, low power 

consumption, and simple programmability. Main technical specifications of the Arduino Nano module 

are shown in Table 3.

Figure 8: Arduino Nano Board

Table 3: Arduino Nano Module – Technical Specifications

Microcontroller ATmega328

Operating Voltage 5V

Clock Speed 16MHz

DC Current (I/O pins) 40mA

Input Voltage (VIN pin) 7~12V

Power Consumption 19mA

PCB Size 18x45mm

Weight 7g

Analog I/O pins 8

Digital I/O pins 22

Wireless Transceiver – TB394 2.4 GHz 

The TB394 module shown in Figure 9 is a low-cost wireless transceiver module (RS232 TTL UART) 

for Arduino with an IPEX antenna [19]. It operates in two modes, the AT mode and the data 

transmission mode. The module is configured while working in the AT mode. A serial port is used to 

issue basic AT commands to set the module parameters (baud rate, frequency settings, ID number, 

factory settings, and version information). Table 4, shows the main technical specifications of the 

selected wireless transceiver modules.

Figure 9: Wireless Transceiver Module (RF)
Page 24

Table 4: Wireless Transceiver Module – Technical Specifications

Parameter Specification

Operating Voltage 3.3V~5V

RS232 Interface 3.3V/5V TTL level

Frequency Range 2402-2482 MHz

Transmit power 20dBm (100mW)

Receiver sensitivity -87dBm

Operating Temperature -40~+85 oC

Baud rate 9600 (Default)

Transmission distance 400m (Open Ground)

Size 32x13mm

Wireless Receiver

To receive the sensor measures wirelessly, a wireless-to-USB receiver module was implemented using 

the wireless RF transceiver TB394 2.4 GHz and a USB-to-TTL adapter. A 3D-printed package was 

implemented to fit the two modules connected together on the same PCB (Figure 10). The USB-TTL 

adapter is based on the CH340 chip, and supports a baud rate ranging from 50bps to 2Mbps.

Figure 10: Wireless-to-USB receiver module

Light Emitter Module

Based on the preliminary experimental work done on the spectrometer device, the blue light was 

selected as the light emitting module mainly because of the high correlation observed between bluelight absorption and the oil thickness. The basic 5mm blue LED with a 30mA max current consumption 

and 3.4VDC forward voltage drop was selected.

2.3 Circuit Design

The control circuit of the LED-based spectrometer (Figure 11), is powered by a linear power supply 

composed of a voltage regulator (LM2940) with two capacitors (C1=0.47uF & C2=22uF) for filter 

functions. The LM2940 regulator was chosen primarily due to its low voltage drop (0.5V) which is 

recommended for battery-powered applications. The GPS module (SKM53) is connected to the main 

controller board through its serial connection pins (RX, TX) and powered with the voltage regulator 

output (5V) through a transistor acting as a switch. Since the GPS module is connected to digital I/O 

Arduino pins, a software serial library is used to replicate the serial connection functionality on those 

pins. The wireless transceiver module is connected to the hardware serial pins of the microcontroller 

(TX, RX) and powered by the regulator output voltage (5V) through a transistor acting as a switch. The 

use of transistors (2N3940) is essential due to the relatively high current consumption of the modules 

which cannot be handled by the Arduino controller pins. Resistors are used to limit the current between 

the Arduino digital pins and the transistors base connector.
Page 25

2.4 Hardware Implementation

A double-sided PCB (Figure 12) is used to implement the LED-based sensor control circuit. The top 

layer of the PCB holds the microcontroller module, the GPS module, and the RF wireless transceiver. 

The bottom layer of the PCB holds the Light-to-Frequency converter module carrying the TSL230BR 

chip. The blue LED is placed in the bottom part of the buoy (directed upward) and centered with the 

photodiode position at the top. While the liquid mixture (Oil/Water) passes through the middle part of 

the sensed area, the received light is processed and converted to oil thickness based on the calibration 

functions.

Figure 11: LED-based spectrometer control circuit schematic

Figure 12: LED-based spectrometer PCB

GPS (SKM53)

Wireless (RF)

Arduino Nano

Light Sensor 

(TSL230BR)
Page 26

2.5 Initial Experiments

Given that the LED-based sensor is intended to measure very thin oil layers, light oil types are the major 

target for testing. Based on past experiments presented in the project proposal, where several LEDs with 

different colors were tested, the blue light was found to have the maximum correlation between the 

absorbed light and oil thickness. To assess the effectiveness of the method in measuring different lightoil thicknesses, a small experimental platform, shown in Figure 13, was implemented representing the 

inner part of the sensor buoy. The light source (blue LED) is mounted in the bottom part of the platform 

aligned with the receiver at the top of the container. Frequency measurements were taken by the lightreceiver module at different thicknesses of oil. To know the actual oil thickness, oil was added to the 

container with controlled amounts, by using a labeled micro-pipet. The container is closed from its sides 

by the black adhesive material. The black cover has one major opening in the bottom side to allow the 

blue light to pass, and other minor openings at the top layer kept letting some amount of light from the 

environment to enter to the container. This is needed because during operation, the sensing part is not 

isolated completely from the environment; instead, water and oil pass through the horizontal openings 

allowing some exterior light to enter and interfere with the measurements.

To handle the interference with the exterior light, several light measurements are taken while the LED 

is turned off. When activating the LED, the difference between the new measurement and the stored 

measurement is calculated to remove the effect of the environmental light. The frequency provided by 

the receiver is directly proportional to the light intensity. In other words, higher light intensities result 

in increased frequency of the generated square wave signal.

To control the sensitivity pins S0 and S1 are used. To decrease the noise effect, low sensitivity was set 

by connecting the S1 pin to GND and the S0 pin to VCC. The division factor was set to 100 by 

connecting S2 and S3 pins to VCC. To measure the output frequency, the output pin of the TSL230BR 

was connected to the Arduino second digital pin provided with an external interrupt. The frequency is 

calculated by counting the number of rising edges occurred at the pin for a certain interval of time (1 

sec).

Figure 13: LED-based Sensor Sensing Section (Side View)

LED-based Sensor Initial Experiment – Light Oil

In this experiment, a lubricating oil with a viscosity of 10 centistokes (Power-10W) was tested. The 

received light intensity was measured successively after adding controlled amounts of oil to the 

Sensing Area

Processing and Communication Unit

(Arduino, GPS, and Wireless modules)

Light Receiver Module

(TSL230BR)

Light Source 

(Blue LED)
Page 27

container measured by its volume. To get the corresponding oil film thickness, the volume (m3) of the 

added amount is divided by the area of the container (m2). The container dimensions are listed below:

 Width = 4.6cm

 Length = 8.9cm

 Height = 5.5cm

 Distance between LED and Oil: 0.3cm

 Distance between PCB and light Receiver module: 1.6mm

The statistical analysis of the experimental results of thirty-eight samples (N=38) are shown in Table 5, 

and the corresponding graph is shown in Figure 14.

Table 5: LED-based Sensor - Light Oil - Experiment Results

Figure 14: Output Frequency vs. Oil Thickness Plot (LED-based Light Oil Experiment)

As shown in Figure 14, the initial experiments on light oil show a linear relationship between the 

received frequency and oil thickness. As more oil is added to the solution, the received frequency 

decreases linearly since the received light intensity is decreased. The equation relating oil thickness to 

the received frequency is obtained by using linear regression analysis. For instance, for the 

experimented oil type with a viscosity of 10cSt, the calibration equation is calculated and represented 

by the following formula: Y = −5.7965X + 72.073; where Y is the output frequency and X is the oil 

thickness measured in mm.

LED-based Sensor Initial Experiment – Heavy Oil

Even though the heavy oil types are not the main target for the LED-based sensor, this experiment aims 

at monitoring the behavior of the sensor when dealing with heavy oil types. The same experimental 

procedure used in the light-oil experiment was repeated in this test. The light oil was replaced with a 

0

10

20

30

40

50

60

70

80

0.00 1.22 2.44 3.66 4.89 6.11 7.33 8.55 9.77

Frequncy (Hz/100)

Oil Thickness (mm)

Oil Thickness (mm) 0 1.22 2.44 3.66 4.88 6.1 7.32 8.54 9.77

Frequency

(Hz/100)

Mean 69 64 56 45 38 35 31 28 24

Standard Deviation 0.22 0.55 0.36 0.72 0.48 0.43 0.47 0.45 0.44

Maximum 69 65 56 46 38 36 31 28 24

Minimum 68 63 55 43 37 35 30 27 23
Page 28

heavy oil sample with a viscosity of 140cSt (Gear Oil). After adding controlled amounts of oil, the 

output frequency of the light receiver module was calculated and stored for each thickness (sample size 

(N) = 38). The experimental results are shown in Table 6 and the corresponding graph is shown in 

Figure 15.

Table 6: LED-based Sensor - Heavy Oil - Experiment Results

Oil Thickness (mm) 0 0.48 0.73 0.97 1.22

Frequency 

(Hz/100)

Mean 64.60 24.57 14.65 6.57 2.31

Standard Deviation 0.49 0.50 0.48 0.50 0.47

Maximum 65 25 15 7 3

Minimum 64 24 14 6 2

Figure 15: Output Frequency vs. Oil Thickness (LED-based Sensor - Heavy Oil Experiment)

The result of this experiment showed that the received light intensity is highly attenuated due to the use 

of heavy oil with a viscosity of 140cSt limiting the measurement range to around 1.2mm using the 

current sensitivity configuration. This result was expected due to the physical properties of heavy oils. 

In addition, it was observed that the relation between the oil thickness and output frequency is almost 

linear after 0.4mm of oil thickness.

2.6 LED-based Sensor Mechanical Design

As described before, the sensor must be able to measure thin oil film thicknesses on the surface of water 

based on the light absorption technique, therefore, any light coming from the environment would be 

undesirable. On the other hand, oil films with thicknesses in the micrometers to millimeters range are 

subject to breaking and separating into oil bubbles, the film thickness would be discontinuous, and 

bubbles will have different and non-uniform thicknesses along their spread. A package for the two-part 

sensor (emitter/receiver) is needed to keep water away from the electronics and the power source as 

well as to maintain a straight and fixed geometry of the opposing parts of the sensor, while considering 

the stated constraints, keeping the emitter below water at a fixed distance from the surface, and keeping 

stability when waves are present in the sensing environment. Multiple designs were evaluated for the 

0

10

20

30

40

50

60

70

0.00 0.49 0.73 0.98 1.22

Frequency (Hz/100)

Oil Thickness (mm)
Page 29

sensor package, all intended to act as buoy packages. Figure 16 shows a three-part package focused on

keeping light away from the light receiver. First, by having the sensor in Compartment ‘a’ pointing 

downward covered by the white curved cap, second by making all other parts mat black to minimize 

subsea light reflection. The source/emitter is placed in Compartment ’c’, while Compartment ‘b’ serves 

as the floating part of the package.

Figure 16: a) Light shield and cap, b) Light shield and floater, c) LED light channel and electronics 

holder

Figure 17, shows a first iteration of the model in Figure 16. For ease of manufacturing, modifications

were made to simplify the design in terms of geometry. Compartment ‘a’ serves as the electronics case 

where batteries and the light receiver are placed, similarly to the package in Figure 16, the sensor is 

downward facing, it can be accessed by screwing off the cap, ‘b’ is the light shield aiming to eliminate 

direct sunlight from being reflected towards the sensor. Compartment ‘c’ is a hydrodynamic foam 

floater holding the light source, the source similarly to the case in Figure 16 is powered by the battery 

in ‘a’ using wires passing by ‘d’. The main design improvement in the package seen in Figure 17 is the 

reduction of the interfering cross-sectional area of the wire tubes with the oil film, thus reducing the 

film distortion caused in the case of dynamic environments.

Figure 17: a) Electronics case, b) Light shield, c) Floater, d) Cable channel
Page 30

Figure 18, shows the second iteration based on the two previous models. This iteration aims for minimal 

manufacturing cost, maximum shading, and buoy stability. Nevertheless, this iteration is not ideal in 

terms of robustness against film distortion.

Figure 18: Maximum shading iteration (isometric view and front section plane view)
Page 31

Chapter 3- Capacitive Sensor Design and Initial Testing

3.1 Sensor Design

Since oil is lighter than water, it rises to the top of the water surface and floats, thereby forming an oil 

layer. The capacitive sensor is composed of an array of electrodes built onto a planar PCB. Capacitance 

is measured at each electrode independently to detect the number of electrodes immersed in oil and 

calculate the thickness. Due to sea water waves and the motion of the platform holding the sensor, the 

height of the examined liquid may vary up and down through the sensor plates, changing the 

surrounding medium of the electrodes (air/oil/water), and thus changing the capacitance measured 

between each couple of electrodes. Figure 19 shows a cross-section of the geometrical capacitive sensor 

design. The main advantage of the vertical capacitive sensing arrays is that calibration against different 

types of oil/water is not needed on site since the sensor functions based on the difference in capacitance 

between electrodes, and not on the absolute capacitance values.

By using the vertical capacitive sensor array design, the presented sensor can distinguish the water/oil 

interface and the oil/air interface. The thickness of oil can be deduced, regardless of where along the 

sensor this thickness occurred. In other words, in contrast to several implemented capacitive liquid level 

sensors found in the literature which use a floater to maintain a fixed position of the sensor on the top 

of the liquid surface, the presented sensor does not require any assumptions regarding its position 

relative to the liquid surface. Illustration in Figure 20 demonstrates that if the sensor can deduce the two 

interfaces between the different mediums, the thickness can still be calculated (to within the resolution 

of the sensor). The application of this sensor does not require any special packaging considerations 

beyond waterproofing of the box in which the electronics and the battery are housed.

Figure 19: Capacitive Sensor Design

PCB

Water (Ԑr= 80)

Oil (Ԑr= 2.1)

Electrodes

Capacitance

Air (Ԑr= 1)
Page 32

Figure 20: Illustration of vertical capacitive array at different positions

To maintain an accurate measure while the sensor is being dragged through the water and to reduce the 

fouling effect, the sensor is designed to be very flat (knife-like design) so that it possesses minimal 

resistance and drag. This way it “cuts” through water and oil whether going straight or in a turning

maneuver. Since the sensing array is mounted on a slim PCB plate (1.6mm thickness) with sharp edges, 

water comes off easily from the sensor body while dragging, reducing the accumulation of oil on the 

sensing strips. This was evident throughout the tests conducted.

It is important to note that while the main use case of the presented device is to be mounted on 

skimmers/booms, the capacitive sensor design allows for mounting it on several other platforms 

including Unmanned Aerial Vehicles (UAV) and drones, since the sensor can move freely in 

air/oil/water layers without the need for floaters or fixed platforms. In such cases, while scanning the 

oil spills area, the drone can take sample measurements of the oil thickness from several locations, and 

produce a map showing the oil distribution in the affected area.

3.2 Capacitive Sensor Initial Implementation

Capacitive Sensing Unit

Before implementing the Capacitive sensor prototype, several PCB boards with different electrode 

designs (Figure 21) were implemented and tested to demonstrate the concept of using the capacitive 

touch sensing in measuring oil thickness. The first implementation of the complete sensing unit contains 

thirty-seven conductive electrodes distributed equally from top to bottom on the top layer of a doublefaced PCB (Figure 22). The electrodes have the following dimensions: Width: 2mm, Length: 50mm, 

Vertical separation gap: 0.5mm. The bottom layer of the PCB contains the tracks connecting electrodes 

to the connection socket. A 37-Pin D-Subminiature PCB connector [20] [21] was selected to connect 

the sensing unit to the capacitive controller modules (MPR121) in the processing unit. The DSubminiature connectors are selected due to their compact size, and the high number of pins they offer.

To protect the sensing unit from the effects of direct contact with the examined liquid and to reduce 

fouling, the PCB was sprayed by an electrically isolating material (Nanoprotech) [22].

AIR

OIL

Water
Page 33

Figure 21: Initial sensing unit prototypes

Bottom View (Tracks) Top View (Electrodes)

Figure 22: Initial Capacitive Sensor Design

Capacitive Processing Unit

The capacitive processing unit (Figure 23) is composed of the capacitive touch controllers (MPR121), 

microcontroller module (Arduino Nano), and communication modules (GPS & RF). Basically, the 

MPR121 modules are used to measure the capacitance values of the sensor electrodes and the

microcontroller board (Arduino Nano) is used to control the measurement process, calculate the oil 

thickness, and report it wirelessly to the base station.
Page 34

Capacitive Touch Controllers (MPR121)

The MPR121 is a capacitive sensor controller used in touch and proximity sensing applications. It 

supports twelve sensing electrodes and can be connected to a microcontroller equipped with an I2C 

communication channel. This module normally works with an input voltage between 1.6V and 3.3V 

under a temperature range of -40°C to +85°C. It has a low current consumption of around 29μA at a 

sampling rate of 16ms. The address pin (ADD) is used to set the I2C address of the chip. By default, 

the ADD pin in the break out board (Figure 24) is connected to GND setting the I2C address to 0x5A

[27]. The chip may be configured to three other I2C addresses by connecting the ADD pin as follows: 

ADD to 3.3V = 0x5B / ADD to SDA = 0x5C / ADD to SCL = 0x5D [23].

Figure 24: MPR121 Breakout Board

Three MPR121 modules were used in the initial sensor design to cover the thirty-seven electrodes in 

the sensing unit. Each of the controllers is set to a unique I2C address by changing the connection of 

the ADD pin as described before. The connection between the controllers and the Arduino is set by 

using two power lines and two I2C communication lines (3.3V – 3.3V / SCL – A5 / SDA – A4 / GNG 

– GND). The sensing pins are connected sequentially to the electrodes in the sensing card starting from 

top to bottom. The code scans each electrode iteratively and reads the filtered voltage value provided 

by the internal 10-bit Analog-to-Digital-Converter (ADC).

The MPR121 can measure a range of capacitances from around 10pF to 2000pF with a resolution of 

0.01pF. The voltage measured by the chip is inversely proportional to the capacitance which is affected 

by the amount of charge stored in each electrode. The voltage of each electrode is measured after 

applying a constant amount of current for a fixed duration of time (Figure 25).

Capacitive Touch Controller (MPR121)

Analog Voltage 

Measured

MUX Sensing Unit (Electrodes Array)

Output 

Voltage ADC

First and Second 

Filter Stages

Microcontroller 

(Arduino Nano)

Wireless Module

(RF 2.4GHz)

GPS (SKM53)

Figure 23: Capacitive Processing Unit - Block Diagram
Page 35

Figure 25: MPR121 Voltage Measurement Cycle

The capacitance of the electrode changes due to the change in the dielectric constant of the examined 

liquid, thus changing the measured voltage. The amount of current (I) and the charge time (T) are

configured to set the charge amount applied to the electrodes. The peak voltage is measured after 

charging each electrode sequentially. The capacitance (C) is calculated as C = Q/V = IxT/V, Where 

Q is the charge amount measured in coulomb, and V is the voltage amount measured in volts.

The chip contains a set of different registers used to configure the operational parameters and to get the 

output data from the device. The main registers used in our application are listed as follows:

 Electrode Filtered Data Register (0x04-0x1D):

This register holds the filtered output data corresponding to the output of the second filter ranging from 

0 to 1024. This data is obtained by measuring the voltage value of each channel and converting it from 

analog to digital using the internal 10-bit ADC. This register is updated every ESI x SFI and is a readonly register.

 Filter/Global CDC Configuration Register (0x5C)

The first two bits of this register are used to set the number of iterations for the first filter (First Filter 

Iterations - FFI) and may be set to the following values: 00 (6 samples - Default), 01 (10 samples), 10 

(18 samples), and 11 (34 samples). The remaining six bits are used to configure the amount of charge 

current applied to the electrodes. This current configuration section is named the Charge Discharge 

Current (CDC) and can be set to a range of values between 0 and 63 (000000 (Disabled), 1 (1μA), 

010000 (16μA) (Default), and 111111 (63μA)).

 Filter/Global CDT Configuration Register (0x5D)

The first three bits of this register are used for configuring the charging time (Charge-Discharge Time 

- CDT), two bits to set the number of samples taken for the second filter (Second Filter Iterations –

SFI), and three bits to set up the sampling time (Electrode Sample Interval – ESI). The CDT can be set

to 32μs by setting the bits to 111 and may be disabled by using 000. Other values may be set by using 

the following equation: CDT = 2^(n-2), where n is the decimal encoding of the three-bit binary value. 

The default value for the CDT bits is 001 which corresponds to 0.5μs. The SFI can be set to the 

following values: 00 (4 samples – Default), 01 (6 samples), 10 (10 samples), and 11 (18 samples). The 

ESI may be set to a value between 000 and 111 which corresponds to period value between 1ms and 

128ms using the following equation period = 2^n ms, where n is the decimal encoding of the 3-bit 

binary value used. The 0x5C and 0x5D registers are the two main registers used in our application to 

Charging Discharging

T 2T

Measured Voltage (V)
Page 36

configure the MPR121 devices. All the global filtering and charging parameters are set up using these 

two registers. The applied settings will be utilized by all electrodes if the auto-configuration option and 

the individual charge/discharge feature are disabled.

Initial Capacitive Sensor Prototype

The connections between the Arduino module and the MPR121 modules are described in the schematic 

shown in Figure 26. Twelve input channels from each MPR121 module are connected to the electrodes 

of the sensing unit. The main power supply (VDD) of the MPR121 with an operational voltage range 

between 1.71V and 3.6V is connected to the 3.3V output pin in the Arduino board regulator and 

decoupled by a 0.1μF capacitor to GND. The GPS module (SKM 53) and the RF wireless transceiver 

(2.4GHz) is connected to the Arduino through hardware and software serial communication. Transistors 

(2N3904) are controlled by the microcontroller and used as a switch to power ON/OFF the GPS and 

wireless modules. The hardware implementation of the initial capacitive sensor prototype is shown in 

Figure 27.
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Figure 26: Capacitive Sensor - Control Circuit Schematic
Page 38

Capacitive Controller Circuit + GPS &Wireless

Initial Capacitive Sensor Prototype 

(Overall)

Capacitive Sensor (Top View - Electrodes)

Capacitive Sensor (Bottom View – Tracks)

Figure 27: Capacitive Sensor PCBs

3.3 Capacitive Sensor Initial Mechanical Design

Preliminary design iterations have been made, modifications have been made to each design to fit a 

final description of an easy to manufacture and sealed package. Figure 28 is the CAD model of the very 

first design made with the 3D printed prototype, it represents the first iteration, and contains a sealing 

chamber and a sealed cap with side brackets for fixture purposes, this iteration turned out to be bulkier 

than expected.
Page 39

Figure 28: Capacitive Sensor Packaging - Prototype 1

Figure 29 represents the first iteration, this iteration takes into account space for vibrating motors and 

battery storage, it also includes sealing measures. Nevertheless, it is not efficient for manufacturing 

since it is made up of four parts, each requiring small tolerance which would probably cause malfunction 

(weak seals and loose parts). Figure 29 represents an exploded view of the package, the grey parts made 

of rubber for sealing purposes, since the assembly needs to withstand splashing and occasional 

submersion depending on the conditions under which it is operated. The white parts are machined out 

of Polyamide, which was chosen as a material for its durability and shock absorption properties.

Figure 29: Capacitive Sensor Packaging - Iteration 1

The second iteration in Figure 30 takes into consideration the manufacturing process and tolerances 

with which this design can be achieved on the CNC mill. Sealing is achieved by applying pressure on 

rubber O-rings and gaskets. Figure 31 provides a close-up on the capacitive strip, control board, and the 

hasp lock chosen to apply pressure on the case gasket. Shop drawings with detailed dimensions for the 

iteration shown in Figure 30 are present in the Appendix at the end of this document.
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Figure 30: Capacitive Sensor Alpha Iteration (adopted for Prototype 2 machining)

Figure 31: a) Capacitive card, b) Control board, c) Spring reinforced hasp lock

The capacitive sensor packaging has been completed and tested for waterproofing. Three tests were 

made twice each to determine the package’s water resistance:

- Continuous water splashing for 2 minutes (passed)

- Continuous contact with running water under the sink for 2 minutes (passed)

- Submersion in water at a depth of 20cm for 30 seconds (passed)
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Figure 32: Alpha package - Bottom view

Figure 33: Alpha package and extension front view

Figure 34: Alpha package and extension right view
Page 42

Since all three tests were successful, a bracket/extension (Figure 32, Figure 33, Figure 34) was designed 

and 3D printed to fix the package to the linear actuator (part of the testbed discussed later). The package 

mounted on the testbed linear actuator was tested once upside down, and once in the proper orientation, 

the fixing bracket/extension was able to withstand the stresses, which peaked at the change in direction.

3.4 Capacitive Sensor Initial Testing 

Several experiments were performed to assess the performance of the proposed capacitive sensor under 

different conditions and using different oil types (Heavy/Light). During the experiments, the sensor was 

immersed in the examined liquid (oil/water), and voltage measurements were sent wirelessly to the base 

station. A software application was developed to receive and save the measured voltage values.

Oil Types Used in Initial Testing Experiments

3.4.1.1 Fuel Oil

Experiments were performed to assess the performance of the proposed capacitive sensor while working 

with heavy fuel oil. However, first we measured the viscosity of the procured heavy fuel oil using the 

viscous meter available in our labs, and the results were in the range of 3300 to 3500cP. The viscosity 

of the fuel oil was measured using the following procedure: a spindle of a specified diameter is 

immersed into the oil and rotated at a precise speed, the drag measured gives the viscosity of the 

medium. The results of the experiment are shown in Table 7.

Table 7: Measured Viscosity (Fuel Oil)

Spindle Speed (RPM) Viscosity (cP)

SP R4 10 3446.4

SP R4 20 3456.1

SP R4 30 3381.7

SP R4 50 3332.6

Average 3404.2

As shown in Table 7, while the measured viscosity is in Centipoise (cP), which represents the absolute 

viscosity of the oil, the kinematic viscosity is represented in Centistokes (cSt). The main difference 

between the two units is that the cSt (kinematic) represents the ratio of a liquid density to its absolute 

viscosity in cP. To convert from absolute (cP) to kinematic (cSt), the obtained values (cP) are divided 

by the density of the liquid. Most hydrocarbons (fuel or lubricating oil) have a density between 0.85 

and 0.9. To calculate the average kinematic viscosity of this fuel oil, the average of the measured 

absolute viscosity (3400cP) is multiplied by the density of the oil. For a density of 0.85, the kinematic 

viscosity is 4000cSt. For a density of 0.9, the kinematic viscosity is 3777cSt. As a conclusion, the 

kinematic viscosity of the available fuel oil is between 3777 and 4000cSt measured at room temperature 

(around 25°C).

3.4.1.2 Light/Medium Oil

Three different single-grade oil types available in the local Lebanese market were selected for the light 

and medium oil experiments each with a different viscosity, as shown in Table 8.
Page 43

Table 8: Viscosity of light and medium oil samples

Viscosity (cSt) Name Description

680 cSt BP Energol GR-XP 680 Industrial Extreme Pressure Gear Oil

140 cSt Power Gear 140W Automotive Gear Oil

10 cSt Power 10W Engine Oil

Preliminary Capacitive Sensor Experiments – Light Oil

Experiments were first conducted on the proposed capacitive sensor to assess its ability to differentiate 

between air, water, and oil.

3.4.2.1 Experiment 1 – Sensor in Air

In this experiment, the sensor was surrounded only by air, the values of the first twelve electrodes were 

measured and logged every five seconds. An instance of the experimental results is shown in Table 9, 

and the corresponding graph is shown in Figure 35.

Table 9: Experiment 1 (Air) – Numerical Values

Time E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12

10:25:30 180 170 168 172 170 175 172 175 169 171 166 170

10:25:35 180 170 168 173 171 176 172 175 170 171 166 169

10:25:40 180 170 167 173 170 176 172 176 170 172 165 170

10:25:45 180 170 168 173 171 176 172 175 169 171 166 169

10:25:50 180 169 167 173 170 177 172 175 169 171 166 169

10:25:55 180 170 167 172 170 176 172 175 169 171 166 169

10:26:00 180 170 167 173 170 176 172 175 169 171 166 170

Average 180 170 167 173 170 176 172 175 169 171 166 169

Figure 35: Experiment 1: Sensor in Air

The results of this experiment showed a difference between the absolute voltages measured by each 

electrode while placed in the same medium (air). This difference is primarily attributed to the resulting 

inevitable manufacturing inconsistencies between each electrode, including issues such as trace length, 

soldering, and connectors. However, since our oil thickness estimation algorithm is based on relative, 

100

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10:25:3010:25:5010:26:1010:26:3010:26:5010:27:1010:27:3010:27:5010:28:1010:28:3010:28:5010:29:1010:29:3010:29:5010:30:1010:30:3010:30:5010:31:1010:31:3010:31:5010:32:1010:32:3010:32:5010:33:1010:33:3010:33:5010:34:1010:34:3010:34:5010:35:10

Voltage (ADC)

Time

E1 E2 E3 E4 E5 E6

E7 E8 E9 E10 E11 E12
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and not the absolute voltage measurements, the difference between the measured values will not affect 

the accuracy of our results. More importantly is the stability and repeatability of the measured values 

over time.

3.4.2.2 Experiment 2 – Locating the Oil/Water Interface (Light-oil)

The aim of this experiment is to monitor the effect of oil on the actual voltage value measured by each 

electrode. The sensor was placed initially in water, and light oil (10cSt) was gradually added to the 

container; this procedure ensures no oil contact with the bottom sensor electrodes during immersion. 

Thus, the first six electrodes from the top of the sensor were covered with oil, and the remaining 

electrodes are immersed in water. Voltage values of the first twelve electrodes were measured every 

five seconds; an instance of experimental results is shown in Table 10, and the corresponding graph is 

shown in Figure 36.

Table 10: Experiment 2 (Light Oil/Water) – ADC Voltage Values

Time E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12

11:11:03 168 157 157 162 159 162 53 6 6 6 5 6

11:11:08 167 157 158 162 159 162 53 6 6 6 5 6

11:11:13 167 157 158 162 159 162 53 6 6 6 6 6

11:11:18 168 157 158 162 159 162 53 6 6 6 6 6

11:11:23 168 157 157 162 159 162 53 6 6 6 6 6

11:11:28 168 157 158 162 160 162 53 6 6 6 6 6

11:11:33 167 157 157 162 159 162 53 6 6 6 6 6

11:11:43 168 157 157 162 159 162 53 6 6 6 5 6

11:11:48 168 157 157 162 159 162 53 6 6 6 5 6

11:11:53 168 157 158 162 159 162 53 6 6 6 6 6

11:11:58 167 157 157 162 159 162 53 6 6 6 6 6

Average 168 157 157 162 159 162 53 6 6 6 6 6

Figure 36: Experiment 2 - Light Oil / Water

0

20

40

60

80

100

120

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180

11:02:3211:03:0711:03:2711:03:5211:04:1211:04:3211:04:5211:05:1211:05:3211:05:5811:06:1811:06:3811:06:5811:07:1811:08:0311:08:2311:08:4311:09:1811:09:3811:09:5811:10:1811:10:3811:10:5811:11:1811:11:43

Voltage (ADC)

Time

E1 E2 E3 E4 E5 E6

E7 E8 E9 E10 E11 E12
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As shown in Figure 36, the results show that the voltage measured by the bottom five electrodes (E8-

E12) was much lower than the voltage measured by the other electrodes. This result was expected 

because of the relatively high conductivity of water. Also, the voltage of the first five electrodes 

(covered by oil) was lower than the voltage read by the same electrodes while placed in the air medium 

(experiment 1). The voltage measured by the seventh electrode (E7) was partially decreased because of 

it being located at the interface of oil and water, and accordingly being partially immersed in oil. The

results of this experiment were considered encouraging since it proved the viability of our approach in 

differentiating between electrodes immersed in air, oil, or water.

3.4.2.3 Experiment 3 – Locating the Oil/Air Interface (Light Oil)

The aim of this experiment is to monitor the difference between the voltage measured by each of the 

sensor electrodes while moving the sensor between the air and oil mediums. The voltage measured by 

the first twelve electrodes of the sensor were recorded while moving the sensor in a random manner 

between the two mediums. A measurement was recorded every second. An instance of the experimental 

results is shown in Table 11, and the corresponding graph is shown in Figure 37.

Table 11: Experiment 3 (Air/Oil) – ADC Voltage Values

Time E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 Status

11:23:27 179 170 168 173 170 175 171 175 169 171 165 169

Air

11:23:29 179 170 168 173 170 175 171 175 169 171 165 169

11:23:30 179 170 168 173 170 175 171 175 169 171 165 169

11:23:31 179 170 168 173 170 175 171 175 169 171 165 169

11:23:32 179 170 168 173 170 175 171 175 169 171 165 169

11:23:34 179 170 168 173 170 175 171 175 169 171 165 169

11:23:35 179 170 168 173 170 175 171 175 169 171 165 169

11:23:36 179 170 168 173 170 175 171 175 169 171 165 169

11:23:38 179 170 168 173 170 175 171 175 169 171 165 169

11:23:39 179 169 166 171 168 172 168 172 165 167 161 165

11:23:40 170 161 158 162 160 164 160 163 158 159 154 158

Oil

11:23:41 170 161 158 162 160 164 160 163 158 159 154 157

11:23:43 170 161 158 162 160 164 160 163 158 159 154 157

11:23:44 170 160 158 162 160 164 160 163 158 159 154 157

11:23:45 170 160 158 162 160 164 160 163 158 159 154 157

11:23:47 170 161 158 162 160 164 160 163 157 159 154 157

11:23:48 170 160 158 162 160 164 160 163 158 159 154 157

11:23:49 170 160 158 162 160 164 160 163 158 159 154 157

11:23:51 170 160 158 162 160 164 160 163 158 159 154 157

11:23:52 170 160 158 162 159 164 160 163 158 159 154 157

11:23:53 170 160 158 162 160 164 160 163 158 159 154 157
Page 46

Figure 37: Experiment 3 (Air/Light Oil)

The experiment shows an immediate change in the measured voltages upon moving between the oil and 

air mediums. Note that although the difference is small, the measured voltage in the air is always higher 

than in oil.

Preliminary Capacitive Sensor Experiments – Heavy Oil

3.4.3.1 Experiment 1 – Locating Oil/Water Interface (Fuel Oil)

In this experiment, the sensor was immersed in fuel oil and water, the values of three electrodes were 

measured and logged every one second. The aim of this experiment is to monitor the change in the 

sensor voltage readings while moving the sensor electrodes between fuel oil and water. A sample of the 

numerical results are shown in Table 12, and the corresponding graph is shown in Figure 38

(Temperature: 23°C, RH: 60%).

Table 12: Fuel oil experiment 1- ADC Voltage Values

Time E8 E9 E10 Time E8 E9 E10

3:19:13 165 160 159 3:20:13 165 158 157

3:19:14 165 161 159 3:20:14 165 158 157

3:19:15 166 161 159 3:20:15 166 158 157

3:19:16 166 161 159 3:20:16 166 159 157

3:19:17 166 161 158 3:20:17 167 159 157

3:19:18 166 161 159 3:20:19 167 160 158

3:19:19 166 161 159 3:20:20 167 160 158

3:19:20 166 161 159 3:20:21 168 161 158

3:19:21 166 161 159 3:20:22 168 162 159

3:19:22 166 162 160 3:20:23 168 162 159

3:19:23 166 162 160 3:20:24 168 163 159

3:19:24 166 162 161 3:20:25 167 163 160

3:19:26 166 162 161 3:20:26 168 163 160

3:19:27 167 162 162 3:20:27 167 163 160

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11:22:3411:22:4411:22:5511:23:0511:23:1611:23:2611:23:3611:23:4711:23:5711:24:0711:24:1811:24:2811:24:3911:24:4911:24:5911:25:1011:25:2011:25:3111:25:4111:25:5111:26:0211:26:1211:26:2211:26:3311:26:4311:26:5411:27:0411:27:1411:27:2511:27:3511:27:4611:27:56

Voltage (ADC)

Time(hh:mm:ss)

E1 E2 E3 E4 E5 E6

E7 E8 E9 E10 E11 E12
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3:19:28 167 162 162 3:20:28 167 163 161

3:19:29 167 162 162 3:20:29 167 163 161

3:19:30 167 162 162 3:20:30 167 163 160

3:19:31 167 162 162 3:20:32 167 163 160

3:19:34 167 162 162 3:20:33 167 163 160

3:19:35 167 162 162 3:20:34 167 163 159

3:19:36 167 162 162 3:20:35 167 162 159

3:19:37 167 162 162 3:20:36 165 158 157

3:19:39 167 162 162 3:20:37 161 156 156

3:19:40 167 162 162 3:20:38 161 156 156

3:19:41 167 162 162 3:20:39 161 156 156

3:19:42 167 163 162 3:20:40 161 156 156

3:19:43 167 163 162 3:20:41 161 156 156

3:19:44 167 163 162 3:20:42 161 156 156

3:19:45 168 163 162 3:20:43 161 156 156

3:19:46 168 163 162 3:20:45 161 156 155

3:19:47 168 163 161 3:20:46 161 156 155

3:19:48 168 163 161 3:20:47 161 156 155

3:19:49 168 163 161 3:20:48 161 156 155

3:19:50 168 163 160 3:20:49 161 156 155

3:19:52 164 158 157 3:20:50 161 156 155

3:19:53 161 156 156 3:20:51 161 156 155

3:19:54 160 156 156 3:20:52 161 156 155

3:19:55 160 156 156 3:20:53 161 156 156

3:19:56 160 156 156 3:20:54 161 157 156

3:19:57 160 156 156 3:20:55 162 157 156

3:19:58 160 156 156 3:20:56 162 157 157

3:19:59 160 156 155 3:20:57 163 157 157

3:20:00 160 156 155 3:20:59 164 158 157

3:20:01 160 155 155 3:21:00 165 158 157

3:20:03 160 156 155 3:21:01 166 159 157

3:20:04 160 156 155 3:21:02 167 159 158

3:20:06 161 156 155 3:21:03 167 160 158

Figure 38: Fuel-oil experiment 1 – Locating oil/water interface

The results show the difference between the absolute voltage measured by each electrode while moving 

between fuel oil and water. The high values illustrated in Figure 38 correspond to values measured 

while the electrodes are in fuel-oil, and the low values correspond to values measured in water.

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3:19:133:19:163:19:193:19:223:19:263:19:293:19:343:19:373:19:413:19:443:19:473:19:503:19:543:19:573:20:003:20:043:20:083:20:113:20:143:20:173:20:213:20:243:20:273:20:303:20:343:20:373:20:403:20:433:20:473:20:503:20:533:20:563:21:003:21:033:21:063:21:09

Voltage (ADC)

Time

E8 E9 E10
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Although the sensor electrodes are completely covered with a thin layer of oil after being immersed in 

the fuel oil for the first time (Figure 39), the voltage values measured by the electrodes, while being 

immersed in oil are different from the values measured in the air, despite being covered with fuel oil. 

This difference is used in our algorithm to distinguish between oil/water interfaces.

Figure 39: Capacitive Sensor - Initial Experiment (Fuel Oil) - Experimental Setup

3.4.3.2 Experiment 2 – Locating Oil/Air Interface

The aim of this experiment is to monitor the difference between the voltage measured by the sensor 

electrodes while moving between fuel oil and air. Voltage values of two electrodes were measured every 

one second; experimental results for one of the experiments are shown in Table 13, and the 

corresponding graph is shown in Figure 40 (Temp: 23°C, RH: 60%).

Table 13: Fuel oil experiment 2- ADC Voltage Values

Time E6 E7 Time E6 E7

2:21:50 172 167 2:22:26 170 165

2:21:51 172 167 2:22:27 170 165

2:21:52 172 167 2:22:28 170 165

2:21:53 172 167 2:22:29 170 165

2:21:54 172 167 2:22:30 170 165

2:21:55 172 167 2:22:31 170 165

2:21:56 172 167 2:22:32 170 165

2:21:57 172 167 2:22:33 170 165

2:21:58 172 167 2:22:34 170 165

2:22:00 172 167 2:22:35 170 165

2:22:01 172 167 2:22:36 170 165

Air

Fuel Oil

Water
Page 49

2:22:02 172 167 2:22:37 170 165

2:22:03 172 167 2:22:39 170 166

2:22:04 172 167 2:22:40 170 165

2:22:05 172 167 2:22:41 170 166

2:22:06 170 163 2:22:42 170 165

2:22:07 166 160 2:22:43 170 165

2:22:08 164 160 2:22:44 170 166

Figure 40: Fuel oil experiment 2 – Locating air/oil interface

The results show that the voltage measured by the two electrodes (E6-E7), while moving between fueloil and air are different. In the last part of the experiment, the rate of movement was increased thus 

producing a higher frequency signal as shown in Figure 40. The importance of this result lies in that it 

reveals that the sensor is feasible for oil types with high viscosity such as the tested fuel oil.

Preliminary Capacitive Sensor Experiments - Environmental Conditions 

To assess the environmental conditions (temperature and relative humidity) effect on the measurements 

we conducted two experiments. In the first experiment, the sensor was placed in open air, and the 

readings were logged every 30 sec. During the 16-hour experiment, the relative humidity varied 

between 72% and 58% and the temperature ranged from 21°C to 24.7°C. The span of measurement for 

each electrode was very limited and didn’t exceed 3.8% in the worst case.

In the second experiment, the sensor was placed in a closed room where the temperature and humidity 

were changing relatively fast due to air conditioning. During the experiment, the relative humidity 

varied between 60% and 46% and the temperature varied between 21.2°C and 24.7°C. The span of 

measurement for each electrode was very limited and didn’t exceed 2.7% in the worst case.

As a result, these experiments provided an initial evidence that the sensor repeatability is high under 

different environmental conditions.

3.5 Preliminary Algorithms for Estimating Oil Thickness 

Primarily, the measurement algorithms aim to calculate the oil thickness based on the dimensions of the 

sensor. Several approaches were evaluated to detect the most accurate and reliable method to estimate 

the oil thickness for all environmental cases (static/dynamic). The following sections describe the 

different approaches.

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E6 E7
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Non-supervised Clustering Algorithm (K-Means)

Before implementing any algorithm to estimate oil thickness we plotted the experimental results on a C 

vs. ΔCt graph, where C is the difference between the baseline capacitance (in air) of each strip and its 

capacitance in its residing medium; ΔCt is the transient change in C, representing how fast the oil is 

slipping off a strip (Figure 41).

Figure 41: C vs. ΔCt

In this graph, we noted the obvious clustering of data points according to the medium they are in. 

Accordingly, we attempted to group data points using a clustering algorithm known as K-means[26], 

with a feature vector comprised of C and ΔCt; K-means is known to produce good results when the 

number of clusters K is known beforehand. Here we chose three clusters, representing air, oil, and water. 

To implement the algorithm in real-time, the software was developed using Microsoft Windows Forms

.Net (C#) framework. The results of the measurement algorithm including the estimated oil-thickness 

(mm) and the index of electrodes included in each cluster and the raw voltage measurements are shown 

in the Graphical User Interface of the application (Figure 42).

Figure 42: K-means algorithm results
Page 51

As shown in Figure 42, the activated electrodes (first ten) are clustered into three different groups 

(air/oil/water). The index of each electrode classified into one of the three clusters is shown in the figure. 

From a position directly above the fluids, the sensor is lowered, in a vertical direction, into the oil/water 

medium. Notice that, as expected, the decreasing values of the bottom two electrodes (E8 & E9) did not 

affect the classification decision since the rate of change is considered as the second attribute of 

clustering. The estimated number of oil electrodes (4) in this case was identical to the actual number of 

electrodes immersed in oil, as was noted visually.

Although the results of the K-means algorithm were accurate while the sensor was not moving (static 

case), the results were not reliable in the dynamic case, because K-means under vertical movement 

results in inconsitent clustering primarily because of strips that are in transition between mediums and 

got fouled (e.g, oil-water).

Thickness Estimation Based on Interfaces Detection

In this approach, oil thickness was estimated based on the difference between the air/oil interface and 

the oil/water interface. The algorithm starts by calculating the relative change of voltage of each 

electrode from its calibrated value—measured while the sensor is in air. This step is essential for 

normalizing the voltage measurements taken by all electrodes. Then, for each electrode of index “i” we 

divide its voltage differential with that of its neighbor electrode of index “i+1” (lower electrode), and 

call this parameter “Ratios”. To avoid division by zero, null deltas on the denominator are replaced by 

a value of one.

To test the algorithm, an experiment was conducted on a light engine oil (10W-40). The sensor was 

immersed in an oil/water mixture with 1cm of oil thickness. The experiment was implemented in the 

lab under a temperature of 24 °C and relative humidity of 59%; a sampling rate of 1/300msec was 

adopted. A sample of the calculated ratios is shown in Figure 43. To get the oil thickness, the algorithm 

proceeds by calculating the difference between the index of the electrode located at the oil/water 

interface layer and the index of the electrode located at the air/oil interface layer. To do so, the interfaces 

are identified based on the relative electrode behavior. Depending on the results shown in Figure 43, 

we notice a clear difference between the values before and after electrode E6. Also, electrode E10 shows 

a similar behavior.

Figure 43: Interfaces detection algorithm – Ratios

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Ratios

Electodes
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The electrodes located at the interface layers (E6 and E10) show a severe drop in the computed ratios. 

To get the index of each interface electrode, the first two minima of the ratios series are identified. Then,

the difference between the indexes of the two minimum electrodes is calculated (E10 - E6 = 4) and is 

used for calculating the oil thickness based on the dimensions of the electrodes.

To assess the performance of this algorithm in static cases, accuracy values were calculated after taking 

a set of thickness measurements. Two cases were tested in this experiment: the first case represents 

applying the measurement algorithm on each voltage reading without using any averaging; in the second 

case, a moving average of the voltage readings is used before applying the measurement algorithm. To 

calculate the moving average, each new voltage reading is added to the voltage measurements taken 

before it and the result is divided by two.

The following table presents the results, where in each case, the average of around three hundred 

measurements was taken. The oil used is light-oil, with a viscosity of 10 cSt. The experiment was done 

indoor at a temperature of around 24°C. The first case results are shown in Table 14 and the second 

case measurements are shown in Table 15.

Table 14: Static Experimental Results (Case 1)

Index of Oil/Water 

Interface

Index of Water/Oil 

Interface

Oil 

Interval

Measured 

Thickness (mm)

Actual 

Thickness (mm)

Percentage 

Error (%)

29 24.05 4.94 11.86 12.5 5.08

Table 15: Static Experimental Results (Case 2)

Index of Oil/Water 

Interface

Index of Water/Oil 

Interface

Oil 

Interval

Measured 

Thickness (mm)

Actual 

Thickness (mm)

Percentage 

Error (%)

29 24 5 12 12.5 4

The experimental results show that after applying the moving average (Case 2) the percentage error is 

reduced from 5% to 4%. As a result, this algorithm was considered acceptable for static conditions. 

Unfortunately, for dynamic situations, due to fouling a relatively-high error was observed in detecting 

the air/oil interface. Therefore, this method was deemed acceptable for static conditions, but not 

recommended for the dynamic cases.

Based on the results obtained from the first two methods, we concluded that the algorithm must first be 

capable of differentiating between the static and dynamic cases, and focus on the strips that are 

exhibiting change, since those would be the electrodes that are transitioning between mediums. In the 

next trials, the work was focused on dealing with high-dynamic conditions.
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Chapter 4- Experimental Setups

In order to simulate the environments and conditions within which the sensors will be operating we 

designed and developed two main experimental test-beds. The experiments were organized into two 

categories, indoors and outdoors. Therefore, indoor testbeds and an outdoors testbed were designed, 

manufactured, and used to run all planned experiments. For indoors experiments two testbeds were 

made, one for the LED-based sensor, and another for the capacitive sensor. As for the outdoor 

experiments, a single testbed was made for both sensors. The testbed is a large tank made to simulate 

the behavior of the sensors in open sea conditions.

4.1 LED-based Sensor Testbed

The following design was used for tests conducted in our small tank; it comprises of two major parts: 

the fixed sensor case shown in Figure 44, and the LED/emitter case of Figure 45. The first design is 

intended to be suspended above the oil during tests, and the second is intended to be moved vertically,

while keeping alignment between LED and spectrometer. Once the optimal distance between emitter 

and spectrometer is determined, it would be possible to design a third package to contain both the light 

source and spectrometer in their fixed validated positions. Shop drawings with dimensions of this 

testbed can be found in the Appendix.

Figure 44: Exploded view of assembly fit for the spectrometer case
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Figure 45: Experimental Spectrometer package assembly (Left) and exploded view (right)

An experimental package was designed and manufactured for the indoor lab, (Figure 46, Figure 47). 

The distance between light source and frequency sensor is adjustable by including slider beams on 

which the light source package is lowered and lifted on an M10 screw shaft that rotates around its own 

axis. Rotating the screw clockwise lifts the source holder with respect to the sensor box, and inherently 

lowering the source with respect to the sensor box by rotating counter-clockwise. The freedom to vary 

the distance between both parts of the setup allows the user to identify the optimal distance for reading 

accuracy and sensitivity to oil film thickness. The package was machined on the CNC out of polyamide, 

due to its ease of machinability and low friction coefficient. Figure 46 shows the experimental setup’s 

upper part open (left) and closed (right), the upper part is a box manufactured to fit the experimental 

printed circuit board with the 12V battery. The system is mounted to a linear actuator in case dynamic 

actuation is desired in future experiments.

Figure 46: Sensor/battery box (left: lid off, right: lid on)
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Figure 47: complete setup (left: lid off, right: lid on)

4.2 Capacitive Sensor Indoors Lab Testbed

The indoor lab tank design was implemented to test the capacitive sensor in various conditions 

encountered in the application environment such as waves, still water, and sensor tilting due to dragging 

in the sea. The tank is made of plexiglass and equipped with a mechanism for sensor actuation. The 

implemented lab tank is shown in Figure 48.

Figure 48: Capacitive Sensor Experimental Testbed - Lab tank
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A 3-phase brushless DC motor (BLDC) was chosen to actuate the rack and pinion mechanism fitted on 

the tank, the motor specifications are as follows:

 Operating Voltage: 12V

 Motor rated speed: 3700rpm

 Motor Diameter: 36mm

 Gearbox: planetary gear reducer

 Speed: about 150rpm/s.

 Shaft length: 20mm

 Reduction ratio: 27:1

 Signal cycle pulse number: 2*27

 Control mode:

 PWM speed control

 Direction control

 Feedback pulse output

The motor is interfaced with MATLAB Simulink (Figure 49) using an Arduino Mega 2560 to run realtime external mode simulations with all type speed profile input normalized to PWM range. After 

conducting several experiments on the motor for parameter identification purposes, it was noticed that 

the motor speed reaches a maximum of 150rpm at a PWM range of 150 to 255.

Figure 49: overall Simulink block diagram
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Figure 50: Motor performance

As seen in the comparison of Figure 50 the maximum speed is reached at 150PWM input for most 

inputs, the response is fast enough for our application. Considering that one revolution induces 3cm of 

travel for the rack and pinion mechanism and that the sensor capacitive card is 15cm in length, the 

desired wave profiles can all be realized in terms of relative amplitude travel and wave frequency using

the motor at hand mounted on the designed lab tank setup. Upon testing the motor, it was noticed that 

due to backlash and sideways play, a non-uniform load is applied on the motor shaft by the rack and 

pinion mechanism causing a random change in torque. Therefore, a bang-bang controller was adopted 

to make the position tracking independent of such disturbances; the results of this method were much 

acceptable for wave amplitude simulation. A second change to the tank has been made on the software 

application end, allowing the user to set the frequency, amplitude, and phase shift of the simulated 

wave/sensor trajectory. Both the software and hardware implementation changes allowed the 

experiments to be performed more efficiently but one person.

4.3 Capacitive Sensor Outdoors Lab Testbed

The tank in Figure 51 shows the final design used to build the outdoors testbed; modifications were

made regarding the tank partitioning, such as the inclusion of a wooden separator along the length of 
Page 58

the tank in order to enable testing of different conditions in the tank at the same time. The linear actuator 

in Figure 55 can be placed on either side of the partitions since it is not fixed on the tank rim. The tank 

dimensions are 1x1x3meters (three meters cubed volume), although only a fraction of the tank’s volume 

(~50%) is filled with seawater and oil. The medium is controlled with a submersible controllable pump 

(Figure 52, Figure 53) in order to test various wave profiles. The complete setup was tested in rainy and 

sunny weather, with no complications regarding waterproofing both on the sensor and tank ends. 

Nevertheless, it was noticed that due to residue and debris accumulation from tree and dirt around the 

tank, caused the pump to clog if the tank isn’t cleaned prior to testing. Manufacturing drawings of the 

steel tank can be found in the Appendix.

Figure 51: Large tank Galvanized steel SolidWorks design

The chosen pump model is the Abyzz A200 manufactured by VENOTEC Germany, it can deliver up 

to 17 l/h flow rate at a maximum speed of 5.4m/s with a maximum head of 8.8m and a maximum

pressure of 1.5bar, and a power range of 4 to 200W.

Figure 52: Abyzz 200 controllable pump Figure 53: Pump specifications
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The large wave tank was manufactured and delivered to the test location (Figure 54), the wave 

generating pump was also delivered, and was tested to check the resistance of the tank to waves. The 

pump was first set to run in positive mode from 0% to 100% (200W) power, the random mode was 

tested with random flow directions at random power delivery; wave mode was tested with varying flow 

direction time (symmetric profile and non-symmetric profile). Furthermore, the pump was tested with 

minimum negative flow direction (varying from 0% to 100%) as well as positive direction (minimum, 

to the full power positive flow direction). The tank passed all tests, welds were checked twice for 

leakage as well as the see-through windows, and no leaks were detected. Finally, for the sake of 

displacing the sensor through the tank, a linear actuator with a stroke of 1.5meters was fixed onto the 

tank (Figure 55).

Figure 54: Wave tank

.

Figure 55: Linear actuator

Functionality tests were made on the large tank, where actuator, switches and control panel were

waterproofed and tested in the rain while the sensor was in operation. A control panel (Figure 56) was

developed for the actuator, in which the panel contained a 220VAC to 24VDC and 5VDC switching 

mode power supply (SMPS) to power the stepper drive. and the Atmega microcontroller; both the drive 

and the microcontroller were mounted on the panel as well. A potentiometer along with limit switches

were added for speed control and direction change respectively.
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Figure 56: Control panel Figure 57: 3D printed extension installed on rail

A bracket/extension (Figure 57) was designed and 3D printed to fix the package with the linear actuator. 

The package mounted on the actuator was tested once upside down and once in the proper orientation, 

the fixing bracket/extension was able to withstand the stresses, which peaked at the change in direction.

Figure 58, shows a photo of the initial capacitive sensor prototype mounted on the large tank actuator 

while dragging.

Figure 58: Photo of Capacitive sensor mounted on the large tank actuator while dragging
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Chapter 5- LED-based Sensor Design Evaluation and Refinement

This chapter describes the experimental work done to evaluate the preliminary LED-based sensor 

design performance under different conditions. After analyzing the experimental results, design 

refinements were applied to the final buoy and sensor design.

5.1 Experiments for Tuning Distance

As described in the previous sections, the LED-based sensor measures the thickness of a transparent oil 

layer floating on water surface using the principle of light-absorption analysis. The LED-based sensor 

device is composed of a light source module that emits a blue light on the lower surface of the oil-water 

mixture and a light-to-frequency converter module to measure the intensity of the light after penetrating 

the oil layer. To select the optimal distance between the two modules (light-emitter and light-sensor), 

several experiments were performed to tune the system by recording the sensor response with respect 

to a set of different distances. The experimental setup shown in Figure 59 is used for this purpose, where 

it is composed of a graduated plexiglass container (Figure 59-Left), and a variable distance platform 

(Figure 59-Right). The variable distance platform is composed of two independent units connected with 

a waterproof cable. While the lower unit contains the light source (blue LED), the upper unit contains 

the sensor board. Changing the distance between the two units is done by rotating a threaded rod as 

described in previous chapter.

Figure 59: Experimental setup for LED-based sensor tuning experiments

Distance Tuning Experiment 1 (5.8/ 6.5/ 7.4 cm - sensitivity (x100) - scaling (100))

The first set of experiments were performed using the following distances: 5.8, 6.5, and 7.4cm. During 

these experiments, the sensitivity and scaling factors of the light-to-frequency converter were set to 100. 

The experiments were performed indoors using a light lubricating oil with a viscosity of 10cSt. The 

experimental results of the three experiments are shown respectively in Figure 60, Figure 61, and Figure 

62.
Page 62

Figure 60: LED-based Sensor Tuning – Distance: 5.8 cm, Sensitivity: 100, Scaling: 100

Figure 61: LED-based Sensor Tuning – Distance: 6.5 cm, Sensitivity: 100, Scaling: 100

Figure 62: LED-based Sensor Tuning – Distance: 7.4 cm, Sensitivity: 100, Scaling: 100

The results of the first set of tuning experiments showed that the output frequency decreased as the oil 

thickness was increased. Since the output frequency is related in a direct manner to the received light 

intensity, it is concluded that as the oil thickness increases the penetrating light is more attenuated. 

However, as shown in Figure 60, and Figure 61, for distances below 7cm, major fluctuations appeared,

6400

6500

6600

6700

6800

6900

7000

7100

7200

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Output Frequemcy (Hz)

Oil Thickness (μm)

5600

5800

6000

6200

6400

6600

6800

7000

0 500 1000 1500 2000 2500 3000 3500

Output Frequency (Hz)

Oil Thickness (μm)

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Output Frequency (Hz)

Oil Thickess (μm)
Page 63

especially in the thicknesses below 1000μm. In contrast, results obtained from the 7.4cm experiment 

shown in Figure 62 show more stable and an almost linear response with respect to oil thickness.

Distance Tuning Experiment 2 (7.4/8.5 cm - sensitivity (x1) - scaling (100)

To assess the effect of different configuration parameters on the system response, several distances were 

tested while the sensitivity factor of the light-to-frequency converter module was decreased to 1x, and 

the scaling factor was set to 100. The first two experiments were done using the same light source. A

resistor of 220Ω was connected in series with the blue-light LED. After that, the LED intensity was 

increased by replacing the series resistor by a smaller resistance of 100Ω, and the second experiment 

with 8.9cm distance was repeated. The experimental results of the three experiments including the 

frequency response with respect to the oil thickness are shown in Table 16 and the corresponding graph

is shown in Figure 63.

Table 16: LED-based - Experimental Results - Distance tuning experiment 2

Frequency (Hz/100)

Thickness (um) 7.4cm 8.9cm 8.9cm (increased LED brightness)

0 81.921 62.581 104.167

1000 76.233 58.233 97.577

2000 73.332 54.695 92.133

3000 70.932 52.089 88.723

4000 67.421 50.173 84.578

5000 65.326 47.944 79.500

Figure 63: LED-based Sensor Tuning – Experiment 2 (Graph)

The three experiments showed an almost linear relation between the output frequency and the oil 

thickness, with no major fluctuations. However, major differences between the three curves were 

recorded regarding the threshold and slope values. To interpret the result more precisely, the average 

value and the average slope of each curve was calculated. The average threshold was obtained by 

calculating the arithmetic mean of all frequencies recorded in each experiment. Slope values were 

40.000

50.000

60.000

70.000

80.000

90.000

100.000

110.000

0 1000 2000 3000 4000 5000

Frequency (Hz/100)

Oil Thickness (μm)

7.4cm 8.9cm 8.9cm (increased LED brightness)
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obtained by calculating the absolute differences between consecutive frequencies, recorded by each 

experiment. In addition, the range of each experiment is acquired by calculating the absolute difference 

between the maximum and minimum frequency recorded in each experiment. The threshold, slope, and 

range values for each experiment are shown in Table 17 and the corresponding graph is shown in Figure 

64.

Table 17: Distance Tuning Experiment 2 - Comparison

Thickness (um) 7.4cm 8.9cm 8.9cm (increased LED brightness)

Average Slope 3.319 2.927 4.933

Average Threshold 72.527 54.285 91.112

Range (Max - Min) 16.595 14.637 24.667

Figure 64: Distance tuning experiment 2- Comparison (graph)

The experimental results of the second set of tuning experiments showed that when the distance was 

increased from 7.4 cm to 8.9cm, the threshold, range, and slope values were decreased. Since working 

at a larger distance is more desired to protect the sensor lenses from fouling while working in dynamic 

sea conditions (waves), a solution was tested by increasing the LED brightness. The result of the 8.9cm 

experiment with enhanced LED brightness showed the maximum values of frequency slope, threshold, 

and range. Regarding the threshold value, the result was expected since a larger amount of light is 

incident on the sensor lens. The most important point is that the slope and range values representing the 

system sensitivity to oil thickness changes were increased. This result proved the efficiency of using 

distances larger than 7cm (8.9 cm) while increasing the light intensity.

Distance Tuning Experiment 3 (6.5/10 cm - sensitivity (x10) - scaling (10)

In the third set of experiments, we increased the sensitivity factor to 10x and decreased the scaling 

factor to 10. Two experiments were performed using two different distances (6.5cm - 10cm). The 

experimental results describing the frequency response with respect to the oil thickness in the two 

cases are shown in Table 18 and the corresponding graph is shown in Figure 65.

0 10 20 30 40 50 60 70 80 90 100

Average Slope

Average Threshold

Range

8.9cm (increased LED brightness) 8.9cm 7.4cm
Page 65

Table 18: LED-based - Experimental Results - Distance tuning experiment 3

Frequency (Hz)

Thickness (um) 6.5 cm 10 cm

0 7799.49 5910.55

100 7508.87 5651.60

200 7221.89 5776.64

300 7114.79 5556.38

400 6982.67 5474.42

500 6865.20 5385.42

1000 6582.92 5160.02

2000 6113.75 4871.74

3000 5101.94 4519.09

4000 4835.85 4168.43

5000 4621.81 3845.51

Figure 65: LED-based Sensor Tuning – Experiment 3 (Graph)

The frequency response recorded at 6.5cm and 10cm showed a similar linear relation with the oil 

thickness. However, it was observed that the 6.5cm frequency curve decreased in average when the 

distance is increased to 10cm. This result was expected since the amount of light reaching the light-tofrequency converter decreases as the distance is increased. To assess the effect of the distance increase 

on the sensitivity of the system, more detailed interpretation was performed by calculating the average 

slope, average threshold, and range of the curves obtained from the two experiments (Table 19, Figure 

66).

3000

4000

5000

6000

7000

8000

9000

0 1000 2000 3000 4000 5000

Output Frequency (Hz)

Oil Thickness (μm)

Distances

6.5 cm 10 cm
Page 66

Table 19: Distance Tuning Experiment 3 - Comparison

Frequency (Hz)

Thickness (um) 6.5cm 10cm

Range 3177.683919 2065.044133

Average 6431.748564 5119.987496

Average slope 317.7683919 231.5130086

Figure 66: Distance Tuning Experiment 3 - Comparison (Graph)

The slope and the range of frequency change were also decreased with increasing the distance. This 

result revealed that the 6.5cm was better in terms of sensor sensitivity to changes in oil thickness. 

However, a tradeoff between the sensitivity and the distance was considered since larger distance 

contributes to protecting the sensor lenses from oil fouling during operating in the dynamic sea after 

conditions. Based on that and since the frequency response at 10 cm was considered acceptable also, 

the 10 cm was selected to be used in final sensor design with a configuration of 10x and 10 scaling 

factors.

Diesel Experiment 4 (10 cm - sensitivity (x10) - scaling (10))

The same experimental setup was used to evaluate the sensor performance in diesel oil instead of 

lubricating oil. The distance between the light-emitter and the light-to-frequency converter modules 

was set to 10cm. The oil thickness was added gradually through three stages, where in the first stage,

the thickness was increased from 0μm to 500μm by increments of 100μm. After reaching 500μm, the

thickness was doubled to reach 100μm. In the last stage, the thickness was increased from 1000μm to 

5000μm with increments of 1000μm. Table 20 shows the experimental results including the sensor 

frequency response at each thickness level, and Figure 67 shows the corresponding graph.

0 1000 2000 3000 4000 5000 6000 7000

Range

Average

Average slope

Distances

10 cm 6.5 cm
Page 67

Table 20: LED-based - Experimental Results - Distance tuning experiment 4

Thickness (μm) Frequency (Hz)

0 6067.41

100 5931.47

200 5826.41

300 5741.53

400 5594.78

500 5402.47

1000 5222.15

2000 5138.92

3000 5044.20

4000 4945.12

5000 4862.07

Figure 67: LED-based Sensor Tuning – Experiment 4 (Graph)

The experimental results of the diesel experiment showed a decrease in the output frequency with the 

increase in oil thickness. It is important to note here that a major change in the curve slope was observed 

past the thickness of 1000μm.

5.2 Oil-Thickness Calibration and Measurement

The aim of this experiment is to evaluate the performance of the sensor after performing calibration, 

which is done by recording the output frequency of the sensor against several oil thicknesses, and a 

piecewise linear fit to the data was performed, with the first equation between 0μm thickness and 

1000μm thickness, and the second between 1000μm thickness and at 3000μm thicknesses.

To measure the oil thickness based on the calibration equations, oil was removed from the experimental 

setup and new amount of tap water was filled. Lubricating oil (10cSt) was added gradually to the 

graduated container representing several oil thicknesses ranging from 100μm to 3500μm. The 

sensitivity factor of the light-to-frequency converter module was set to 10x and the scaling factor to 10. 

The distance between the upper and lower parts of the experimental setup was set to 6.5cm. The 

4500.00

4700.00

4900.00

5100.00

5300.00

5500.00

5700.00

5900.00

6100.00

6300.00

0 1000 2000 3000 4000 5000 6000

Output Frequency (Hz)

Oil Thickness (μm)
Page 68

experimental results showing the measured oil thicknesses with respect to the actual thicknesses are 

shown in Table 21.

Table 21: Results of thickness measurement experiment - LED-based sensor

Actual oil thickness (μm) Measured oil thickness (μm) Absolute Error (μm) Percent Error (%)

100 107.75 7.74 7.74

300 291.19 8.81 2.93

500 522.03 22.03 4.40

800 676.17 123.83 15.47

1000 1042.48 42.48 4.24

2000 2102.34 102.33 5.11

3500 3530.60 30.60 0.87

Based on the experimental results, it can be concluded that the oil thickness measurement using the 

current configuration settings were reliable with a maximum error of 5.1% for the values above 1000um 

oil thickness, and 15.5% for the values below 1000um oil thickness.

5.3 Bouy Design

A floating buoy (Figure 68) was manufactured for the LED-based sensor. Based on the critical 

dimensions identified in the tuning experiments, the distance between the light-emitter module and the 

light sensor module was set to 10cm. Mainly, the buoy structure is composed of 3 rods parallel to each 

other, each one holding several floaters. The rods are joined with three separate platforms. A triad of 

floaters was chosen for the stability of the design, waterproof light case and box were chosen for the 

ease of deployment and resistance to salt water wear. The buoy has an enclosing volume of a 40cm 

diameter cylinder with a 35cm height. The buoyancy can be increased and decreased depending on 

necessity and weight by adding and removing secondary foam floaters around the exposed parts of the 

threaded shafts. A blue LED is placed inside the light holder as a separate unit.

Figure 68: Spectro-based sensor - Implemented Buoy

Primary floaters

Light Sensor and Control 

Unit

Secondary Floaters

Light Source (Blue LED)
Page 69

5.4 Bouy Experiments

After designing the final sensor buoy, several experiments were performed to assess the performance 

of the sensor in floating conditions. The sensor was embedded into the buoy structure and a 100-Liters 

tank filled with seawater was used in the experiments. During the experiments, controlled amounts of 

lubricating light-oil with a viscosity of 10cSt was added to the container to form a set of different 

thicknesses. The experimental setup with the buoy installed is shown in Figure 69. 

Figure 69: LED-based Buoy - Experimental Setup

Table 22: Bouy Experiment - Frequency response vs. Oil Thickness

Thickness (um) Average Frequency (Hz) Standard deviation

0 4910.08 15.56626

200 4847.60 217.1475

400 4758.28 105.6207

600 4703.27 19.51385

800 4540.35 26.51144

1000 4471.62 32.18685

Figure 70: Buoy Experiment - Frequency response vs. Oil Thickness (Graph)

4400

4450

4500

4550

4600

4650

4700

4750

4800

4850

4900

4950

0 200 400 600 800 1000 1200

Output Frequency (Hz)

Oil Thickness (μm)
Page 70

The experimental results showed an almost linear decrease of the frequency response with the increase 

of oil thickness (Figure 70). However, in this experiment, high standard deviation values were recorded 

(Table 22). Then, a set of experiments were performed to assess the effect of the water level and waves 

on the stability of the frequency response. The new experiments were done using several scenarios 

listed as the following:

- Fixed buoy (no waves)

- Changing the water level while the buoy is fixed

- Fixed buoy subjected to waves

- Floating buoy (no waves)

- Floating buoy subjected to waves

The same experimental procedure was repeated with two different oil thicknesses; 500μm and 1000μm.

The output frequencies recorded from the two experiments are shown in Table 23, and Table 24

respectively.

Table 23: Floating Buoy Exp. 1 (500 μm)

Average Frequency (Hz) Standard deviation Condition

10247.7 24.46433 4 cm water level above led

10508.59 41.98751 7 cm water level above led

10377.53 426.8029 7 cm water level + waves

9947.77 65.60328 Floating buoy (3 cm water level)

9869.097 593.6416 Floating + waves

Table 24: Floating Buoy Exp. 2 (1000μm)

Average Frequency (Hz) Standard deviation Condition

10708.91 26.81183 4 cm water level above led

12033.64 67.09064 7 cm water level above led

11698.08 1146.598 7 cm water level + waves

10577.9 120.4646 Floating buoy (3 cm water level)

10283.84 476.4398 Floating + waves

It was observed that once waves are introduced, the standard deviation was highly increasing because 

of high-frequency fluctuations dominating the measured values. However, since the average frequency 

obtained at the same thickness level were almost similar with and without waves, the result was 

considered acceptable, and work was focused on decreasing the fluctuations algorithmically.
Page 71

Chapter 6- Capacitive Sensor Design Evaluation and Refinement

This chapter describes the experimental work done to evaluate the performance of the preliminary 

capacitive sensor design under different conditions. After analyzing the results, design refinements were 

applied to the sensor layout, and the final design was implemented and tested.

6.1 Performance Testing 

To evaluate the performance of the preliminary sensor design, three sensor boards with similar 

dimensions (Figure 71) were implemented and tested.

Figure 71: Sensor prototypes for repeatability testing

The three sensor boards use the D-37 male connector (shown at the top of each sensor) to connect to 

the controller circuit. To compare the three boards, raw voltage values were taken from each sensor in 

three cases: air, oil, and water. In each sensor board, 36 strips were activated, and a set of voltage 

measurements were recorded for each strip during the experiment.

First, the comparison of the sensors in the air medium shows that the baseline values of the three boards 

(A = 151, B = 154, C=149) are similar. Note that the baseline values of the sensor boards depend not 

only on the dimensions of the sensor board but also on the actual hardware implementation and finishing 

aspects in terms of soldering and copper thickness, which add to the base capacitance of the electrodes. 

Second, the three sensors were totally immersed in water to compare the measured voltages. In the case

of immersing the board in water, a large attenuation of the voltage values of the three boards was 

observed. The results of this experiment showed a similar response of the three sensors while in water. 

With similar average values (A=2.2, B=2.3, and C=2.11), the three sensors are demonstrated to be 

behaving similarly.
Page 72

Figure 72: Capacitive Sensor Performance Experiment - Oil Case

To analyze the results in the case where the sensor is immersed in oil, the relative differences from the 

calibration values are calculated (Figure 72). The relative differences are divided into two categories:

the first category contains the electrodes located in the air (E1 to E4), and the second category contains

the electrodes immersed in oil (E5 – E36). It was noted that the relative difference of each electrode 

was increasing with the electrode index, since the connection tracks mounted on the bottom layer of the 

PCB have more length in oil when the electrode is lower. We also noted that one electrode in sensor A 

(E25) did not follow the expected pattern (i.e., a decrease) with its relative difference less than 4% in 

oil. This problem was more obvious in sensor B with several electrodes (E5, E6, E7, E8, and E11) 

behaving similarly to E25 in sensor A. The main difference between these electrodes and the other 

electrodes is that the relative difference of their values is very small, ranging from 0.1 % to 1.28% when

the sensor is immersed in oil. While interpreting the experimental results, the electric connection

between all the sensor components (sensing electrodes, connection tracks, male D37 connector, female 

D37 connector, and MPR121 controllers) was tested. A major problem was observed in the connector 

part (D37), where we noticed that a number of the connector pins were electrically not connected. 

Despite the fact that this problem was not observed initially during the preliminary experiments, it was 

demonstrated that it may occur after several iterations of sensor replacement (mounting/dismounting) 

due to connector bending and stress.

Based on the experimental results, and to avoid possible electrical connectivity problems, we decided 

to completely remove the connector from the design, and mount the capacitive controllers on the sensor 

board, instead of the controller board. By doing so, the size of the controller board size significantly 

decreased. The major advantage of this change is that capacitance measurements are done on the sensor 

boards without having any connector between the measurement channel and the sensing strip. This 

enhancement increases the sensitivity and the robustness of the sensor design. By removing the 

connector, we also avoid the issue of mechanical variations, connector bending, and aging of soldering.

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536

RD (%)

Electrode Index

Sensor A Sensor B Sensor C
Page 73

6.2 Evaluating the Impact of Different Power Sources on the Sensor Performance

During the initial testing of the sensor, two main sources were used to power the circuit. First, the 

control circuit was powered using a USB-port on a laptop. The USB-port was used to power the Arduino 

module, and to retrieve the measured voltages from the MPR121 modules. The USB-port provides the 

Arduino with 5VDC, and the Arduino provides 3.3VDC to the MPR121 controllers through the 3.3V 

embedded voltage regulator. During operation, the laptop was connected to the power adapter, plugged 

into the AC power lines (220AC). The second source used to power the sensor was a lead-acid battery 

of 12VDC. To assess the effect of the two power sources on the voltages measured by the capacitive 

stripes, the sensor was placed at a fixed position, and a set of voltages measured by all electrodes were 

recorded. The sensor was set at a fixed position (surrounded by air), and a sample of 250 measures was 

recorded. The voltages measured by the first twelve electrodes are analyzed and presented in Table 25. 

Also, a graph showing voltages measured by the first electrode E1 in the two cases, is shown in Figure 

73.

Table 25: Voltage (ADC) - Battery vs. USB-port Power Sources

Electrode Mean Standard 

Deviation Max Min Range

Battery 

Powered 

E1 167.00 0.00 167 167 0

E2 157.00 0.00 157 157 0

E3 156.00 0.00 156 156 0

E4 159.00 0.00 159 159 0

E5 158.07 0.26 159 158 1

E6 161.00 0.00 161 161 0

E7 160.00 0.00 160 160 0

E8 166.00 0.00 166 166 0

E9 159.00 0.00 159 159 0

E10 158.00 0.00 158 158 0

E11 149.00 0.00 149 149 0

E12 153.00 0.00 153 153 0

USB-port 

Powered

E1 167.43 0.50 168 167 1

E2 157.02 0.15 158 157 1

E3 156.00 0.06 157 156 1

E4 159.04 0.19 160 159 1

E5 158.58 0.49 159 158 1

E6 161.00 0.06 162 161 1

E7 159.50 0.50 160 159 1

E8 162.05 0.22 163 162 1

E9 159.00 0.00 159 159 0

E10 158.00 0.06 159 158 1

E11 148.70 0.46 149 148 1

E12 153.00 0.06 154 153 1
Page 74

Figure 73: Voltages measured by E1 in Battery-powered and USB-powered trials

This experiment showed that using the USB-port as a power source, introduced additional noise to the 

measured voltage signals. This can be inferred from the increased standard deviation values shown in 

Table 25, and the high frequency oscillations introduced to the E1 voltage signal shown in Figure 73. 

Based on the experimental results, it was concluded that connecting the device to an AC power source

is not recommended, to avoid inserting additional noise to the sensor measurement. 

6.3 Sensor Design Refinement 

As an enhancement for the preliminary sensor design, and to ensure a reliable connection between the 

sensor electrodes and the connection tracks, the refined design uses plugged vias embedded in a 

multilayer PCB, instead of using the exposed vias (double layer). The new board is composed of a 4-

layer PCB, with sensor pads mounted on the top and bottom layers of the PCB, and the connection 

tracks are mounted in the two inner layers of the PCB. In this case, the connection tracks are completely 

isolated from the external material and sensing is done only by the horizontal pads. To drive power and 

ensure data connections to the microcontroller, the sensor board is connected to the controller board 

through a 4-pin connector (wire). To extend the range of the sensor, an additional MPR121 device was 

added to cover twelve new electrodes added to the sensing unit. The main advantage of this design is 

that the controller board and the sensor board become separated, connected to each other using only 

four wires. The updated capacitive sensor schematic is shown in Figure 74. The schematic shows the 

connection between four MPR121 controllers and the sensing pads numbered from 1 to 48. Addressing 

of the MPR121 is done using the ADD pin of the controllers. By connecting the address pin to GND, 

3.3V, SDA, SCL the four controllers have the following I2C addresses respectively 0x5A, 0x5B, 0x5C, 

and 0x5D.

In summary, the main change regarding the sensing unit is that it includes forty-eight electrodes instead 

of thirty-six, the four MPR121 controllers are added to the sensing unit instead of the processing unit, 

166.4

166.6

166.8

167

167.2

167.4

167.6

167.8

168

0.10.91.72.53.34.14.95.76.57.38.18.99.7

10.511.312.112.913.714.515.316.116.917.718.519.320.120.921.722.523.324.124.925.726.527.328.1

Voltage (ADC)

Time (sec)

E1 /Battery (ADC) E1/USB-Port (ADC)
Page 75

the tracks are buried inside the inner layers of a 4-layer PCB design, and the electrodes are implemented 

on the two layers of the PCB (bottom, top).

Figure 74: Capacitance Measurement Schematic - Final Design

The final version of the processing unit included a low-voltage drop regulator (LM2940), in addition to 

two capacitors for signal smoothing (0.47uF and 22uF). In addition, several components were added to 

the circuit, such as heat sinks for regulators and power jacks (3.5mm) to connect the battery packs. 

Three boards were implemented and tested. Figure 75 shows a sample of the implemented boards.

Figure 75: Processing and Communication Unit

Communication and 

power pins connected to 

the sensing unit

GPS Module (SKM53)

Arduino Nano

Wireless Communication Module

Input Voltage Regulator
Page 76

6.4 Optimized Sensor Prototype

The final sensor design was prepared as a 4-layer PCB board (Figure 76); where connection tracks are 

embedded in two inner layers and sensing strips are placed on the surface layers. After adding an 

additional MPR121 controller to the sensor board, the number of sensing strips was increased from 36 

to 48. Considering all the past experiments and design recommendations, and in order to enhance the 

sensitivity of the unit, increasing the width of the sensing pads with respect to the gap between the 

electrodes is recommended. Although the sensor measurement range is 10cm, we increased the 

measurement range to 14.2cm in order to have additional margins in case of maximum thickness 

scenario and/or wave conditions. The distance between the first sensing pad and the end of the last 

controller module is 3.28cm, this gap only includes hidden tracks to allow for the packaging to overlap 

the board and to lower the sensing stripes slightly below the package edge. The width of the sensor 

board is 60.0mm. The total length of the sensor board was increased to 25cm. As the capacitive 

controllers are embedded inside the implemented package, they will be fully isolated from water and 

oil. The sensing strips will be immersed in the measured mixture. The microcontroller provides the 

sensor with the power and Ground lines and gets the measured thickness through the data lines of a 4-

pin wire. Five items from the sensor design with identical dimensions were ordered from “PCBWay” 

company in China (https://www.pcbway.com/).
Page 77

Figure 76: Optimized Sensor Prototype

Two Capacitive Touch 

Controllers (MPR121) 

mounted on the top layer

Communication and Power pins

Two Capacitive Touch 

Controllers (MPR121) mounted 

on the bottom layer

4-Layer PCB

Electrodes

Connection tracks embedded 

inside the inner layers
Page 78

6.5 Capacitive Sensor Tuning Experiments

As described before, the MPR121 controllers are used to measure the capacitance of each electrode in 

the sensing unit. Since the MPR121 modules use a DC charge technique to measure the capacitance, 

the current charge and the charge duration parameters need to be configured before operation. Based 

on this, tuning experiments were performed to find the best combination of the charge current and 

duration to be used in the final sensor design. The main aim of the tuning process is to select the 

combination (current/time) that maximizes the sensor sensitivity in terms of differentiation between air 

and oil. The voltage measurements used in the tuning experiments are taken at the output of the second

filter of the MPR121 controller. The third filter output of the MPR121 controller is ignored in this 

project because it compares the raw voltage measurements with the baseline values used mainly for 

capacitive touch applications. The charge current may take a value between 1μA and 63μA, and the 

charge duration is set to value between 0.5μsec and 32μsec. During the experiments in which tuning 

was performed, the sensor was fixed on the indoor tank and immersed in a liquid that contains a layer 

of heavy oil (Fuel) with 20mm thickness (see Figure 77).

Figure 77: Experimental Installation - Tuning Experiments

Current Tuning Experiments

Current tuning was performed by setting the charge duration time to a fixed value of 0.5μsec and 

changing the charging current amount from 1μA to 63μA by an increment of 1μA. The experimental 

results are shown in Table 26 and the corresponding graph is shown in Figure 78. The vertical margins 

in Figure 78 indicate the interfaces between different groups of electrodes, distributed as follows:

 E1 to E25: Air

 E26: Air/Oil

 E27 to E33: Oil

 E34 to E40: Water, covered by a thin layer of oil (fouling effect)

 E41 to 48: Water.
Page 79

Table 26: Current tuning experiment (Voltage (ADC) vs. Current(1-62μA))

Current (μA)

Electrode 1 8 16 24 32 40 48 54 62

E1 4 52 105 159 212 265 319 358 408

Voltage (ADC)

E2 4 49 100 151 201 252 304 340 389

E3 4 50 100 152 202 253 305 342 390

E4 4 50 101 152 203 254 306 343 391

E5 4 50 101 152 202 254 305 342 390

E6 4 49 101 151 202 253 303 341 389

E7 4 50 101 152 202 253 304 342 391

E8 4 50 101 153 203 254 305 343 392

E9 4 49 100 152 202 253 303 341 390

E10 4 49 99 150 200 250 298 338 386

E11 4 48 99 149 198 248 298 335 382

E12 4 49 99 150 200 250 300 337 385

E13 4 49 99 150 200 251 301 339 387

E14 4 47 96 145 194 243 292 328 375

E15 4 47 96 146 194 242 292 329 377

E16 4 47 97 146 195 244 293 330 378

E17 4 48 98 148 197 246 296 333 381

E18 4 48 97 147 196 245 295 331 379

E19 4 48 97 147 195 244 294 331 378

E20 4 48 97 147 196 245 295 331 379

E21 4 48 98 147 196 246 295 332 379

E22 4 48 97 147 195 245 296 331 378

E23 4 47 96 145 194 242 292 328 375

E24 4 48 97 147 195 245 293 331 378

E25 4 49 100 151 201 253 306 341 390

E26 4 48 98 147 196 246 294 332 381

E27 4 47 96 144 192 241 289 326 373

E28 4 47 95 145 192 240 288 324 372

E29 4 47 95 143 191 239 288 323 371

E30 4 46 95 143 191 238 288 322 370

E31 4 46 95 143 191 239 285 322 369

E32 4 46 94 143 190 238 285 321 368

E33 4 46 93 141 187 235 281 316 362

E34 4 43 88 132 175 220 263 295 338

E35 3 41 83 126 167 208 250 280 321

E36 4 42 85 127 169 211 253 284 326

E37 3 37 75 112 150 187 224 251 290

E38 3 35 70 106 140 175 209 234 270

E39 2 32 65 98 129 161 191 215 247

E40 2 25 50 74 99 122 145 162 185

E41 0 6 14 21 27 34 40 45 51

E42 0 5 11 17 22 28 33 38 43

E43 0 2 6 10 13 17 20 23 27

E44 0 3 7 12 16 20 25 28 32

E45 0 2 5 8 11 14 17 20 23

E46 0 1 4 7 9 12 14 16 19

E47 0 1 3 6 8 10 13 14 17

E48 0 1 3 5 7 9 11 13 15
Page 80

Figure 78: Current tuning – Graph (Voltage(ADC) vs. Current(μA))

Based on the tuning results, it was observed that the difference between the air and oil electrodes 

increase with the current in a directly proportional manner. Theoretically, this result is expected since 

the capacitance is directly related to the product of duration and current. However, to avoid the 

saturation of the 10-bit ADC contained in the MPR121, the suitable time duration parameter need to be 

assigned considering additional tuning experiments.

Time Tuning Experiments

In contrast to the current tuning experiments, in the time tuning experiments the current amount was set 

to a fixed value of 1μA and charging duration time was changed gradually from 0.5μsec to 32μsec. To 

do this, the CDT register included in the MPR121 controller was set with different values in order to 

set the needed charge time values (see Table 27). The state of the sensor electrodes is similar to the one

presented in current tuning experiment section above. The experimental results are shown in Table 28

and the corresponding graph is shown in Figure 79.

Table 27: Hex values representing different charge time configurations

Charge Time (μs) Hex Value

0.5 0x20

1 0x40

2 0x60

4 0x80

0

50

100

150

200

250

300

350

400

450

E1 E3 E5 E7 E9 E11 E13 E15 E17 E19 E21 E23 E25 E27 E29 E31 E33 E35 E37 E39 E41 E43 E45 E47

Voltage (ADC)

Electrode Index

Charge Current (μA)

1 8 16 24 32 40 48 54 62
Page 81

8 0xA0

16 0xC0

32 0xE0

Table 28: Time tuning experiment (Voltage(ADC) vs. Time (0.5-32 μs))

Time (μs)

Electrode 0.5 1 2 4 8 16 32

E1 4 11 24 49 98 196 386

Voltage (ADC)

E2 4 10 22 47 93 186 368

E3 4 11 23 46 93 187 372

E4 4 11 23 47 94 188 373

E5 4 11 23 47 93 187 370

E6 4 11 23 47 93 186 368

E7 4 10 23 47 93 186 371

E8 4 11 23 47 94 189 373

E9 4 10 23 47 94 188 371

E10 4 10 22 47 92 184 366

E11 4 10 22 46 91 182 364

E12 5 11 23 47 93 185 366

E13 4 10 22 54 98 190 374

E14 4 10 22 45 92 183 364

E15 4 10 22 46 91 184 365

E16 4 10 22 46 92 184 368

E17 4 10 22 46 93 187 369

E18 4 10 22 46 93 186 368

E19 4 10 22 46 92 185 368

E20 4 10 22 46 93 186 368

E21 4 10 22 46 93 186 371

E22 4 10 22 45 92 186 369

E23 4 10 22 45 92 184 365

E24 4 10 23 46 93 185 368

E25 4 10 23 47 96 191 378

E26 4 10 22 46 93 185 371

E27 4 10 22 44 90 180 359

E28 4 10 21 45 90 180 357

E29 4 10 21 44 89 179 359

E30 4 10 21 44 90 179 356

E31 4 10 21 44 89 180 354

E32 4 10 21 43 89 178 354

E33 4 10 21 43 86 172 346

E34 2 6 14 29 59 119 237

E35 2 4 10 22 44 87 169

E36 2 5 12 25 51 102 202

E37 1 4 9 20 41 82 163

E38 1 4 9 19 39 78 153

E39 1 3 8 17 34 70 139

E40 0 0 1 2 3 5 9

E41 0 0 0 0 1 2 3

E42 0 0 0 0 1 2 4

E43 0 0 0 0 0 1 2

E44 0 0 0 0 0 1 2

E45 0 0 0 0 0 0 1
Page 82

E46 0 0 0 0 0 0 0

E47 0 0 0 0 0 0 0

E48 0 0 0 0 0 0 0

Figure 79: Time tuning - Graph (Voltage (ADC) vs. Time(0.5-32 μs))

The experimental results revealed an increase in the measured voltage in a directly proportional manner 

with time.

Combined Tuning Experiments

Based on the results of current and time tuning experiments, and to select the best combination of

current and time in terms of sensitivity, two electrodes were selected as references from air and oil 

mediums. The absolute difference between the voltages measured by each of the two electrodes (E25 –

E27) was used as the sensitivity factor to be monitored while testing all possible combinations. The 

difference between the two electrodes (delta) is calculated for all possible combinations and shown in

Table 29 and the corresponding graph is shown in Figure 80.

Table 29: Combined tuning - Numerical results

Time (μsec) 

Current (μA) 0.5 1 2 4 8 16 32

1 0.00 0.00 1.00 3.00 4.00 6.00 14.00

2 0.67 1.00 2.00 3.33 8.33 16.00 18.67

3 0.67 1.67 2.67 5.00 13.33 23.67 14.33

4 0.67 2.00 3.67 7.67 16.67 27.00 3.33

5 0.67 2.33 4.67 10.33 20.00 20.00 0.00

0

50

100

150

200

250

300

350

400

450

E1 E3 E5 E7 E9 E11 E13 E15 E17 E19 E21 E23 E25 E27 E29 E31 E33 E35 E37 E39 E41 E43 E45 E47

Voltage ADC

Electrode

Charging Time (μs)

0.5 1 2 4 8 16 32
Page 836 1.33 2.33 5.67 12.00 22.33 10.67

-0.33

7 1.67 2.33 7.00 9.33 26.67 3.00 0.00 8 2.00 2.33 7.33 11.33 28.33 1.00 0.00 9 2.00 3.33 8.00 12.00 30.00 0.67 0.00

10 2.33 4.00 7.00 16.33 24.67 0.33 0.00

11 2.67 5.33 10.33 15.67 18.330.33 0.33

12 3.00 5.67 11.33 18.33 8.33 0.00 0.33

13 3.00 6.67 13.33 19.67 3.00 0.00 0.00

14 3.33 6.33 14.00 22.33 0.33-0.33 0.00

15 3.67 8.67 14.67 27.33 0.00-0.33 0.33

16 4.00 9.00 15.33 30.00 0.00 0.00 0.00

17 4.00 10.00 14.33 33.67-0.67 0.00 0.00

18 4.00 8.67 15.67 29.33-0.33 0.00-0.33

19 4.33 9.33 15.00 34.00-0.33-0.33-0.33

20 4.67 9.67 16.00 31.33-0.33-0.33-0.33

21 5.00 10.33 16.67 31.33-0.67 0.33-0.33

22 5.00 11.33 17.00 21.00 0.00 0.33 0.00

23 6.00 12.00 18.00 14.00 0.67 0.33 0.00

24 6.33 14.33 16.67 7.00 0.67 0.00-0.33

25 6.33 14.00 25.00 4.67 0.33-0.67-0.33

26 5.67 13.67 26.67 3.33 0.00-0.67-0.33

27 5.67 12.33 29.67 2.67 0.33-0.67 0.00

28 6.00 13.33 25.33 1.00 0.67 0.00 0.00

29 6.33 13.67 25.00 0.00 0.33 0.33 0.33

30 7.00 14.00 26.67 0.33 0.00 0.33 0.33

31 7.67 14.33 25.67 0.67 0.00 0.67 0.67

32 8.00 15.67 26.00 0.33 0.33 0.33 0.33

33 8.33 16.33 26.33-0.67 0.33 0.00 0.00

34 8.67 16.33 27.00 0.00-0.33-0.33-0.33

35 8.67 16.33 28.33 0.33-0.67-0.33 0.00

36 8.67 16.33 27.67 0.33-1.00 0.00 0.67

37 8.67 16.33 30.00 0.33-0.67 0.00 1.00

38 9.33 16.00 31.00 0.67 0.00 0.33 0.67

39 10.33 16.33 31.00 0.00 0.33 0.00 0.33

40 10.67 17.00 30.00-0.67 0.33 0.33 0.00

41 11.00 18.33 29.00-0.67 0.00 0.00-0.33

42 10.67 17.67 27.00 0.33 0.00 0.33-0.67

43 11.00 18.00 25.00 0.00-0.33 0.00-0.67

44 11.33 17.33 19.33-0.33-0.33 0.00 0.00

45 11.67 19.00 16.00 0.00-0.67 0.00 0.00

46 12.00 19.33 12.67 0.33-0.33-0.33 0.00

47 11.67 20.00 11.00 1.00-0.33-0.33-0.33

48 12.00 20.00 9.00 0.00 0.00-0.67 0.00

49 12.33 21.33 7.00-0.67 0.00-0.67-0.33

50 13.33 21.33 5.67-1.00-0.33-1.00-0.33

51 13.00 21.67 5.33-0.67-0.33-0.67-0.67

52 12.33 20.67 4.33 0.33-0.33 0.00-0.33

53 12.33 20.67 3.33 0.67 0.00 0.33-0.33

54 13.00 21.67 2.33 1.33 0.00 0.67 0.00

55 14.00 23.67 2.00 0.33 0.00 0.33 0.33

56 14.00 29.00 1.67 0.33 0.00 0.33 0.33

57 14.00 29.67 1.67-0.33 0.00 0.33 0.33
Page 84

58 14.33 28.67 1.33 0.33 0.00 0.67 -0.33

59 14.67 25.00 1.00 0.33 0.33 1.00 -0.33

60 14.33 25.00 0.67 0.33 0.67 0.33 -0.67

61 14.67 25.00 0.33 0.33 0.67 0.00 -0.67

62 15.00 26.33 1.00 0.00 0.67 -0.33 -1.00

63 16.00 28.00 2.00 0.00 1.00 0.00 -1.00

Figure 80: Combined Tuning Graph (delta vs time and current)

Based on the experimental results for the tuning, the combination of 2μsec charge duration time and 

32μA of charge current amount was selected since it admitted the maximum difference factor between 

oil and air while attaining an acceptable power consumption and relatively high sampling rate before 

reaching the saturation level.

6.6 Capacitive Sensor Enhanced Multi-Layer Design with Pins

Based on all the past observations while working with the different sensor prototypes, we noticed that 

the major fouling effect is occurring while the sensor is moving down into the liquid. During the 

downward movement, a set of electrodes are entering the water carrying a thin layer of oil acting as a 

separator between the electrodes and water. Without having a sufficient amount of time to clean the oil 

layer from the sensor strips, the oil layer sticks to the sensor strips and highly affects the voltage 

measurements of the electrodes. In air, this problem is reduced since oil is pulled by gravity and tension 

with the surface oil downward. The vertical motion of the sensor was recorded several times to monitor 

the fouling effect. A snapshot from a video recording of the sensor vertical motion showing the fouling 

effect in water is shown in Figure 81. Note that this effect is minimal while working with low vicious 

oils.

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

Air Electrode (E25) 

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63

- Oil Electrode (E27) - ADC

Current (μA)

Charge Time (μs)

0.50 1.00 2.00 4.00 8.00 16.00 32.00
Page 85

Figure 81: Photo showing oil-fouled electrodes

To reduce the fouling effect on the sensor, and to increase sensor sensitivity, a novel solution was 

developed, which consisted of adding pins normal to the plane of strips of the sensing array. In addition, 

sensing electrodes were added to the two sides of the sensor and the connection tracks were embedded 

in a 4-layer PCB design. The added pins play a major role in penetrating the thick oil layer covering the 

sensor strips. While the sensor is immersed in the examined liquid containing oil and water layers, the 

pins located in the water section of the sensor are short-circuited due to water conductivity. The upper 

pins located in the oil section are totally immersed in oil and thus the measured capacitances are related 

to the dielectric constant of the oil, which is different from that of water and air. Based on this enhanced 

design, the actual sensing method used by the sensor can be thought of as a mixture between capacitive 

sensing and conductive sensing techniques: short-circuiting is done in the water partition of the sensor,

and capacitive sensing happens in the air and oil sections of the sensor due to their insulating properties. 

Figure 82 illustrates the enhanced sensor design showing the following parts: (a): Sensing electrodes,

(b): Connection tracks, (c): Anti-fouling pins, and (d): PCB. The implemented sensor prototype with 

pins is shown in Figure 83.

Oil-Fouled region: 

Electrodes soiled by 

oil due to vertical 

sensor movement.
Page 86

Figure 82: Enhanced sensor design with pins

Figure 83: Enhanced sensor design with pins

To assess the performance of the added pins, two sensors with/without pins were tested. To compare 

the rate of change of the electrodes’ measures in the two boards, the average voltage measured by the 

last three electrodes in each of the two sensors were recorded and plotted (Figure 84).

Water (Ԑr= 80)

Air (Ԑr= 1)

Oil (Ԑr= 2.1)

(d)

(a)

(c) (b)

Pins

Electrodes
Page 87

Figure 84: Pins Experiment (Voltage(ADC) vs. Time (sec))

The experimental results shown in Figure 84, show clearly that the voltage measured by sensor with 

installed pins made a severe drop in the voltage after being immersed in water. This is because the 

horizontal pins penetrated the oil-fouling layer covering the sensor body, as shown in Figure 85. In 

contrast, the voltage measured by the second sensor (exposed – no pins), showed a much slower voltage 

drop. This experiment demonstrated the ability of the horizontal pins to decrease the oil-fouling effect 

on the sensor performance by increasing the rate of change of the voltage measured by different 

electrodes when moving between oil and water mediums.

Figure 85: Photo showing pins penetrating the oil-fouling layer

0

100

200

300

400

500

600

700

800

0.12.85.58.2

10.913.616.3

19

21.724.427.129.832.535.237.940.643.3

46

48.751.454.156.859.562.264.967.670.3

73

75.778.481.183.886.589.2

Voltage (ADC)

Time (sec)

Pins Experiment (Voltage(ADC) vs. Time(sec))

With Pins Without Pins

Pins penetrating the 

oil-fouling layer
Page 88

Based on the experimental results, it was concluded that the added pins play a major role in penetrating 

the thick oil layer covering the sensor strips. While the sensor is immersed in the examined liquid 

containing oil and water layers, the pins located in the water section of the sensor are short-circuited

due to water conductivity. The upper pins located in the oil section are totally immersed in oil and thus 

the measured capacitances are related to the dielectric constant of the oil, which is different from that 

of water and air. Based on this enhanced design, the actual sensing method used by the sensor can be

thought of as a mixture between capacitive sensing and conductive sensing techniques: short-circuiting

is done in the water partition of the sensor, and capacitive sensing happens in the air and oil sections of 

the sensor due to their insulating properties.

6.7 Additional Attempts to Reduce the Oil-fouling Effect

Adding High-Frequency Vibration Mechanism to the Sensor

Although the initial testing phase demonstrated the ability of the capacitive sensor in determining the 

medium it is in, one problem was observed with the oil soiling of the electrodes in the water under the 

oil slick level, while the sensor bobs up and down. Although the oil eventually slides off these strips 

and rises to the surface, the dynamics of the process is relatively slow for thick oils and risks affecting 

the determination of the oil/water interface. To deal with this issue, the initial sensor prototype was 

equipped with a vibrating module (Figure 86), to help expedite the removal of oil from fouled strips. 

To assess the effect of the vibrator module, two experiments were performed; before and after activating 

the vibrators. The same procedure was used in performing the two experiments. The sensor was 

immersed vertically from a fixed position into the water through a thin floating oil-layer. The voltage 

measured by the capacitive stripes was recorded. To interpret the experimental results, the voltage-drop 

of two stripes passing from the initial position (Air) to the end position (Water), while penetrating the 

oil layer, were recorded and compared. An instance of the experimental results is shown in Table 30, 

and the corresponding graphs, displaying the voltage drop of the two stripes with respect to time, are 

shown in Figure 87, and Figure 88.

Figure 86: Capacitive sensor with vibration mechanism
Page 89

Table 30: Vibrator Experiment (Voltage (ADC) vs. Time (sec))

Time (sec) E1 (ADC) E1 with vibrator (ADC) E2 (ADC) E2 with vibrator (ADC)

1 144 144 161 162

2 136 135 148 148

3 133 137 140 142

4 127 111 132 111

5 132 89 137 54

6 118 76 122 65

7 108 76 88 49

8 90 64 86 40

9 81 65 74 46

10 80 59 66 39

11 76 58 72 35

12 73 54 64 41

13 68 54 58 39

14 69 50 56 36

15 71 49 60 37

16 66 47 58 29

17 62 42 50 33

18 59 44 52 31

19 58 41 54 33

20 59 37 52 30

21 60 38 51 33

22 57 36 49 30

23 54 36 48 28

24 55 33 48 30

25 51 32 50 27

26 51 33 49 27

27 53 33 50 31

28 52 34 50 25

29 50 34 47 27

30 48 1 46 26

Figure 87: E1 voltage-drop, before & after installing the vibrator (Voltage (ADC) vs. Time (sec))

0

20

40

60

80

100

120

140

160

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53

Voltage (ADC)

Time (sec)

E1-Without Vibrator E1-Vibrator
Page 90

Figure 88: E2 voltage drop before & after installing the vibrator (Voltage (ADC) vs. Time (sec)

The experimental results revealed that the use of vibrators helped increase the rate of the voltage drop

of the two electrodes. This result supports the idea of using a vibration system, since speeding up the 

voltage drop is essential in decreasing the oil-fouling effect.

Preliminary Work Done on Coating with Nanoprotech Material

In addition to the proposed vibrator above, several coating materials were tested. First, the initial sensor 

prototype was coated with a transparent electrical insulation material provided by Nanoprotech

composed of the following ingredients: highly purified mineral oil, anticorrosion additives, antioxidant 

paraffinic, and naphthenic hydrocarbons. The recommended temperature for operation of this material 

is between -20oC to +35oC and it maintains its properties for a temperature range from -80oC to +140oC. 

Spraying the sensor cartridge with the Nanoprotech material (Figure 89) is done manually, and the 

board is kept to dry in air for around twenty-four hours before the first use. The excess of the spraying 

process (liquid residue) remaining on the sensor’s body is removed, after the drying process is 

completed. The material is completely transparent, and covers all the components of the sensor 

including, the pins, stripes, and connection tracks.

To compare the sensor performance before and after applying the Nanoprotech coat, two identical 

experiments were performed. In the two experiments, the sensor was immersed into the oil/water 

mixture starting from a fixed position above the water surface. To monitor the effect of the material on 

the oil-fouling process, the voltage-drop of the last two electrodes (E35, and E36), while passing from 

the air layer until reaching the water layer, were recorded and analyzed. The experimental results of the 

immersing experiment are shown in Figure 90.

0

20

40

60

80

100

120

140

160

180

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53

Volatge (ADC)

Time (sec)

E2- Without Vibrator E2-Vibrator
Page 91

Figure 89: Nanoprotech – Liquid Electrical Insulation

Figure 90: Nanoprotech Coating - Assessment Experiment (Voltage (ADC) vs. Time (sec)

As shown in Figure 90, the Nanoprotech coating increased the rate of drop of the measured voltage, 

while the sensor being immersed from oil to water. This can be deduced from the severe drop in E35 

and E36 voltages, recorded immediately after the immersing started. In contrast, before applying the 

coat, the rate of change was much slower as shown in the graph. At the end of the experiment, when 

the voltages are almost stable, values measured by the coated sensor were lower than the values recorded 

by the non-coated sensor. For instance, the voltages measured by E35 and E36 before coating, were 

between seven and eight (ADC). After coating, the voltages of E35 and E36 decreased to around two 

and three (ADC). This decrease validates that the coating played a role in enhancing the conductivity 

of the electrodes. In addition to the oil-fouling assessment experiments, several experiments were done 

0

20

40

60

80

100

120

140

160

0.1

1

1.92.83.74.65.56.47.38.29.1

10

10.911.812.713.614.515.416.317.218.1

19

19.920.821.722.623.524.425.326.227.1

28

28.929.830.731.632.533.434.335.2

Voltage (ADC)

Time (sec)

Not Coated - E35 Not Coated - E36 Coated - E35 Coated - E36
Page 92

to analyze the effect of the coating on the sensor response in all conditions (Air-Oil-Water). Voltages 

measured by electrodes when immersed in air and water, with and without coating, are shown in Table 

31.

Table 31: Voltages in air & water, before and after coating with Nanoprotech insulation material

Voltage (ADC)

Electrode Air (Not Coated) Air (Coated) Water (Not Coated) Water (Coated)

E25 138 138.19 3 2

E26 132 132 3 2

E27 132 132.06 2.44 2

E28 134 134.78 2.46 2

E29 133 133.01 2.04 2

E30 134 134.99 2.06 2

E31 133.69 134 2.24 2

E32 135 135 2.02 2

E33 132.05 133 2 2

E34 131 131.99 2 2

E35 131 131.96 2.04 2

E36 148 148.99 2.91 2

As shown in Table 31, the voltages measured by the sensor electrodes are almost equal in Air and Water 

cases, before and after coating. However, it was noticed that in the water case, voltages measured after 

coating are slightly lower and more stable than the voltages measured before coating. This result, 

validates the conclusion made in the previous experiment, indicating that the Nanoprotech coating 

enhanced the conductivity of the stripes without impacting the sensor behavior in other cases (equal 

values in Air case). Also, to check the coating effect on oil detection, two identical experiments were 

performed before and after coating the sensor cartridge. Initially, the sensor was partially immersed in 

water, where the first five electrodes were surrounded by air. Then, oil was added to the water container 

from a fixed position above the water surface. The purpose of this method was to monitor the oil effect 

while contacting the electrodes directly without being altered by the sensor motion. Also, the main aim 

of this experiment is to check if the coating impactsthe oil detection capability. The experimental results 

including the measured voltages (ADC) and the percent relative changes of the voltages, are shown in 

Table 32.

Table 32: Relative differences (%) in Oil case, before and after coating with Nanoprotech insulation 

Not Coated Coated

Voltage (ADC) Voltage (ADC)

Electrode Air Oil Relative Difference (%) Air Oil Relative Difference (%)

E1 167 160 4.19 168 161 4.17

E2 157 149 5.10 158 150 5.06

E3 156 148 5.13 157 149 5.10

E4 159 150 5.66 160 151 5.63

E5 158 149 5.70 159 149 6.29
Page 93

The experimental results revealed that the percentage relative changes, due to the oil contact, were 

similar in both versions (4-6%); coated and non-coated. Thus, it can be concluded, that the coating 

didn’t have any negative effect on the oil detection ability.

The experimental results presented above showed that the Nanoprotech coating helped in reducing the 

oil-fouling process, while not impacting the air and oil detection capabilities. Also, the use of the 

Nanoprotech material was observed visually to decrease the wetness of the sensor board while being 

immersed in water. However, several questions were raised regarding the actual composition of this 

material, and the reason behind enhancing the conductivity of the electrodes. Clarification of the actual 

role of the material is needed, based on the fact that this material’s primary role is to insulate electrical 

parts in water and not to enhance the conductivity. However, further investigation is needed in the future 

work.

Preliminary Work Done on Coating with Ultra-ever dry Material

While attempting to reduce oil-foiling of the strips, Ultra-Ever-Dry produced by Ultratech was tested. 

First, after spraying the sensor with the two product components (base coat and top coat), the sensor 

was immersed and removed several times in a set of water/oil mixtures, including light, medium and 

heavy (Fuel) oil samples. As a result, in most of the trials, it was observed that the oil was falling-off 

immediately after removing the sensor from the examined liquid. However, in some cases, especially 

when dealing with heavy oil types (Fuel), the material performance was affected.

To analyze the effect more accurately, several experiments were performed. The experiments were done 

using the same sensor board, before and after applying the coating. The ultra-ever dry material is applied 

by manual spraying in two stages. In the first stage, the sensor cartridge is sprayed by the bottom coat 

material. Then, the board is kept drying in air for around 15 minutes. In the second stage, the top coat 

material is applied to the board by manual spraying. After applying the top coat, the sensor is kept 

drying in air for around twenty-four hours, before the first use. The spraying process was performed 

under a specialized fume hood. The fume hood is a self-contained, filtered laboratory enclosure, used 

to remove hazardous vapors and particles resulting from the spraying process. It is important to note 

that the spraying was done based on regular motion iterations, to have a homogenous and equally 

distributed amount of coating material on the sensor surfaces. However, since the spraying was done 

manually (by hand), some differences in the coating distribution was observed on the sensor surfaces. 

In the first experiment, the voltages measured by the sensor electrodes were recorded while the sensor 

was set to a fixed position in air. To assess the impact of this material on the sensor, voltage values 

obtained after applying the coating are compared to the corresponding values stored before applying 

the coating. A sample of the experimental results, showing the average of a set of voltages measured by 

the first twelve activated electrodes, are shown in Table 33, and the corresponding graph is shown in 

Figure 91.

Table 33: Relative differences (%) and Voltages (ADC), before and after coating with Ultra-ever dry

Electrode Non-coated (Avg. Voltage (ADC)) Coated (Avg. Voltage (ADC)) Relative Differences (%)

E1 171.00 167.83 1.85

E2 161.79 158.11 2.27

E3 160.05 156.03 2.51

E4 166.00 156.00 6.02
Page 94

E5 163.12 155.31 4.79

E6 167.02 153.86 7.88

E7 164.48 158.46 3.66

E8 168.98 162.40 3.89

E9 162.60 158.03 2.81

E10 163.72 157.91 3.55

E11 157.72 153.17 2.88

E12 160.93 156.00 3.06

Figure 91: Voltages (ADC) measured before and after coating with Ultra-ever dry (Air) - Graph

The experimental results revealed that the voltages measured by the capacitive stripes in air decreased 

after applying Ultra-ever dry material. Since the voltage is inversely related to the capacitance, it was 

concluded that the additional coating layer increased the base capacitance of the stripes.

To assess the voltage-drop numerically, the percent relative differences are calculated and shown in 

Table 33. The percentage differences varied among the stripes, ranging between 1.85% and 7.88%. This 

variation is due to the different distribution of the coating material on the sensor surface. The average 

percentage difference was around 3.77%.

To monitor the effect of the coating material in the oil and water cases, another experiment was 

performed. In this experiment, the sensor was partially immersed in an oil/water mixture. Before 

applying the coat, the thirty-six activated electrodes were distributed in the oil/water mixture as follows:

 E1 to E16: Air 

 E17 to E21: Oil

 E22 to E36: Water

The voltages measured for the non-coated sensor, before and after immersing in the examined liquid, 

in addition to the percentage relative differences, are shown in Table 34.

140.00

145.00

150.00

155.00

160.00

165.00

170.00

175.00

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12

Voltage (ADC)

Electrode

Voltage (ADC) Non-coated Voltage (ADC) Coated
Page 95

Table 34: Non-coated - Voltage (ADC) & R.D (%). - Partially immersed in oil/water mixture

Voltage (ADC)

Case Electrode Calibration (Air)

Partially immersed in oil/water 

mixture Relative Difference (%)

Air

E1 171.00 172.00 0.58

E2 161.79 162.01 0.14

E3 160.05 160.99 0.59

E4 166.00 165.13 0.53

E5 163.12 162.97 0.09

E6 167.02 166.06 0.58

E7 164.48 164.94 0.29

E8 168.98 170.29 0.78

E9 162.60 164.00 0.86

E10 163.72 163.43 0.18

E11 157.72 159.03 0.83

E12 160.93 160.94 0.01

E13 148.09 147.86 0.15

E14 149.65 149.19 0.30

E15 150.06 149.83 0.15

E16 154.02 153.03 0.65

Oil

E17 152.98 147.97 3.27

E18 155.90 148.99 4.44

E19 154.02 147.88 3.99

E20 157.00 149.01 5.09

E21 153.68 141.99 7.61

Water

E22 154.00 66.82 56.61

E23 149.01 48.33 67.56

E24 151.04 48.15 68.12

E25 138.94 74.94 46.06

E26 139.61 45.64 67.31

E27 140.18 35.21 74.88

E28 142.98 18.07 87.36

E29 140.99 21.00 85.11

E30 143.09 36.58 74.43

E31 141.99 77.72 45.26

E32 143.51 17.97 87.48

E33 140.00 23.29 83.36

E34 139.43 17.49 87.46

E35 138.99 12.44 91.05

E36 153.23 32.11 79.04

The same experiment was repeated after coating the sensor with the ultra-ever dry material. The 

voltages (ADC) measured by all electrodes, in addition to percentage relative differences are shown in 

Table 35.
Page 96

Table 35: Coated (Ultra-ever dry) - Voltage (ADC) & R.D (%). - Partially immersed in oil/water

Case Electrode Calibration (Air) Partially immersed in oil/water mixture Relative Difference (%) Air

E1 167.83 166.95 0.52

E2 158.11 157.97 0.09

E3 156.03 156.00 0.02

E4 156.00 157.04 0.66

E5 155.31 156.60 0.82

E6 153.86 155.15 0.84

E7 158.46 157.01 0.92

E8 162.40 163.33 0.57

E9 158.03 157.81 0.14

E10 157.91 156.99 0.59

E11 153.17 152.11 0.70

Oil

E12 156.00 153.99 1.29

E13 143.23 140.06 2.21

E14 144.69 139.30 3.72

E15 144.20 139.78 3.07

E16 150.06 144.17 3.93

Water

E17 147.03 126.89 13.70

E18 144.94 107.01 26.17

E19 127.00 91.21 28.19

E20 127.97 92.70 27.57

E21 147.00 103.11 29.86

E22 147.09 103.90 29.36

E23 144.03 103.19 28.36

E24 146.97 103.74 29.41

E25 130.43 95.82 26.53

E26 131.94 95.25 27.81

E27 137.00 97.62 28.75

E28 154.00 136.27 11.51

E29 137.14 95.97 30.03

E30 133.03 97.48 26.72

E31 137.00 95.62 30.20

E32 134.00 94.69 29.34

E33 135.03 89.08 34.03

E34 135.71 83.13 38.75

E35 136.20 78.64 42.27

E36 152.29 90.60 40.51

To assess the effect of the coating material on each of the three cases (Air/Oil/Water), the average value 

of the percent relative differences calculated for each case in the two previous experiment (Table 34, 

and Table 35) are shown in Table 36, and the corresponding graph is shown in Figure 92.
Page 97

Table 36: Comparison between relative differences in Air/Oil/Water cases before and after coating

Average Percentage Difference (%)

Non-Coated Coated (Ultra-Ever Dry)

Air 0.42 0.53

Oil 4.88 2.84

Water 73.41 28.95

Figure 92: Comparison between relative differences in Air/Oil/Water cases before and after coating

This experiment revealed that after applying the Ultra-ever dry coating, the relative percentage change 

in the water was reduced. Theoretically, this result was expected since the coating plays a major role in 

isolating the electrodes from water. Without coating, the electrodes were short-circuited due to the water 

conductivity, thus, the potential difference was dropped to around zero. In contrast, after the coating 

was applied, an additional layer (dielectric) was added to the electrodes, increasing the base capacitance, 

and decreasing the sensor sensitivity. Also, a decrease of the relative change of the electrodes immersed 

in oil was observed.

Several important notes were recorded while working with the ultra-ever dry material. First, it was

noted that the manual spraying technique (by hand) resulted in an irregular distribution of the coating 

thickness on the sensor surfaces. Thus, a non-uniform coating layer was observed, impacting the base 

capacitance of the electrodes inconsistently. Based on this, a more consistent method is recommended 

to be used during the coating process. Also, it was observed that the addition of the Ultra-ever dry 

coating layer, decreased the sensitivity of the sensor. This conclusion was made after observing the 

decrease in the percentage relative difference calculated for each of the electrodes while being immersed 

in water and oil. Further investigation regarding the performance of this coating and super-hydrophobic 

materials will be done in future work.

As a conclusion, despite the fact that adding the vibration mechanism decreased the oil-fouling effect, 

it was not considered in the final sensor design since it has a major disadvantage related to the added 

power consumption and the complexity introduced to the packaging. Also, the super-hydrophobic 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Air Oil Water

Relative Difference (%)

Case

Average Percentage Difference (%) Non-Coated

Average Percentage Difference (%) Coated (Ultra-Ever Dry)
Page 98

(Ultra-ever dry) coating was not considered in the final sensor design since it decreased the sensor 

sensitivity. Regarding the Nanoprotech coating (Nano electrical insulation), despite the fact that it 

helped decrease the oil-fouling effect, it was not considered since further investigation is needed to 

validate the initial observations recorded during the initial experiments. At the end, the best solution to 

handle oil-fouling was found to be the horizontal pins introduced in the previous section, where no 

power consumption was added, and no major changes were applied to the sensor design and packaging. 

It is important to note that chemical coatings will be studied in more details in the future development.

6.8 Battery Testing

Several experiments were performed to test the performance of the selected batteries. The following

presented experiment was implemented to assess the selected 9V battery pack composed of 6 AA 1.5V 

batteries (single use – Energizer) connected in series. The battery pack used is shown in Figure 93, and 

the experimental parameters are shown in the list below:

 Baud Rate: 115200

 Number of MPR121 controllers: 4

 Charge Current: 32 μA

 Charge duration: 2 μsec

 Sampling time: 1 message every 2 seconds

 Data: One Message containing all voltage readings (48) in String format delimited by ‘,’. 

Message Example:

"786,750,754,756,755,753,755,757,753,745,736,728,729,699,695,698,702,699,694,680,625,609

,605,599,580,543,557,561,556,545,517,465,326,107,87,115,110,108,104,107,103,105,96,88,75,

69,56,43"

 Wireless Module Status: ON.

 Experiment Start Time: 10:00 PM 

 Experiment Date: 2/10/2017

 Sensor installation: Small tank (Lab). E1 – E13 (Air), E13-E22(Oil), E23-E48 (Water)

 Power Source: 9V made of 6 batteries energizer single-use (1.5V) connected in series.
Page 99

Figure 93: 9V Battery Pack - AA Batteries

The experiment lasted around 4 hours and the results showed that the selected batteries remained in an 

acceptable condition providing the circuit with the needed voltage during the entire experiment 

duration. For more accurate monitoring of the battery voltage value with respect to time, the input 

voltage was logged every 30 minutes. The numerical voltage values are shown in Table 37 and the 

corresponding graph is shown in Figure 94. 

Table 37: Voltage vs. Time - Battery Experiment

Time (HR: MIN) Voltage (V)

10:05 8.84

10:35 8.47

11:00 8.33

11:30 8.19

12:02 8.07

12:30 7.99

13:07 7.92

13:34 7.86

14:05 7.80

14:36 7.74
Page 100

Figure 94: Voltage vs. Time (Graph) - Battery Experiment

As a conclusion, since the batteries remained in good condition for almost 4:30 hours of continuous 

transmission, with a relatively high baud rate and no heating or disconnection in transmission was 

recorded, the 9V battery pack was selected for the beta design of the sensor. To increase the battery 

lifetime, NiMh AA batteries could be used without performing any modifications to the battery pack. 

Example of the rechargeable NiMh batteries with a capacity of 2500mAh is shown in Figure 95.

Figure 95: AA Rechargeable Batteries (2500 mAh)

6.9 Wireless Transmission Testing

To validate the range of the selected wireless modules, an experiment was performed on the third of 

October 2017, where all the sensor components were activated and embedded inside the alfa version of 

the proposed capacitive instrument (version 1). The RF transmitter was activated for the duration of the 

entire experiment. A message of raw voltage values measured by all electrodes was sent every two 

seconds. The base station (Laptop + RF Receiver) was placed at a fixed position and the sensor was 

moved around up to a distance of 200 meters from the base station: no disconnection or delays were 

recorded. The result of this experiment was considered acceptable, since the signal transmitted by the 

RF module was received for a distance exceeding the target range of the eventual application. 

7.60

7.80

8.00

8.20

8.40

8.60

8.80

9.00

10:05 10:35 11:00 11:30 12:02 12:30 13:07 13:34 14:05 14:36

Voltage (V)

Time (HR:MIN)
Page 101

6.10 Enhanced Algorithms Based on Sensor Movement Tracking

Movement Tracking in Dynamic Liquid Conditions

As described before, the aim of the algorithm enhancement is to detect the best set of measurements 

where we need to apply the oil thickness analysis. This approach is based on detecting the dynamic 

water conditions and tracking the direction of the wave interacting with the sensor body

(upward/downward). When the sensor is moving downward, most of the calculated relative differences 

increase since more electrodes will be immersed in the examined liquid. Electrodes moving from oil to 

water, and electrodes moving from air to oil will have more relative differences from baseline 

calibration values taken in air. In contrast, when the sensor is moving upward, the relative differences 

will decrease. The average relative difference of all electrodes is calculated at each measurement cycle 

and used as an indicator for the sensor movement. For the static case, since no movement occurs, the 

average should be changing within the random error interval with respect to time. In dynamic cases, the 

average will be increasing or decreasing based on the direction of the sensor movement or waves. If the 

sensor is fixed in place, the average value is used to track the motion of the liquid in contact with the 

sensor. To demonstrate this capability, the relative differences of all electrodes at each measurement 

cycle are stored in a 2D array and converted to a grayscale image, shown in Figure 96, where the y-axis 

corresponds to the electrode index and x-axis corresponds to time. A clear variation of the color intensity 

in the image is observed with respect to the vertical movement of the sensor in waves. Brighter 

intensities reflect an increase in the relative difference due to water contact. According to this graph, 

the amplitude and frequency of the waves can be determined, since the sampling time and the 

geometrical sensor properties are known.

Figure 96: Relative differences converted to grey scale image

To detect the set of measurements taken at the highest point of the sensor movement, the minimum 

relative difference presented by each electrode during a certain interval of time is selected. In contrast, 

to detect the measurements taken at the lowest point, maximum relative differences are selected.

Overall Thickness Measurement Algorithm

The measurement algorithm aims to find the indices of the electrodes located at interfaces between 

materials with different dielectric constants. In this application, and based on the sensor geometrical 

properties, electrodes located at the water/oil interface and the air/oil interface are detected and used to 

calculate the oil thickness in both static and dynamic conditions. The algorithm has a low dependence 

on the direct numerical values of the voltage measurements and can work on a device with a limited 

amount of computational resources.

The system starts by measuring the voltage values of all electrodes in a sequential manner, using the 

capacitive touch controllers, indexed by the multiplexers. The microcontroller reads the digital voltage 

values acquired from each electrode and applies several layers of digital filtering to enhance the stability 
Page 102

of the measurements. To normalize the acquired voltage values, relative percentage changes from 

baseline calibration values are calculated for each electrode. The calibration values are taken when the 

sensor is completely dry and placed in open air for a sufficient duration of time. The calculated relative 

percentage changes are used by the algorithm to decide on the state (air/oil/water) of each electrode. 

Since each ratio is assigned to its electrode index and knowing the geometrical dimensions of the sensor 

(separation between electrodes and stripe thickness), the actual thickness of oil can be calculated. For 

instance, the sensor was set to a fixed position in the oil/water mixture, and the relative algorithm based 

on finding the maximum gradients were tested. The relative differences and the detected Air/Oil and 

Oil/Water interfaces displayed at the MatLab GUI are shown in Figure 97, where the upper figure shows 

the relative differences on y-axis and the electrode index on the x-axis. The lower figure shows a onedimensional array where the electrode index is on the x-axis and the binary value (1=interface, 0=noninterface) used as an interface indicator is set on the y-axis. Several oil thicknesses were recorded and 

statistical analysis for the obtained measurements are shown in Table 38.

Table 38: Statistical analysis of thickness measured using relative algorithm (static)

Average Thickness (mm) 20.13

Standard Deviation 0.70

Sample Size 496

Confidence Coefficient 1.96

Margin of Error 0.06

Upper Bound 20.19

Lower Bound 20.07

MAX 26

MIN 17

Figure 97: MatLab GUI showing detected interfaces
Page 103

To handle dynamic cases where the sensor rises or drops relative to the oil layer (due to lift or dip of 

the vessel, or due to waves) a two-step algorithm is used. The first step is to store a set sensor voltage 

measurement taken during a specific interval of time. After that, the direction of sensor movement is 

detected for each of the stored rows. This is done by monitoring the sense of the average value in each 

measure (increasing/decreasing). Then, two approaches were introduced, the “Highest Point 

Algorithm” and the “Corrected Lowest Point Algorithm”.

For light oil types (ex. Diesel), the “Highest Point Algorithm” is applied; the approach consists of 

measuring the thickness using the set of values taken when the sensor reaches its highest points (crests) 

through the stored array. This is done since with light oil fouled stripes that moved from oil to air will 

clean relatively fast and stripes that are fouled at the bottom side are minimal (if any) since at the highest 

point most of the fouled striped went back into the oil. This algorithm is used also in static cases since 

the highest points will be regular points in the array.

The second approach is more suited for the heavy oil types (ex. Hydrocal, Calsol) where the negative 

impact of fouling is more effective. This approach is called the “Corrected Lowest Point Algorithm”. It 

works by detecting the measurements taken when the sensor reaches the lowest points in the stored 

array (minimums) and applying a correction method on them to remove the number of fouled electrodes. 

By using time interpolation, the correction mechanism works by finding the number of fouled electrodes 

and subtracting it from the detected oil interval before calculating the actual thickness. The reason for 

adopting this approach because with thick oils when sensor is on top the fouled stripes in the air will 

take long to clean. Therefore, instead consider the sensor when it is submerged the most since most 

fouled stripes would be at the bottom in the water. In this case, the algorithm will overestimate the oil 

thickness due to fouling and would require a correction step where fouled stripes are detected and 

removed from the thickness. The workflow of the overall algorithm is illustrated in the flowchart 

presented in Figure 98.

Algorithm detailed description:

Calibration

The calibration voltage values are acquired while the sensor is completely dry and placed in open-air 

for a preset duration of time. For each electrode, the average of all measured voltages is stored in a onedimensional array. While operating, the relative voltage difference (R) of each electrode of index ‘i’ is 

calculated using the following equation:

R (%) =

|Current[i] − Calibration[i]|

Current[i]

x 100

Validity Check

At each measurement cycle, a validity check is applied on the acquired values to check if the sensor is 

in a valid sensing position. A sensing position is set to be valid when the last electrode in the sensor 

array is immersed in water. The relative change of the last electrode is calculated and compared to a 

threshold value (50%) to decide if the electrode is in water. If the calculated relative change is greater 

than 50%, the measure is considered valid. Otherwise, the measure is considered invalid, and not stored 

in the measurements array. Also, a text message (Invalid) is sent to the base station to inform the user 
Page 104

about the sensor state. This would allow for the detection of the scenario where the sensor is out of the 

water.

Interface Detection (Detect-Interface)

To detect the interface between two different mediums, the algorithm uses a voting method named 

“Detect-Interface” and is described as the following:

// Function to get the interface between two different layers 

Detect-Interface (Relative Differences Array)

// Create a one-dimensional array (Votes) of size N initialized by zeros.

Votes = zeros (1, N) 

// Calculate votes of each electrode

index =1 

While (index<N) 

{

Votes(index) = mean (RD (index+1: N) – mean (RD (1: index)) – (RD(index)/2) 

}

// Get the index of the interface with the maximum value of votes 

Max_Value = MAX(Votes)

Interface = getIndex (Max_Value) 

// Return the index of the interface 

Return Interface

Where “N = 48” is the total number of electrodes, “index” is the electrode index, and “RD” is the array 

holding the relative differences from calibration.

To measure the oil thickness, the interface detection method is applied two times iteratively. After 

finding the water interface, all of the electrodes below it (greater in index) are removed, and the method 

is applied on the remaining electrodes to find the oil/air interface.

Highest Point Algorithm

Start 

While (1)

{

// initialize counter to zero 

c=0

While (c< 50)

{

// Measure voltages (all electrodes)

M = Measure ();

//Get relative Differences (RD), C: Calibration, M: Measured,

 RD = Get Relative Differences (C,M);

V = Check Validity (RD);

If (V = True (valid))

{

// add to the 2-dimensional temporary array holding valid relative differences

Temp Array (c,:) = RD;
Page 105

// increment counter

c = c+1;

}// End If 

}//End While - Temp Array Full

// Initialize temporary vector 

Temporary Vector = zeros (48);

// Get the minimum relative difference from each column (electrode)

For (k=0, k<48, k++) 

{

Temporary Vector (k) = MIN (Temporary Array(:,k));

}//end For 

// Calculate thickness 

Thickness = Get Thickness (Temporary Vector)

Overall Thickness = 0.8*Overall Thickness + 0.2*Thickness 

// Clear Temporary Array

Clear (Temp Array)

// Clear Temporary Vector 

Clear (Temporary Vector)

// End global While 

}//end While 

End

Get Thickness Method (Thickness = Get Thickness (Temporary Vector))

This function gets the oil thickness based on the “Detect-Interface” method described above. Detecting 

the oil/water interface, removing oil/water interface and all electrodes below it (greater in index) from 

Temporary Vector, detect the air/oil interface, and calculate the oil thickness using the detected number 

of electrodes located between the two interfaces.

Corrected Lowest Point Algorithm

Start

While (1)

{

// initialize counter to zero 

c=0

While (c< 50)

{

M = Measure ();

 RD = Get Relative Differences (C, M); //C: Calibration, M: Measurements

V = Check Validity ();

If (V = True) // Valid measure

{
Page 106

// add to temporary array

Temporary Array (c, :) = RD;

// increment counter

c = c+1;

}// End If 

}//End While - Temporary Array is Full 

// initialize empty arrays for maximum, minimum, and amplitude relative differences

Temporary Max Vector = zeros (48);

Temporary Min Vector = zeros (48);

Temporary Amp Vector = zeros (48);

// Get the maximums, minimums, and amplitudes

For (k=0, k<48, k++) 

{

Temporary Max Vector (k) = MAX (Temporary Array(:,k));

Temporary Min Vector (k) = MIN (Measurements Array(:,k));

Temporary Amp Vector = Temporary Max Vector (k) - Temporary Min Vector (k)

}//end For 

//Detect water interface 

Water Interface = Detect-Interface (Temporary Max Vector);

// Calculate relative change of averages to detect sensor movement 

Relative Change = ((Mean (Temporary Max Vector) – Mean (Temporary Min Vector)) / Mean 

(Temporary Max Vector)) (100)

// Check if relative change > threshold (%)

If (Relative Change > 3%) // if > threshold, then apply correction step

{

 // Correct Water interface by removing fouled electrodes 

 While (Water Interface > 1)

 {

 If (Temporary Max Vector (Water Interface) > Mean (Temporary Amp Vector (0):

Temporary Amp Vector (Water Interface -1)))

 {

 Water Interface = Water Interface – 1;

 }//end if 

}//end While 

 }//end if 

 // Remove Water interface and other electrodes beneath it from Temporary Max Vector and get 

oil interface 

 Temporary Max Vector = Remove Water Portion (Temp Max Vector, Water Interface) 

 // Get oil interface 

 Oil Interface = Detect-Interface (Temporary Max Vector);
Page 107

 // Get Oil portion and calculate thickness 

 Oil Interval = Water Interface – Oil Interface;

 // Calculate and update thickness 

 thickness = (Oil Interval) (2) + (Oil Interval-1) (1)

 } // end While (1)

End

Figure 98: Algorithm flowchart

Detect lowest 

point values

Apply correction step

Start

Measure

Check 

Validity

Invalid

Vali

d

Normalize and calculate the relative differences

Detect the dynamic state of the sensor 

Static Dynamic

Add to Static Array Add to Dynamic Array

Detect highest 

point values

Get the interfaces (air/oil - oil/water) and calculate the oil thickness

Send oil thickness to the base station
Page 108

6.11 Mechanical Design Refinement and Optimization

Packaging Design

After optimizing the capacitive sensor design, the design of the mechanical packaging was also refined 

and adapted to fit the dimensions of the final sensor prototype. One of the most important factors that 

impact the package dimensions is the size of the batteries used to power the device. Multiple batteries 

have been contemplated to power the sensor, and accordingly, multiple packages have been designed 

to meet the different sizes as well as to optimize the final prototype package. Mainly, two types of 

batteries were considered, the 7.2V 3800mAh Nickel Metal Hydride (Ni-MH) battery, and the 

2500mAh 1.2V 'AA' Ni-MH batteries. As the battery life tests conducted showed promising results for 

the 2500mAh 1.2V 'AA' Ni-MH batteries, the double-sided six-battery holder was selected to be used 

in the final design for its compact size, availability, and acceptable efficiency. Figure 99 shows the two 

types of batteries.

Figure 100 and Figure 101 show the designs made for two different placement configurations of the 

7.2V 3800mAh Ni-MH batteries. Figure 102, shows the package for the sensor with the 2500mAh 1.2V 

'AA' Ni-MH batteries. Figure 103 is a comparison of all 3 packages.

Figure 99: 7.2V 3800mAh Ni-MH (left), 2500mAh 1.2V 'AA' Ni-MH (right)

Figure 100: Batteries flat side to side package
Page 109

Figure 101: package for batteries on one side and sensor on the other

Figure 102: Package of sensor powered by 'AA' Ni-MH
Page 110

Figure 103: Comparison of final packaging designs for capacitive sensor

Three final packages (Figure 102) in total were manufactured; the packages are the third iteration and 

are aimed to be relatively compact in size, made to fit the designed mounts for the skimmer tests and 

the moving bridge tests. The package in Figure 104-Left is mounted on a 3D printed extension bracket 

in order to fit the large testing tank; the extension is made to be modular in size in order to change height 

according to the desired distance from oil surface. The blue 3D printed plates allow varying the distance 

by increments of 1 cm, when added and removed from the assembly during testing. Figure 104-Right

shows the implemented package with the control unit and power unit installed. The final version of the 

packaging design assembled with the sensor cartridge, power unit, and the control unit is shown in 

Figure 105.

Figure 104: Final package with printed modular extension mount and instrumentation
Page 111

Figure 105: Final capacitive sensor prototype with packaging and instrumentation

Attachments for Final Testing

6.11.2.1 Bridge Hinged Mount

For the final sensor design testing, several attachments were manufactured to mount the sensor to the 

testing equipment. In order to mount the capacitive sensor to the moving bridge at the Ohmsett facility, 

another attachment was designed to fit to the setup available at Ohmsett. The attachment design is made 

of two parts: sensor mount fitting the extension on the packaging, and a plate made to size per the 

dimensions sent from Ohmsett. The two parts are hinged together. Figure 106 is the drawing of the 

bridge mount connection plate, and Figure 107 is the first designed attachment using a spring lock 

mechanism, designed to keep the sensor in the water and avoid it lifting due to dragging and waves, 

Figure 108 shows the final attachment that was adopted, relying on the weight of the sensor mount 

extension to keep the sensor horizontal during tests.
Page 112

Figure 106: Tow point mount drawing (dimensions in inches)

Figure 107: Spring lock attachment computer-aided design
Page 113

Figure 108: Retracted attachment (right), Released attachment (left) computer aided design

6.11.2.2 Manufactured Bridge Hinged Mount

The hinge mount was manufactured and then modified after manufacturing in order to reduce weight 

and eliminate non-structural material. Figure 109 and Figure 110 are images of the actual mount after 

weight reduction. A combination of weldments of U-beams and 2x2 metal pieces were added in order 

to provide structural support and to restrict the angle at a 0-90 degrees range of motion. The mechanism 

is naturally open and tends to reopen under the effect of gravity when released, due to the added moment 

of the cable extension arm pointing down and away from the hinge. The sensor fitting is made of the 

same 2x2cm metal rod as the stoppers; unlike the CAD design this fitting is non-hollow, and the same 

geometry is used in the assembly.

Figure 109: Manufactured hinged mount
Page 114

Figure 110: Dimensions after manufacturing

6.11.2.3 Skimmer Mount Design

The mount that goes onto the skimmer is a beam fixed on two of the three floaters at the stainless-steel 

cylinders above water level by an omega bracket fixture, it is composed of three parts: the beam, the 

rail, and the slider. The beam is composed of two equal length parts connected by a bracket assembly; 

to allow for adjustment to the angle of the cylinders, the rail connects to the beam assembly and a slider 

is mated on the rail to allow for vertical adjustment of the sensor height. Two designs were adopted and 

manufactured: the first is made of a weld series of metal 4x4cm beams and weighs 9.3kg; a second

design is manufactured of aluminum extrusions, which are rectangular in cross-section and are of 4x2cm 

in area. The use of the aluminum extrusions allows us to avoid welding, as well as cutting down the 

weight to 4.3kg and a more flexible room for adjustment to the actual size of the skimmer. Figure 111

shows the metal beam design and Figure 112 shows the aluminum design.
Page 115

Figure 111: Metal beam mount computer-aided design

Figure 112: Aluminum extrusion mount computer-aided design

6.11.2.4 Manufactured Skimmer Mount

The skimmer mount was manufactured out of 2x2cm aluminum extrusion slots. Figure 113, Figure 114, 

and Figure 115 are the final subassemblies of the skimmer mount. It should be noted that during 

assembly of the aluminum extrusion, the screws used should have low wiggle in order to prevent 

wedging under sliding friction of the different subassemblies. A preferred way to assemble the parts is 

to combine the different parts before adding the joiners and brackets since sliding the pan heads is much 

simpler and more efficient than having to start with the brackets and build the rest into the assembled 

parts. The assembly order should be the horizontal beam first with the proper distance adjustment onto 

the buoy floats, the vertical slider second with the sensor holder already mounted, finally the height 

adjustment would be made by untightening the vertical screws on the vertical side of the triangular 

brackets.
Page 116

After testing at the facility, it was noted that the skimmer was lower than expected and the sensor got 

completely covered by oil; therefore, fixing the mount’s vertical position should take into account the 

sinking effect that will be caused by an increase in oil volume due to either fluid accumulation under 

flow or change in static environment. In order to prevent dunking of the sensor in oil during use, another 

phenomenon to be accounted for when fixing the sensor holder is to use the complete range of strips in 

order to cover thicknesses from higher buoyancy (minimal oil volume) to smaller buoyancy (maximum 

allowable oil thickness by the sensor). The latter is dependent on the skimmer buoyancy, and the density 

of oil in the environment.

Figure 113: Manufactured skimmer mount sensor holder

Figure 114: Manufactured skimmer mount right angle 

slider

Figure 115: Manufactured skimmer 

mount horizontal fixture
Page 117

Chapter 7- Ohmsett Testing

Between November 27 and December 1, 2017 extensive testing at Ohmsett was carried out. The testing 

included:

1- Capacitive sensor indoor testing in a tank (Static, vertical and horizontal motion)

2- Spectro sensor indoor testing in a tank (Static and slight motion)

3- Capacitive sensor outdoor testing in the large tank (dragged by the bridge with no waves, mounted 

on the skimmer free floating and dragged with and without waves)

4- Spectro sensor outdoor testing in the large tank (free floating with and without waves)

The properties of the oil-types used in the experiments as provided by Ohmsett, are shown in Table 39.

Table 39: Pre-Test Lab Analysis (Ohmsett)

Pre-Test Lab Analysis (T664) 

Test Oil S.G. @20C Viscosity 

@20C I.F.T. S.T. BS&W Date

Diesel 0.842g/mL 8cP 13.8dynes/cm 30.1dynes/cm 0% 11/1/17

Hydrocal 

300 (New) 0.909g/mL 220cP 22.6dynes/cm 33.5dynes/cm 0.8% 11/1/17

Hoops 

(Weathered) 0.896g/mL 55cP 15.8dynes/cm 33.3dynes/cm 1.4% 11/1/17

Calsol 8240 0.931g/mL 2653cP 31.0dynes/cm 36.4dynes/cm 0.2% 11/1/17

Hoops 

(fresh) 0.865g/ml 28cP 16.9dynes/cm 31.5dynes/cm 0% 11/1/17

Description S.G. @20C Salinity I.F.T. S.T. Average 

Temperature

Date

Test Basin 

Water

1.019g/ml 28.3ppt 35.9dynes/cm 65.4dynes/cm See tests 12/4/17

Lab Tech Allen Cannone

This chapter presents the detailed results of all the experiments with the analysis. A video showing the 

capacitive sensor testing is available at: https://youtu.be/GufzKsxNTyo and a video showing the 

spectro-based sensor testing is available at: https://youtu.be/gYLgEAgGhe0.

7.1 Dipping Tests (sensor #1 - Capacitive Sensor)

Dipping tests were designed to test the sensor in known slick thicknesses of different values using four 

different test oils. These tests were performed in the Ohmsett high-bay area using small clear glass tanks

(12.875” x 12.875”) with clear sides, where known oil thicknesses were created on approximately a 6 

inches (152.4 mm) depth of saltwater. Starting with the oil with the lowest viscosity, thicknesses were 

increased, as per a test matrix, by dispensing the appropriate volume using graduated cylinders. The 

oils were dispensed in order of increasing viscosity. The sensor was deployed manually for each 

condition by the onsite AUB engineers and was displaced in a regular pattern upon entry into the test 

slick. The sensor was first held steady to obtain an initial static case reading, followed by a dynamic 

case for a duration of about one minute of reciprocating vertical motion, and then followed by about
Page 118

one minute of lateral motion in the direction parallel to the oil slick. The experimental setup used in 

performing the dipping tests (static and dynamic cases) is shown in Figure 116.

Figure 116: Experimental setup for dipping tests (Ohmsett)

This process was repeated in a relatively consistent manner for each test condition while data was 

recorded by AUB engineers via wireless communication to a nearby laptop. The series of tests included 

four oil types: diesel, Hoops Crude oil (weathered), Hydrocal 300 and Calsol 8240. Starting with the

thinnest slick, each oil was dispensed to create eight different slick thicknesses ranging from 0.125 to 3 

inches (3.175 to 76.2mm). The sensor was removed from the tank when oil was being added but the 

sensor was not cleaned or modified in any way between tests. The tank was completely emptied and 

cleaned between the tests using different oil types.

Ohmsett Observations/Notes:

 A miniscus effect above and below the oil slick was observed along the tank wall perimeter 

potentially skewing thickness.

 When dispensing more viscous oils (Hydrocal 300 and Calsol 8240) residual oil remained on 

the graduated cylinder walls.

As a result, the residual had a cumulative effect on the total volume dispensed and may have skewed 

the thicknesses to less than targeted.

The method used to perform the dipping tests is summarized by the following points: 

- Prepare slick(s) in small tank (over salt water)

- Manually place sensor into slick at random height without visual or other alignment to the oil 

slick, and then obtain readings.

- Remove sensor, increase thickness – repeat.

For all of the dipping tests performed, the volume amounts added (Figure 117) to obtain the required 

thicknesses are summarized in Table 40.
Page 119

Table 40: Dipping tests - Target thicknesses and Volumes

Target Thickness (mm) Total Volume (ml) Volume to Add (ml)

1 339.6 339.6

2 679.2 339.6

3 1,358.4 679.2

4 2,037.7 679.2

5 2,716.9 679.2

6 4,075.3 1,358.4

7 5,433.8 1,358.4

8 8,150.7 2,716.9

Figure 117: Adding controlled amount of oil - Graduated Cylinders

Dipping Tests (1-8) - Diesel

Tests numbered from 1 to 8 used diesel oil with a thickness ranging from 0.125 inches (3.18mm) to 

3inches (76.20mm). Water and oil temperature were recorded ranging between 69°F and 71°F. The 

experimental results showing the measured thicknesses versus the actual thicknesses are shown in Table 

41 for the static case and in Table 42 for the dynamic case. The results were obtained based on the 

“Highest Point Algorithm” developed for light-oil types (ex. Diesel – Hoops). Figure 118, shows the 

plot of the static and dynamic Diesel experiments with respect to the actual estimated thicknesses.
Page 120

Table 41: Capacitive Sensor Dipping Tests (1-8) – Diesel / Static

Test Type Case Oil Type

Dipping Static Diesel

Test Number

Actual 

Thickness 

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error (mm)

1 3.18 6 0 19 02:38 2.83

2 6.35 6.01 0.01 22 03:04 0.34

3 12.70 13.9 1.71 30 04:14 1.2

4 19.05 19.88 1.65 28 03:57 0.83

5 25.40 25.66 1.9 27 03:48 0.26

6 38.10 36.24 2.41 20 02:47 1.86

7 50.80 49.95 0.88 18 02:29 0.85

8 76.20 70.41 7.82 21 02:55 5.79

AVG 03:14 1.745

Table 42: Capacitive Sensor Dipping Tests (1-8) – Diesel / Dynamic

Test Type Case Oil Type

Dipping Dynamic Diesel

Test 

Number

Actual 

Thickness 

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error 

(mm)

1 3.18 10.88 6.98 35 04:58 7.7

2 6.35 6.01 0.01 22 04:23 0.34

3 12.70 13.35 0.97 29 04:06 0.65

4 19.05 18.66 1.5 27 03:48 0.39

5 25.40 25.61 1.8 22 03:04 0.21

6 38.10 29.63 5.72 29 04:06 8.47

7 50.80 36.88 11.78 32 04:32 13.92

8 76.20 76.85 1.38 24 03:22 0.65

AVG 04:02 4.04
Page 121

Figure 118: Dipping Tests (1-8) / Diesel (Static & Dynamic Curves) 

As shown in Figure 118 and Table 41, the absolute error of the measured thicknesses in the static case 

didn’t exceed the resolution of the sensor (3mm) in all the performed tests (1-8) except in the last test 

(8) where the average measured thickness is around 70.41mm and the actual was estimated as 76.2mm. 

It is important to note here that in this case, the absolute error (5.79mm) didn’t exceed the error caused 

by misclassification of two strips (6mm) out of forty-eight strips contained in the sensor PCB. Also, it 

is important to note that 5/8 static tests showed an extremely high accuracy with an average absolute 

error less than 1mm.

In the dynamic case, it is noted that as shown in Table 42, and Figure 118, 5/8 of the dynamic tests 

showed an extremely high accuracy, with an average absolute error less than 1mm. However, as 

expected, and because of the increased dynamic motion of the sensor (vertical and horizontal 

movement), the number of misclassified strips increased in some cases causing an increase in the 

absolute error in comparison to the static case.

Based on the experimental results, it can be concluded that despite the fact that the sensor passed most 

of the static and dynamic tests with an acceptable accuracy, the misclassification of few number of 

electrodes in the dynamic tests was leading to a sudden increase in the absolute error because of the

relatively high width of the sensor electrodes (3mm) and due to some remaining fouling effect. To solve 

this problem, it is recommended to decrease the electrodes width to increase the sensor resolution and 

to lower the impact of misclassified electrodes on the measured thickness. This conclusion is of high 

importance to be considered in the future development of the device.

Dipping Tests (9-16) – Hoops (weathered)

Tests numbered from 9 to 16 used Hoops (weathered) Oil with a thickness ranging from 0.125 inches

(3.18mm) to 3 inches(76.20mm). Water and oil temperature were recorded around 70°F (69°F -71.5°F). 

Some comments recorded during performing these tests are:

- Observed oil coating on sensor below slick (Figure 119)

- Observed meniscus effect on oil containers (skew actual thickness)

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

1 2 3 4 5 6 7 8

Oil Thickness (mm)

Test Number (1-8)

Dipping Tests (1-8) - Diesel

Actual Thick. (mm) Static Thick. (mm) Dynamic Thick. (mm)
Page 122

Figure 119: Coating of the sensor body under slick - Hoops (weathered)

The experimental results showing the measured thicknesses vs. the actual thicknesses are shown in 

Table 43 for the static case and in Table 44 for the dynamic case. The results were obtained based on 

the “Highest Point Algorithm” developed for light-oil types (ex. Diesel – Hoops). Figure 120, shows 

the plot of the static and dynamic Hoops (weathered) experiments with respect to the actual estimated 

thicknesses.

Table 43: Capacitive Sensor Dipping Tests (9-16) – Hoops (weathered) / Static

Test Type Case Oil Type

Dipping Static Hoops 

(weathered)

Test 

Number

Actual 

Thick.

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Dev.

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error (mm)

9 3.175 15.05 14.72 32 04:31 11.88

10 6.350 8.92 3.25 19 02:37 2.57

11 12.700 12.99 1.03 21 02:56 0.29

12 19.050 19.26 0.78 22 03:04 0.21

13 25.400 24.26 2.92 18 02:29 1.14

14 38.100 35.27 4.65 22 03:04 2.83

15 50.800 51.66 2.52 24 03:21 0.86

16 76.200 70.28 7.79 26 03:39 5.92

AVG 03:12 3.21
Page 123

Table 44: Capacitive Sensor Dipping Tests (9-16) – Hoops (weathered) / Dynamic

Test Type Case Oil Type

Dipping Dynamic Hoops 

(weathered)

Test 

Number

Actual 

Thickness 

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error (mm)

9 3.175 18.06 4.81 26 03:39 14.88

10 6.350 7.61 1.07 26 03:40 1.26

11 12.700 11.74 1 22 03:04 0.96

12 19.050 17.24 2.22 28 03:57 1.81

13 25.400 21.3 2.93 23 03:13 4.1

14 38.100 32.14 2.86 24 04:26 5.96

15 50.800 43.32 5.78 26 03:40 7.48

16 76.200 80.43 6.98 24 04:32 4.23

AVG 03:46 5.08

Figure 120: Dipping Tests (9-16) / Hoops (weathered) (Static & Dynamic Curves)

For the static tests, as shown in Table 43, three tests (11, 12, and 15) showed an extremely high accuracy 

with an absolute error less than 1mm. The remaining tests showed an acceptable accuracy with an error 

around one to two electrodes out of forty-eight total electrodes producing an absolute error ranging from 

around 1 to 5mm. It is noted that the larger error occurred at the first test (Test No. 9) where the actual 

thickness is around 3.18mm. To interpret the cause of the error, we plot the set of measurements 

acquired while performing the first test (Test No. 9) with respect to time (Figure 121). 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

9 10 11 12 13 14 15 16

Oil Thickness (mm)

Test Number (9 - 16)

Dipping Tests (9-16) / Hoops (weathered) 

Actual Thick. (mm) Static Measured Thick. (mm) Dynamic Measured Thick. (mm)
Page 124

Figure 121: Test 9 (Static) – Hoops (weathered) (3.175mm)

The curve plot shown in Figure 121 describes the reason for having a relatively large thickness 

measurement average (15.05mm) while the actual thickness is around 3.175 mm. As the sensor is 

immersed in the liquid for the first time, most of the electrodes are covered with a layer of oil due to 

the fouling process. Because of the fouling effect, initial readings of the sensor were relatively large 

(63mm – 52mm – 43mm). Also, the use of a relatively-slow moving average rate contributes to

decreasing the change of the sensor measurements. However, it is obvious that the sensor reaches a 

steady state at around (7:28:35 PM) where all of the remaining measurements are less than 10mm and 

become almost stable at 6mm. It is worth noting that in static cases the fouling of strips that went 

through oil and into water can be detrimental since the sensor is not moving to “wash” it off. In a normal 

operational case the user could be instructed to move the sensor around to reduce this effect; however, 

in all our tests this was not done in order not to bias the results. It also important to note that based on 

the sensor resolution which is around 3mm (single electrode width (2mm) + vertical gap (1mm)), this 

result is considered acceptable. In future development, this problem may be addressed by increasing the 

sensor resolution and speeding up the measurement change rate in addition to coating the sensor.

For the dynamic case, as shown in Table 43, and Figure 120, the error slightly increased due to sensor 

movement and fouling. However, with an absolute error equal to less than one electrode (3mm) in three 

tests (10, 11, and 12) and less than two electrodes (6mm) in another three tests (13, 14, and 16), the 

result is considered acceptable. It is noted that the larger error occurred at the first test (19), and that 

during performing these test, significant fouling of the sensor below the slick was recorded. Regarding 

the first test where the sensor is completely immersed for the first time through the thin oil layer, the 

fouling effect was very high. Also, as reported by the testers, the actual thickness is slightly skewed by 

the effect of meniscus effect noted on oil containers.

While most results were extremely satisfying, it was observed that the effect of fouling was maximized 

while dealing with thin oil layers (test 9). Accordingly, mitigating the fouling issue will be considered 

a priority in the future development of this measurement device. This can be done by several approaches

related to hardware design and algorithm improvements. For example, the anti-fouling mechanical 

structure, including horizontal pins may be further developed by enhancing the pins manufacturing, 

adding high-frequency vibrators, and testing hydrophobic chemical coatings.

0

10

20

30

40

50

60

70

7:26:24 PM 7:27:07 PM 7:27:50 PM 7:28:34 PM 7:29:17 PM 7:30:00 PM 7:30:43 PM 7:31:26 PM 7:32:10 PM Oil Thickness (mm)

Time
Page 125

Dipping Tests (18-25) – Hydrocal 300

Tests numbered from 18 to 25 used Hydrocal 300 oil with a thickness ranging from 0.125 inches

(3.18mm) to 3 inches (76.20mm). Water and oil temperature were recorded around 70°F (69°F -71°F). 

A note was reported while performing the first test (Test No. 18) describing that it was difficult to

uniformly cover the surface area with oil at this thickness (0.125inches). Measurement results are 

obtained by the “Corrected Lowest Point Algorithm” used for heavy oils. The experimental results 

showing the measured thicknesses vs. the actual thicknesses are shown in Table 45 for the static case 

and in Table 46 for the dynamic case, and the corresponding graph is shown in Figure 122.

Table 45: Capacitive Sensor Dipping Tests (17-25) – Hydrocal 300 / Static

Test Type Case Oil Type

Dipping Static Hydrocal 300

Test 

Number

Actual 

Thicknes

s (mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sampl

e Size

Sample 

Duratio

n 

(mm:ss)

Average Error (mm)

17, 18 3.175 12.09 4.09 21 02:56 8.91

19 6.350 6.76 0.67 19 02:38 0.41

20 12.700 13.35 1.53 16 02:11 0.65

21 19.050 18.97 1.94 19 02:38 0.08

22 25.400 27.78 1.9 21 02:55 2.38

23 38.100 36.43 2.57 19 02:38 1.67

24 50.800 47.48 4.39 22 03:04 3.32

25 76.200 72.36 3.51 24 03:22 3.84

AVG 02:47 2.65

Table 46: Capacitive Sensor Dipping Tests (17-25) – Hydrocal 300 / Dynamic

Test Type Case Oil Type

Dipping Dynamic Hydrocal 300

Test 

Number

Actual 

Thicknes

s (mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sampl

e Size

Sample 

Duratio

n 

(mm:ss)

Average Error (mm)

17, 18 3.175 5.97 2.31 26 03:38 2.8

19 6.350 12.01 5.3 27 04:08 5.66

20 12.700 9.13 4.41 22 03:32 3.57

21 19.050 15.3 3.44 19 02:37 3.75

22 25.400 29.67 4.43 19 02:59 4.27

23 38.100 36.85 3.56 24 04:16 1.25

24 50.800 52.03 3.15 25 03:31 1.23

25 76.200 80.08 1.87 24 03:22 3.88

AVG 03:30 3.30
Page 126

Figure 122: Dipping Tests (17-25) / Hydrocal (Static & Dynamic Curves)

The results of the Hydrocal 300 static experiments shown in Table 45, showed an acceptable accuracy, 

since all of the tests except for the first one had an absolute error of less than or around 3mm,

representing a misclassification of a single electrode. As described before, in this case also, the first test 

with the smallest oil thickness had the largest error. In the dynamic tests, the “Corrected Lowest Point 

Algorithm” was proven to produce good results since, as shown in Table 46, all of the dynamic tests 

showed an average absolute error ranging from around 1mm to a maximum of 5.4mm, representing the 

misclassification of fewer than two electrodes out of forty-eight. The measurement results of the static 

and dynamic tests with respect to the actual thicknesses are shown in Figure 122.

Dipping Tests (26-33) – Calsol 8240

Tests numbered from 18 to 25 used Calsol 8240 oil with a thickness ranging from 0.125 inches

(3.18mm) to 3inches (76.20mm). Water and oil temperature were recorded ranging between 69°F and 

70.5°F. Measurements are obtained by the “Corrected Lowest-Point Algorithm” used for heavy oils.

Table 47: Capacitive Sensor Dipping Tests (26-33) – Calsol 8240 / Static

Test Type Case Oil Type

Dipping Static Calsol 8240

Test 

Number

Actual 

Thickness 

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error (mm)

26 3.175 8.998 4.415 35 04:57 5.823

27 6.350 10.022 0.878 26 03:48 3.672

28 12.700 10.637 3.277 20 02:47 2.063

29 19.050 26.582 2.419 28 03:56 7.532

30 25.400 30.8 0.767 23 03:13 5.4

31 38.100 37.143 2.014 18 02:29 0.956

32 50.800 47.728 4.633 19 02:38 3.072

33 76.200 73.015 4.447 21 02:55 3.185

AVG 03:20 3.96

0.00

20.00

40.00

60.00

80.00

100.00

17, 18 19 20 21 22 23 24 25

Oil Thickness (mm)

Test Number (17-25)

Dipping Tests (17-25) - Hydrocal 300

Actual Thick. (mm) Static Measured Thick. (mm) Dynamic Measured Thick. (mm)
Page 127

Table 48: Capacitive Sensor Dipping Tests (26-33) – Calsol 8240 / Dynamic

Test Type Case Oil Type

Dipping Dynamic Calsol 8240

Test 

Number

Actual 

Thickness 

(mm)

Average 

Measured 

Thickness 

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Average Error (mm)

26 3.175 9.886 3.542 26 03:39 6.711

27 6.350 8.882 3.204 26 03:39 2.532

28 12.700 19.946 4.349 30 04:27 7.246

29 19.050 26.018 2.429 26 05:53 6.968

30 25.400 30.658 2.516 25 03:31 5.258

31 38.100 45.322 3.675 30 04:14 7.222

32 50.800 55.234 1.755 23 04:44 4.434

33 76.200 78.29 2.374 23 06:40 2.09

AVG 04:35 5.30

Experimental results of testing Calsol 8240 oil in static and dynamic cases showed high accuracy against 

different thicknesses. For instance, the results of the static case tests shown in Table 47, showed an 

absolute error ranging from 0.956mm (test 31) to 5.823 mm (test 26). Note that the maximum absolute 

error in this experiment did not exceed the misclassification of more than two electrodes out of fortyeight. For the dynamic case, despite that the error has slightly increased due to fouling and random 

sensor movement, the absolute error was also varying in a range between around 2mm to around 7mm,

representing one or two misclassified electrodes only. As discussed before, increasing the sensor 

resolution by making the electrodes thinner, and decreasing the vertical gap between them contributes 

to decreasing the effect of misclassification. To describe the behavior of all tested cases, Figure 123

shows the measured thicknesses in the static case and the dynamic case with respect to the actual 

thicknesses.
Page 128

Figure 123: Dipping Tests (26-33) / Calsol (Static & Dynamic Curves)

7.2 Outdoor Bridge Mounted Tests (sensor #1 - Capacitive Sensor)

This test was designed to obtain sensor data while advancing in surface slicks at a range of speeds that 

are typical when deployed onto spill response equipment or in fast water currents. The test setup was 

accomplished by preparing a channel along the test basin west wall, using boom attach brackets, end 

panels and a section of foam filled 24-inch boom (Figure 124). The final channel dimensions were 34 

inches wide by 58 ft-6 inches long. This nominal area was used to contain varying slick thicknesses as 

defined by a test matrix. The slick parameters for this series included thicknesses of 0.25. 0.5, 1.0 and 

2.0inches and was repeated using two test oils namely Hydrocal 300 (tests 34 to 61) and weathered 

Hoops (tests 73 to 86). Test oils were dispensed from totes positioned along the west deck and the 

volumes determined using physical depth soundings converted to gallons using the manufacturer 

conversion table. Sensor #1 was attached to the fabricated mount (AUB) which provided the ability to 

manually rotate the sensor from the deployed position to a raised (above channel barrier elevation) 

position using a hinge and pull rope. This was monitored during tests by a technician and implemented

if needed as a safety to avoid possible collision of the sensor into the barrier. The AUB mount and 

sensor was attached to an Ohmsett provided mount affixing the sensor to the main bridge providing for 

vertical adjustment. Figure 124, shows the sensor mounting setup with the sensor raised, boom attached

bracket and channel end panel.

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

26 27 28 29 30 31 32 33

Dipping Tests (26-33) - Calsol 8240

Actual Thick. (mm) Static Measured Thick. (mm) Dynamic Measured Thick. (mm)
Page 129

Figure 124: Capacitive Sensor – Bridge-mounted Experimental setup

Multiple passes were performed, and sensor data collected while traveling both in the north and south 

directions at 0.5 and 1.0knots. Passes at 2.0knots and above were performed in the south direction only. 

A total of 28 Ohmsett data files were recorded when testing in Hydrocal 300 oil and 14 files recorded 

when testing in Hoops Crude (weathered). Ohmsett files were recorded using LabVIEW software and 

were subsequently provided to us in an enclosed media drive. The recorded information pertinent to 

this test includes the speed of the sensor (bridge), the relative bridge position, ambient weather 

conditions, and the water temperature.

Ohmsett Observations/Notes:

- Wind effects apparently caused the contained slick to stack more towards the downward 

direction of the wind. When present, the wind was typically from the south direction Figure 

125. The wind direction and speeds are provided in the LabVIEW data files for confirmation.

- The target slick thickness was skewed to a lesser thickness especially with 2-inch slicks due to 

the boom bellying outward, and thereby increasing the surface area (Figure 127).
Page 130

- While advancing, leading edge of sensor appeared to create a bow wave possibly diverting oil 

away from the sensor contacts (Figure 126).

Figure 125: Contained oil in the channel - Wind Effect
Page 131

Figure 126: Bridge-mounted Sensor while dragging (Snapshot from video# 00131)

Figure 127: Photo showing boom bellying outward

Dynamic Tests (34, 60) – Hydrocal 300 

Hydrocal 300 dynamic tests were performed using four slick thicknesses, and four advance speeds. The 

test area is 34” x ~58.5’ channel along test basin west wall. The method used in performing the dynamic 

tests is as follows:

- Prepare defined slick thickness; with the sensor in oil-travel north at test speed, record 

measurements, stop; reverse direction, record measurements.

- Raise sensor to exit test area, lower sensor into clear basin water, record measurements travel 

at test speed 30 feet, reverse direction, stop near test area, raise sensor to move into test area, 

repeat north and south passes and clear water passes 3X.
Page 132

The ambient weather conditions recorded while performing these tests are shown in Table 49. The 

results of dynamic experiments are provided in Table 50. Measurements are obtained by the “Corrected 

Lowest-Point Algorithm” used for heavy oils. For each of the tests, the average of and the standard 

deviation of the recorded measured thicknesses is provided. Figure 132, shows the plot of the average 

measured thickness with in comparison to actual (estimated) thicknesses.

Table 49: Ambient weather conditions - Tests (34-60)

Test 

No.

Avg. Water Temp 

(°F)

Avg. Wind Speed 

(mph)

Avg. Wind Direction 

(°)

Avg. Air Temp 

(°F)

34 48.63 10.26 103.36 50.95

35 48.78 10.49 102.33 51.16

36 48.78 9.06 114.82 51.33

37 49.01 9.75 111.18 51.66

38 49.43 7.73 124.19 51.83

39 50.04 4.99 125.33 51.87

40 49.31 9.34 134.23 52.22

41 51.73 9.82 130.30 54.07

42 51.62 8.25 139.10 53.59

43 51.75 9.54 126.78 54.04

44 51.91 9.52 137.80 54.08

45 53.02 11.08 137.82 53.38

46 52.39 6.82 142.19 53.21

47 48.64 10.49 142.08 52.50

48 49.34 10.56 148.80 52.38

49 49.67 7.51 149.14 52.49

50 50.11 10.29 136.40 53.29

51 52.96 9.52 136.51 53.32

52 52.45 7.08 158.12 53.75

53 54.14 7.49 153.49 53.31

54 54.98 9.45 150.56 53.85

55 53.82 11.03 157.32 53.89

56 52.48 8.50 161.18 53.78

57 52.42 5.23 143.68 53.92

58 53.07 7.23 129.60 54.16

59 53.00 6.52 144.20 54.17

60 52.70 7.07 123.18 54.13

The measured thickness with respect to time and speed are shown in the figures below (Figure 128-

Figure 131) and the average of the acquired measurements in each case are shown in Table 50.
Page 133

Figure 128: Tests 34 - 40 / Thickness: 6,35mm / Speed: 0.5-1-2 knots

Figure 129: Tests 41 - 46 / Thickness: 12.7mm / Speed: 0.5-1-2 knots

0

10

20

30

40

50

60

70

80

90

100

18:03:0718:03:3318:03:5918:04:2618:04:5218:05:5218:06:1818:06:4418:09:2218:09:4818:10:1518:10:4118:12:1118:12:3718:13:0318:13:2918:14:4418:15:1018:15:3718:19:1918:20:3518:21:0218:21:2818:23:3618:24:0318:24:2918:24:5518:25:21

0.5 1 2

Oil Thickness (mm)

Speed (knots) & Time (hh:mm:ss)

Measured Thickness (mm) Actual Thickness (mm)

0

10

20

30

40

50

60

70

80

90

100

19:38:4019:39:1519:39:5019:40:2519:41:0019:41:3619:45:0419:45:3919:46:1419:46:4919:49:1019:50:1319:50:4819:51:5819:52:3219:53:0819:54:1519:54:5919:55:3419:56:0919:56:4419:57:1920:01:5820:02:3320:04:3920:06:0720:06:4220:07:1620:07:5120:08:2620:09:0120:09:3620:10:11

0.5 1 2

Oil Thickness (mm)

Speed (knots) & Time (hh:mm:ss)

Measured Thickness (mm) Actual Thickness (mm)
Page 134

Figure 130: Tests 47 - 52 / Thickness 25.4mm / Speed: 0.5-1-2 knots

Figure 131: Tests 53 - 60 / Thickness 50.08mm / Speed: 0.5-1-2 knots

Table 50: Capacitive Sensor Dynamic Tests (34-60) – Hydrocal 300

Test 

No.

Bridge/

Sensor 

Speed 

(knots)

Actual 

Thick.

(mm)

Average 

Measured 

Thick.

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Avg.

Error 

(mm)

Notes

34, 35 0.50 6.35 9.88 2.45 86 16:09 3.527

Preload: 26 gal, wind 

effects – slick think 

at south end

36, 37, 

38 1.00 6.35 3.10 0.15 32 06:23 3.253

0

10

20

30

40

50

60

70

80

90

100

20:24:0620:24:4120:25:5220:26:2720:27:0220:27:3720:28:1320:28:4820:30:1020:30:4520:31:2020:33:2520:34:2020:34:5520:35:3020:36:0520:37:1420:37:4920:38:2420:41:2420:41:5920:42:3420:43:0920:43:4420:44:1920:45:1920:45:5420:46:2920:47:04

0.5 1 2

Oil Thickness (mm)

Speed (knots) & Time (hh:mm:ss)

Measured Thickness (mm) Actual Thickness (mm)

0

10

20

30

40

50

60

70

80

90

100

21:24:3721:25:0321:25:2921:27:1121:29:0921:30:2221:31:0421:31:3121:33:2821:34:0721:34:3421:35:2421:35:5021:36:1621:36:4221:37:0921:38:1821:38:5321:41:5821:42:2421:43:1421:43:4021:44:0621:44:5121:45:18

0.5 1 2

Oil Thickness (mm)

Speed (knots) & Time (hh:mm:ss)

Measured Thickness (mm) Actual Estimated Thickness (mm)

Measured Manually Thickness (mm)
Page 135

39, 40 2.00 6.35 8.10 3.60 27 06:20 1.749

41, 42 0.50 12.70 11.97 4.26 38 08:18 0.727 Preload: 52 gal

43, 44 1.00 12.70 12.39 5.98 50 08:53 0.313

45, 46 2.00 12.70 19.89 6.75 43 08:31 7.189

47, 48 0.50 25.40 23.72 5.19 44 07:40 1.685 Preload: 104 gal

49, 50 1.00 25.40 20.86 4.90 29 04:59 4.537

51, 52 2.00 25.40 27.43 3.48 42 06:24 2.029

53, 54 0.50 50.08 33.06 4.71 24 07:42 17.020

Preload: 208 gal -

Thickness Measured 

1.5 inch (38.1 mm)

55, 56 1.00 50.08 34.10 2.83 29 05:34 15.984 Thickness Measured 

1.5 inch (38.1 mm)

57, 58, 

59, 60 2.00 50.08 34.60 2.61 22 03:46 15.482 Thickness Measured 1.5 inch (38.1 mm)

AVG 07:33 6.12

Figure 132: Dynamic Tests (34-35) – Hydrocal

As shown in Table 50, and in Figure 132, the average absolute error in all tests performed while 

dragging in different speeds ranging from a thickness of 6.35 to 25.04mm ranges between 0.313mm to 

7.1mm in the worst case. However, most of these tests showed an impressive accuracy of absolute error 

less than 6mm, representing the misclassification of fewer than two electrodes out of forty-eight

electrodes. Based on the different testing scenarios including different speeds and dragging conditions, 

this result is considered acceptable. It is obvious that for the last three tests numbered from 53 to 60 

where the actual estimated thickness was recorded as 50.08mm, the largest absolute error occurred. 

However, it is important to note that for this case, and based on the notes provided by Ohmsett, the 

target slick thickness was skewed to a lesser thickness, especially with 2-inch slicks, due to the boom 

bellying outward increasing the surface area. Also, Ohmsett staff noted that in this case, the actual 

0

10

20

30

40

50

60

34, 35 36, 37,

38

39, 40 41, 42 43, 44 45, 46 47, 48 49, 50 51, 52 53, 54 55, 56 57, 58,

59, 60

Oil Thickness (mm)

Test Number (34 - 60)

Dynamic Tests (34-35) - Bridge Dragging - Hydrocal 300

Actual Thick. (mm) Average Measured Thick. (mm)
Page 136

thickness was measured by manual visual tools as 1.5 inches (38.1mm). Based on this fact, and by 

taking the 38.1mm as the actual thickness, we note that the actual absolute error in these tests was in 

the range of 4 to 5mm since the measured thicknesses ranged from 33.06 to 34.60mm.

Dynamic Tests (73, 89) – Hoops (weathered)

Measurement results obtained from tests (73, 89) were recorded but will not be taken into consideration 

due to a technical error that occurred in the sensor while performing these tests. Most measurements for 

these test cases were stuck at the same number indicating a malfunction.

7.3 Skimmer Mounted Tests in Waves (sensor #1 - Capacitive Sensor)

The purpose of this test was to collect slick thickness data when mounted to a typical skimmer while 

experiencing wave conditions and traveling slowly into and against the waves. The test setup consisted 

of a boomed area along the test basin west wall measuring 10ft. x 34ft. Sensor #1 was rigidly mounted 

to a Desmi Termite skimmer frame and positioned between two of the floats, and in front of the 

skimming weir. The AUB and Ohmsett team provided mounts for vertical adjustment. Prior to testing 

the skimmer was placed into the test basin and the sensor adjusted such that the waterline was near the 

center of the measurement range.

Figure 133: Sensor #1 Mounted to Desmi Termite Skimmer

Figure 133 shows the sensor mounted on the skimmer in the test area. The skimmer was tethered with 

ropes; one rope routed to each of the main and auxiliary bridges. From these locations, technicians 

manually controlled the skimmer position and slowly towed the skimmer into the waves. As shown, the 

waves approached the skimmer from the right (south end of test basin). For these series of tests, 

Hydrocal test oil was provided at two slick thicknesses: 1 and 3inches. Multiple wave conditions were 

generated and adjusted during tests to provide wave heights not exceeding the operational range of the 

sensor. A total of three Ohmsett data files were recorded (tests 90-92) in which surface profile data was 

captured. Surface profile data, (for wave analysis) was captured using a downward looking distance 

sensor (named banner west) located on the main bridge. Video and still photos documented the response 

of the skimmer to the waves. Since the sensor was rigidly mounted to the skimmer, it was subject to the 

response of the skimmer in waves with respect to pitch, heave, and roll.
Page 137

Ohmsett Observations/Notes:

Testing performed using hardwire communication – due to wireless not functioning.

Skimmer-mounted Tests numbered from 90, 91, and 93 were performed using Hydrocal 300 oil, two 

thicknesses, and varied wave conditions. The method used to perform these tests is described as the 

following:

- Sensor #1 (Capacitive) mounted to skimmer via bracket provided by the AUB team.

- Establish initial sensor depth with a skimmer installed in water.

- Main and auxiliary bridges positioned at opposite ends of the test area.

- The skimmer will be tethered – a control rope going to each bridge.

- Obtain stationary readings; begin wave condition, run test for approximately 15 minutes, 

manually maneuver skimmer in the area as directed.

The wave properties used in the three tests are summarized in Table 52. Ambient weather conditions 

are shown in Table 51.

Table 51: Ambient weather conditions - Tests (90-92)

Test 

No.

Avg. Water Temp 

(°F)

Avg. Wind Speed 

(mph)

Avg. Wind Direction 

(°)

Avg. Air Temp 

(°F)

90 45.87 7.53 311.70 49.20

91 46.92 10.12 319.09 51.64

92 51.23 10.34 321.88 52.83

Table 52: Skimmer-mounted tests - Wave conditions

Test Wave Type Wave Setting H 1/3 (in.) Mean Wave 

Height (in.)

Avg. Wave 

Period (s)

Average Wave 

Length (ft.)

90 Sine

15 cpm, 3" 3.412 2.776 4.194 61.026

25 cpm, 3" 5.265 4.652 2.534 30.543

91

Harbor chop

45 cpm, 3" 8.795 5.752 n/a n/a

30 cpm, 3" 4.467 3.588 n/a n/a

92

15 cpm, 3" 3.533 2.812 n/a n/a

25 cpm, 3" 2.683 1.896 n/a n/a

25 cpm, 3" 2.72 2.189 n/a n/a

35 cpm, 3" 5.094 3.426 n/a n/a

Skimmer-mounted Tests - Test No. 90

- Description: Skimmer mounted, manual pull-in Wave

- Oil Thickness: 1 inch (25.4mm)

- Wave Condition: 9:00am – 1ST setting: 15 cpm, 3” / 2nd setting: 25 cpm, 3”

For the first test, three log files were recorded. The average measurement thickness calculated from 

each wave experiment, in addition to the starting and ending times are provided in Table 53. Figure 

134, shows the measured thickness with respect to time.
Page 138

Figure 134: Capacitive Sensor Waves Test (90) – Hydrocal 300

Table 53: Results for Skimmer-mounted (Capacitive - Test 90)

Wave 

No.

Start 

Time

End 

Time

Actual 

Thick.

(mm)

Average 

Measured Thick.

(mm)

Standard 

Deviation

Sample 

Size

Sample 

Duration 

(mm:ss)

Absolute 

Error 

(mm)

1

9:00:1

4

9:10:5

0

25.4 43.30 19.24 60 10:36 17.90

2

9:13:5

1

9:30:5

7

25.4 35.58 10.91 75 17:06 10.18

The result of the sine test 90 shown in Table 53 and Figure 134, revealed that the sensor’s accuracy was 

impacted mostly by the start of the wave and then the absolute error of the measurement decreased with 

time even when the second wave was actuated. This is related to two main factors. First, the fouling 

effect of heavy oil caused by oil accumulation on the sensor body increases the measured thickness 

especially at starting phase of the wave, where a sudden transition from calm to wave conditions occurs. 

The second factor is related to smoothing rate used in the algorithm. Smoothing is controlled by the 

moving average attributes, and based on the current settings, the implemented moving average rate is 

relatively slow. Speeding up the change rate may contribute to enhancing the recovery process.

Skimmer-mounted Tests - Test No. 91

Test No. 91, Harbor chop test. The Wave Condition:

- 10:00 am – 15cpm, 3” (too aggressive, reduced cpm to 30)

Figure 135, shows the measured thickness with respect to time, and average results are shown in Table 

54.

0

10

20

30

40

50

60

70

80

90

100

9:00:149:00:579:01:419:02:249:03:079:03:509:04:339:05:169:05:599:06:429:07:259:08:089:08:519:09:349:10:179:13:519:14:349:15:179:16:009:16:439:17:269:18:109:18:539:19:369:20:199:24:519:25:349:26:179:27:009:27:439:28:269:29:099:29:529:30:35

Oil Thickness (mm)

Time (hh:mm:ss)

Waves Test (90) – Hydrocal 300

Measured Thickness (mm) Calculated Thickness based on Volume (mm)
Page 139

0

10

20

30

40

50

60

70

80

90

100

9:58:289:59:119:59:54

10:03:2210:11:4410:12:2710:13:1010:13:5310:14:3610:15:1910:16:0210:16:4510:31:4410:32:2710:33:1010:33:5310:34:3610:35:1910:36:0210:36:4510:37:2810:38:1110:38:5410:39:3710:40:2110:41:0410:41:3710:42:2110:43:13

Oil Thickness (mm)

Time (hh:mm:ss)

Capacitive Sensor Waves Test (91)

Measured Thickness (mm) Calculated Thickness based on Volume (mm)

Figure 135: Capacitive Sensor Waves Test (91) – Hydrocal 300

Table 54: Results for Skimmer-mounted (Capacitive - Test 91)

ID Start 

Time

End 

Time

Actual 

Thick. 

(mm)

Avg. 

Meas. 

Thick. 

(mm)

Stand. 

Dev.

Sample 

Size

Sample 

Duration 

(mm:ss)

Absolute 

Error 

(mm)

Note

1

09:58:2

8

10:03:33 25.4 25.837 5.53 14 05:05 0.437 Calm

2

10:11:2

2

10:16:56 25.4 3 0 32 05:34 -

Sensor dunk into 

water (3mm –

invalid measure)

3

10:31:2

2

10:43:30 38.1 34.56 12.84 69 12:08 3.54

The results of the test 91 (Table 54) showed a very high accuracy at the beginning five minutes in calm 

conditions with an average absolute error of 0.437mm. After the wave was created, based on the notes 

recorded by the Ohmsett staff, the sensor was dunking into the water. During this time, sensor recorded 

3mm for a set of measurements. Actually, the constant 3mm measures are considered invalid since the 

sensor is located under the oil layer while dunking. After that, starting from 10:31:22, the sensor was 

producing valid measures. Based on the average of the valid measures taken in the third case, the 

average absolute error was also acceptable (3.54mm). It is important to note here that the dunking 

problem is one of the important points that should be taken into consideration in the future development 

of the device.

Skimmer-mounted Tests - Test No. 92

Test No. 92, Harbor chop test. The Wave Condition:

1- 10:55 am – wave started, 15cpm, 3”

2- 11:08 am – increased to 25cpm, 3”

3- 11:18 am – changed to 25cpm, 4.5”

Invalid
Page 140

4- 11:21 am – changed to 35cpm, 4.5”

5- 11:25 am – wave stopped

Figure 136: Capacitive Sensor Waves Test (92) - Hydrocal 300

Table 55: Results for Skimmer-mounted (Capacitive - Test 92)

ID Start 

Time

End 

Time

Actual 

Thick. 

(mm)

Avg. 

Meas. 

Thick. 

(mm)

Stand

. Dev.

Sample 

Size

Sample 

Duration 

(mm:ss)

Absolute 

Error 

(mm)

Wave 

Condition

1 10:51:00 11:03:16 38.10 46.16 7.58 69 12:16 8.07 wave started, 

15 cpm, 3”

2 11:05:39 11:14:59 38.10 46.79 5.42 53 09:20 8.69 increased to 

25 cpm, 3”

3 11:18:24 11:20:55 38.10 48.33 2.11 15 02:31 10.23 changed to 25 

cpm, 4.5”

4 11:21:06 11:23:58 38.10 41.91 6.46 12 02:52 3.81 changed to 35 

cpm, 4.5”

5 11:26:24 11:37:21 38.10 41.22 5.39 60 10:57 3.22 stopped

AVG 7:35 6.80

The measured thickness with respect to time and wave conditions is shown in Figure 136. The 

experimental average results shown in Table 55, shows that the average absolute error of the measured 

values in the first two cases were similar (8mm). However, the error increased to around 10 mm when 

the amplitude of the wave was increased to 4.5” in the third case. However, in the last two cases, the 

average error was highly reduced with the stopping of waves. It is important to note here, that despite 

the fact the sensor accuracy was affected by the presented wave conditions, the absolute average error 

0

10

20

30

40

50

60

70

80

90

100

10:55:0810:55:5110:56:3410:57:1710:58:0010:58:4310:59:2611:00:1311:00:5611:01:3911:02:2211:03:0511:06:0111:06:4411:07:2711:08:1011:08:5311:09:3611:10:1911:11:0211:11:4511:12:2811:13:1211:13:5511:14:3811:18:3511:19:1811:20:0111:20:4411:21:2711:22:1011:23:1311:27:1111:27:5511:28:38

Oil Thickness (mm)

Time (hh:mm:ss)

Capacitive Sensor Waves Test (92)

Measured Thickness (mm) Calculated Thickness based on Volume (mm)

Wave Condition: 1 2 3 4 5
Page 141

in terms of the sensor resolution was considered acceptable (10mm = 3 strips). Again, it can be 

concluded that fouling effect must be further mitigated by future enhancements.

We note that the skimmer was powered off during the skimmer tests. Based on the behavior of the 

skimmer while operation, vibrations may be introduced into the sensor body which may indirectly help 

in decreasing the fouling effect.

7.4 Slick Thickness Testing (sensor #2 – Spectro Sensor)

The Spectro sensor is designed to identify and measure relatively thin slicks less than 3 millimeters. 

Tests conditions provided for sensor #2 were primarily created in open-top totes and the slick 

thicknesses defined using mass balance. Two open top totes were used to provide a known surface area 

for testing. Each was near filled with saltwater from the Ohmsett test basin and placed in the high bay

area for use. One tote was used to provide a known slick thickness for calibration purposes and the 

second for varying slick thicknesses for measurement. Two oils, diesel and Hoops Crude (fresh) were 

supplied to create the range of slicks required as per the test matrix. The sensor was calibrated for each 

oil type used by taking measure at 0mm, 1mm and 3mm.

Ohmsett Observations/Notes:

- 100 and 200um slick thicknesses were difficult to establish, voids and variations were present.

- Use of dark oil (Hoops Crude) reduced the measurement range due to lack of transparency. The 

matrix was modified to use a narrower range of thicknesses; 100 to 700um.

Spectro Sensor Tank Test - Diesel

Spectro sensor Thickness Measurement Tests (high bay - slick prepared in 38. 5” x 44.0” x 30” poly

tank). The testing method is summarized as follows:

- Prepare slick in defined tray on basin water

- Place sensor onto slick, obtain readings.

- Add oil to the slick, obtain readings

The experimental results showing the average estimated thickness versus the actual thickness (estimated 

by volume) are shown Table 56 and the corresponding graph is shown in Figure 137.

Table 56: LED-based Sensor - Diesel / Tank Experiment (Measured Thicknesses)

Test Thick. 

(μM)

Average 

Measured 

Thickness (μM)

Abs. 

Error 

(μm)

S.D.

Sample 

Size

Sample 

Duration 

(mm:ss)

Notes

63 500 1422.79 922.79 954.35 63 02:06 Thin slick difficult to 

accomplish

64 -

Trial 1 1000 1485.94 485.94 863.82 65 02:08 Sensor moving around

64 -

Trial 2 1000 1544.56 544.56 1288.49 48 01:34 Sensor moving around

65 -

Trial 1 2000 1882.72 117.28 1043.50 87 02:52 Sensor moving around

65 -

Trial 2 2000 2162.05 162.05 815.72 91 03:00 Sensor moving around

66 3000 2745.70 254.3 395.76 105 03:30 adding oil from 2mm to 

3mm.

AVG 414.48 AVG 02:31
Page 142

Figure 137: Spectro-based Sensor - Diesel / Tank Experiment (graph)

As shown in figures above, the results showed an acceptable accuracy with an absolute error of 200 -

300μm in the last three tests where the actual thickness was estimated at 2000 and 3000μm. However,

the average error was around 500μm at the 1000μm thickness test and increased when testing thinner 

thicknesses. For interpreting this result, it is important to note that thicknesses below 500μm were

difficult to establish in all cases since the oil had an irregular distributed film as shown in the picture of 

Figure 138 and therefore the actual thickness was larger.

Figure 138: LED-based Sensor - Diesel Test (Tank)

Spectro Sensor Tank Test – Hoops (fresh)

The same experimental setup and method were used to test the spectro sensor against Hoops (fresh) oil. 

The experimental results are shown in Table 57, and the corresponding graph in Figure 139.

0

500

1000

1500

2000

2500

3000

3500

63 64 - Trial 1 64 - Trial 2 65 - Trial 1 65 - Trial 2 66

Oil Thickness (μm)

Test ID

Spectro-based Sensor - Diesel - Tank Experiment

Thick. (μM) Average Measured Thickness (μM)
Page 143

Table 57: LED-based Sensor - Hoops / Tank Experiment (Measured Thicknesses)

Test Thickness

(μM)

Average 

Estimated 

Thickness 

(μM)

Abs. 

Error 

(mm)

S.D. Sample 

Size

Sample 

Duration 

(mm:ss)

Notes

68 -

trial 1 100 981.82 881.82 10.51 54 02:06 Fouling (lens)

68 -

trial 2 100 815.03 715.03 244.39 126 04:12 after cleaning lens and dipping

69 200 894.96 694.96 110.26 70 02:18 Hoops was observed not 

uniform

70 300 549.45 249.45 389.42 145 05:12 Oil pushed in and out and 

moved around

71 500 792.78 292.78 310.97 107 03:56 moving around and clearing oil 

then moving in.

72 700 841.32 141.32 314.31 237 09:58 moving sensor

AVG 495.89 AVG 04:37

Figure 139: LED-based Sensor – Hoops (fresh) / Tank Experiment (Graph)

In this experiment, because of the dark color of the Hoops (fresh) oil, fouling of the light-emitter lens 

was clearly observed (Figure 140). It was observed also that the oil was not uniformly distributed 

(Figure 141).

0

200

400

600

800

1000

1200

68 - trial 1 68 - trial 2 69 70 71 72

Oil Thickness (μm)

Test Number

LED-based Sensor – Hoops (fresh) / Tank Experiment

Thickness (μM) Average Estimated Thickness (μM)
Page 144

Figure 140: Lens fouling – Hoops (fresh) Figure 141: Non-uniform Distribution (Hoops)

For the last three tests (70, 71, and 72), the measurement accuracy was acceptable with an average 

absolute error ranging from 141μM to 249μM. However, for the first three tests, the calculated error 

with the theoretically calculated thickness was relatively high (around 700 μM). However, this result is 

not conclusive since ground truth cannot be verified at these thicknesses. It is worth noting that during 

testing it was observed that when clear water patch passed under the sensor it gave low (down to 0 in 

some cases) measures and when oil slick passed under the thickness increased.

LED-based Waves Test - Diesel

The LED-based sensor floating experiment was performed within in open-water testing area; tested in 

thin “slicks” in test basin 10’ x 30’ area. The method used in implementing the tests is as follows:

- Deploy sensor #2 into area after preparing slick

- Allow it to drift. (have lightweight rope /string for control and retrieval)

Test No. 92, Wave Condition: 

1- 10:55 am – wave started, 15cpm, 3”

2- 11:08 am – increased to 25cpm, 3”

3- 11:18 am – changed to 25cpm, 4.5”

4- 11:21 am – changed to 35cpm, 4.5”

5- 11:25 am – wave stopped

Figure 142: Spectro-based Sensor - Test 92 - Waves (1, 2)

0

1000

2000

3000

4000

5000

10:52:0010:53:0310:54:0710:55:1110:56:1510:57:1910:58:2310:59:2711:00:3111:01:3511:02:3911:03:4311:04:4711:05:5311:08:5111:09:5511:10:5911:12:0311:13:0911:18:0111:19:0511:20:0911:21:1311:22:1711:23:2111:24:2511:25:2911:26:3311:27:3711:28:4111:29:4511:30:4911:31:5511:32:5911:34:0311:35:0711:36:1111:37:15

Measured Thickness (μm)

Time (hh:mm:ss)

Spectro-based Sensor / Wave Test / Test 92 (Time Plot)

Measured Thicness (μm) Actual Thickness (μm)
Page 145

Table 58: Spectro-based Sensor - Diesel / Waves Experiment

ID Start 

Time

End 

Time

Avg. 

Meas. 

Thick. 

(μm)

Actual 

Thick. 

(μm)

Abs. 

Error 

(μm) S.D. Sample Size

Sample 

Duration 

(mm:ss)

Wave Condition

1 10:52:00 11:05:57 545.81 500 45.81 471.43 419 13:57 10:55am, wave 

started, 15cpm, 3”

2 11:07:49 11:13:43 1072.61 500 572.61 391.05 175 05:54 11:08am, increased 

to 25cpm, 3”

3 11:17:33 12:17:33 274.11 500 225.88 381.54 104 0:03:26 11:18am, changed 

to 25cpm, 4.5”

4 11:21:01 11:24:59 273.72 500 226.27 515.91 120 0:03:58 11:21am, changed 

to 35cpm, 4.5”

5 11:25:01 11:37:55 1246.72 500 746.72 1259.33 120 0:12:54 11:25am, stopped

Figure 143: Spectro-based Sensor - Test 92 - Average graph

The measured thicknesses with respect to time are shown in Figure 142, and the average thickness with 

respect to each wave condition is shown in Figure 143. As shown in some cases the sensor measures 0 

which is expected as visually it was evident there were clear patches within the ring due to the nonuniform spread of the oil. The average of the measured oil thicknesses in each of the five wave 

conditions is presented in Table 58. It is noted that in this experiment, measurements obtained from the 

Spectro-based sensor (Table 58) showed high standard deviation values in all cases. This result was 

expected due to the behavior of the sensor in waves. However, for a baseline value of around 500μM, 

the sensor was able to measure 545μm in test 1 and around 247μm in tests 3 and 4. The other tests 

showed an average measurement ranging from 1072μm to 1246μm. As the main aim of the test was to 

assess the ability of the sensor to detect thin oil slicks, this result proved the ability of the sensor to 

0

500

1000

1500

2000

2500

3000

1 2 3 4 5

Measured Thickness (μm) 

Test Id

Spectro-based Sensor / Wave Test / Test 92 (Average Graph)

Avg. Meas. Thick. (μm) Actual Thick. (μm)
Page 146

detect and estimate the oil slick even with a slightly increased error in harsh dynamic conditions. It is 

worth noting that in such a test the ground truth cannot be controlled and thicker/darker oil from the 

nearby test seeped into the ring with diesel as shown in Figure 144.

Figure 144: Experimental area - Test 92
Page 147

Chapter 8- Conclusion, Recommendations and Future Development

We believe that most of the project objectives were met but there is still room for improvement. Several 

prototypes of the two sensors were developed and one of each of the latest models was kept at Ohmsett. 

Overall, the testing at Ohmsett was very successful in testing the limits of both sensors and identifying 

areas of improvement. We believe both sensors accomplished most aspects of TRL 5.

Based on the assessment of the experimental results obtained from the tests done at the Ohmsett facility 

and aiming to improve the performance of the presented sensors in real working conditions, we propose 

to consider several enhancements to the two sensing devices as a future development plan.

For the capacitive sensor, several important points will be addressed, including the following:

1- Although the fouling effect was significantly mitigated by the knife design, pins and the 

algorithms, it remained the main source of error, especially when dealing with heavy oils and 

highly dynamic conditions (waves and splashing). To handle this problem, we suggest 

improving the mechanical design of the anti-fouling mechanism used in the capacitive sensor,

including the implementation of the staggered horizontal pins, using hydrophobic coating 

materials, and/or the inclusion of a vibrator on the sensing board to help remove fouling.

2- Another limitation that appeared during the testing of the capacitive sensor against waves is the 

dunking of the sensor package in the examined liquid. One suggested remedy is by changing 

the design of the device in a way where the processing unit will be completely separated from 

the sensing unit. By doing so, the sensing unit length will be extended to be longer and 

extending significantly outside the oil surface, allowing the sensor to move freely through the 

oil/water mixture. Another option is to mount the device on a float, which would allow motion 

in only a vertical direction.

3- Use case Specifics: it was observed that there is a need for two types of capacitive sensors. One 

designed specifically to be handheld and standalone giving measures directly on a screen and 

another one to be mounted on skimmers.

4- We believe the sensor can be further developed to reach TRL 6 but there are operational 

considerations that need to be addressed such as protection from floating debris, faster response 

rate, and easier deployment.

5- To enhance the sensor resolution, and to decrease the impact of electrodes misclassification on 

the measured thickness, we suggest changing the electrodes design by decreasing the single 

electrode width and the vertical gap distance between the electrodes. This will increase the 

sensor resolution and accuracy.

6- Other limitations appeared during the testing such as failure of wireless connectivity and 

processing/memory limitations. We suggest replacing the main processing unit by a more 

advanced module, providing more advanced processing and storage capabilities. To attain low 

cost, compact size, and simplicity, we propose to use the “RASPBERRY Pi ZERO” module 

instead of the Arduino modules. To ensure a reliable wireless connection, the RF modules will 
Page 148

be replaced with more advanced units allowing the real-time configuration of the wireless 

settings. As a backup for communication, a cable connection will be provided as a built-in 

feature in the device.

7- As an enhancement, we propose to store all the raw data of the sensor for future debugging and 

analysis, an SD card slot will be installed in the device allowing the insertion of memory cards 

with several GBs into the device. Also, the memory cards will be used to record measured

thickness values, GPS coordinates, and any other required notes during the tests.

For the LED-based spectrometer device, several enhancements will be taken into account in the 

future design including the following:

1- It was observed that the light-source unit located underwater was highly affected by the fouling 

of oil while inserting and removing the device from the water/oil mixture. To avoid this 

problem, an alternative design relying on light/laser reflection will be tested. Based on the 

concept of light reflection, the light source unit will be placed above the water surface and 

aiming downwards, and the light sensor will be measuring the intensity of light beams reflected 

from the liquid surface. The main advantage of this approach is that it provides protection to 

the light emitter and receiver lenses from oil/water contamination. In addition, laser instead of 

LED emitters should be considered.

2- It is suggested that the buoy design be enhanced to isolate the sensor/emitter lenses from liquid 

splashing while operating in harsh dynamic environments.

3- We suggest to include an array of light emitters and receivers instead of a single light 

emitter/receiver. The use of arrays, in this case, will contribute to decreasing the high-frequency 

measurement fluctuations caused by bubbles and irregular oil films floating on the water 

surface.

4- The calibration of the sensor to different oils should be made more flexible to allow more/less 

data points.

5- We believe the sensor can be further developed to reach TRL 6 but there are operational 

considerations that need to be addressed such as easier deployment, protection from floating 

debris, and reducing the buoy size.

6- Similar to the electrical design enhancements discussed in the capacitive sensor section, we 

suggest that the wireless communication, storage, and processing features be enhanced.
Page 149

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Appendix

Note: All dimensions are in centimeters

Figure 145: Capacitive sensor package iteration 1

Figure 146: Alpha capacitive sensor package (assembly)
Page 152

Figure 147: Alpha capacitive sensor package (controller board compartment)

Figure 148: Alpha capacitive sensor package (sensor card lip seal)
Page 153

Figure 149: LED based sensor top cap

Figure 150: Spectro sensor holder
Page 154

Figure 151: LED case

Figure 152: LED case cap
Page 155

Figure 153: Outdoors lab tank