Summary of Reliability Assessment Practical Examples
The Liaison Council of Three Ministries on Disaster Prevention of Petroleum Complexes（Ministry of Economy, Trade and Industry; Fire and Disaster Management Agency; and Ministry of Health, Labour and Welfare）
March 2021

* This document summarizes the contents regarding reliability evaluation from the seven AI
development cases listed on the Practical Examples of Reliability Assessment (published separately).
Details include system overview, relationship to other systems, architecture of machine learningcomponent, and setting of qualities.

Table of Contents
1. Prediction of Pipe Wall Thickness (Yokogawa Electric Corporation)

2

2. Pipeline image diagnosis (Mitsubishi Chemical Corporation / NEC Corporation)

7

3. Equipment Deterioration Diagnosis (Yokogawa Electric Corporation)

12

4-1. Prediction and Diagnosis of Abnormality (Chiyoda Corporation / Seibu Oil Company Limited)17

4-2. Prediction and Diagnosis of Abnormality (JGC Japan Corporation)

22

5-1. Optimization of Operation (ENEOS Corporation / Preferred Networks, Inc.)

27

5-2. Optimization of Operation (Yokogawa Electric Corporation / JSR Corporation)

32

1

1．Prediction of Pipe Wall Thickness

Yokogawa Electric Corporation

21. Prediction of Pipe Wall Thickness —Case Overview―•••

This is an ML based system that predicts the current pipe wall thickness by estimating the amount of wall thinning
based on process data, etc. The purpose of this system is to ensure safety by responding to rapid corrosion
between periodic inspections, and to improve maintenance efficiency and reduce lost profit by reducing excessive
inspection and replacement work.
The functional requirement set is "Predicting pipe wall thickness by estimating the amount of wall thinning."
ML components predict the current pipe wall thickness by estimating the amount of wall thinning and output
predicted values of wall thickness to maintenance engineers. Maintenance engineers use this output and various
pieces of sensor data to determine whether an actual measurement of wall thickness is necessary.

Before introduction

After introduction

Past process data
Measured value

Maintenanceengineer
Maintenanceengineer

Visual check

SensorPipingcontents,
flow rate,etc.

Check ofleakage by
patrolIn operation
Input

Training

Testing

Sensor data

Training
data

Test
data

Sensordata

Periodic
measurement ofwall thickness

In development

ML components

Plant
Decision for
actualmeasurement ofwall thickness
Maintenanceengineer

Plant

3

OutputOutputPredicted pipe
wall thickness

Predicted pipe
wall thickness

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

1. Prediction of Pipe Wall Thickness —Relationship with Other Systems―
ML components
Input A
Piping contents,
flow rate andother data

Input B

Process
Estimation of theamount of pipe
wall thinning

Process

OutputDecision

Predicted wall
thickness value

Decision

Final
Decision

Overwriting
and correction
by logic

OutputExternal safety mechanism(E.g. Monitoring with rule-based indicators, monitoring with inspection
equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(E.g. Interlocks, alarm systems, etc.)

Input C

Alarm,
shutdown,
etc.

Process

4

Check

Final
Decision

Decision

Final
Decision

Human

(1)
(2)
(3)

Decision for actual
measurement ofwall thickness

Whether the predictedvalue exceeds the required
threshold for actual wall
thickness measurement

1. Prediction of Pipe Wall Thickness—Composition of ML Components and Setting the Quality in Use and the External Quality―<Composition of ML components>
Learning methodology

Supervised regression

The relationship between the piping content type, flow rate, flow velocity, pressure,etc., which may affect pipe wall thinning, and the amount of wall thinning is learned.
Process data (data on contents, flow rate, flow velocity, material and pressure of
Input data in operation
piping)Training data inProcess data (data on contents, flow rate, flow velocity, material and pressure ofdevelopment
piping), thickness data (actual measured values)
Process data (data on contents, flow rate, flow velocity, material and pressure of
Test data in development
piping), thickness data (actual measured values)

Learning model<Setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidance
Do not overlook a state that requires actual measurementof wall thickness by a maintenance engineer.

Keep errors in predicting wall thickness to be thicker than it
actually is within certain limits

Performance
Keep errors in predicting wall thickness to be thinner than it
actually is within certain limits

Eliminate unnecessary maintenance51. Prediction of Pipe Wall Thickness— Details of Case (Role of AI System and Effects of Its Adoption)―Role of<Role
of AI
AI>system•

So far, the amount of pipe wall thinning has been checked by periodic inspections. The amount of pipe wall thinning does notalways progress steadily over time, but sometimes rapidly. Therefore, piping has had to be replaced earlier than originallyplanned. Replacing piping sooner than its service life incurs lost profit.

•

Introducing an AI system to estimate the corrosion state of piping makes it possible to detect rapid wall thinning by knowing theamount of pipe wall thinning in real time, something that has only been checked periodically. This can optimize the piping
replacement timing and also prevent lost profit by replacing piping according to the deterioration state.

Effects•

Because pipe wall thickness, which was traditionally checked by periodical inspections, can be predicted and detected, thepiping replacement timing is optimized in real time.

•

Optimizing the piping replacement timing makes it possible to use piping for a longer period than now, leading to a reduction inlost profit.
(1) Pipe wall thickness estimation model
In: Process data (flow rate, temperature,pressure, etc.)
Out: Estimated value of pipe wall thickness(2) Model to predict future pipe wall thickness
In: Output value of the estimation model (1)Out: Predicted pipe wall thickness after 30
days

The starting point of prediction.
Pipe wall thickness after 30 daysis estimatedSystematized

Measured pipe wall thickness
Pipe wall thickness values estimated by the model (1)Future pipe wall thickness values predicted by
the model (2)6２．Pipeline image diagnosis

Mitsubishi Chemical Corporation/NEC Corporation

7

２. Pipeline image diagnosis—Case Overview―•

This is an ML based system for automatically judging the degree of corrosion of piping beam contact parts
based on image data in order to prevent overlooking corrosion in a visual inspection.

•

The functional requirement set is “Automatically judge the degree of corrosion at piping beam contact parts.”•

Based on images of piping beam contact parts, ML components judge the degree of corrosion on those partsand output the result of the degree of corrosion to the maintenance engineer. The maintenance engineer checks
the image data of the parts deemed "corroded" and judges the feasibility of a field visual inspection. This isoperated in parallel with corrosion management by patrol to prevent overlooking corrosion.
Before introduction

After introduction

CameraMaintenanceengineer

In operation

Maintenanceengineer
Visual check

Visual check

Judgment of thedegree of piping
corrosion bypatrolJudgment of thedegree of piping
corrosion bypatrolIn development

Input

Training

Testing

Piping image
data

Training
data

Test data

PipingImage data

ML components

Plant

Visual check

OutputJudgment
result of thedegree ofcorrosion

Judgment of thedegree of piping
corrosion

Plant
Maintenanceengineer8

OutputJudgment
result of thedegree ofcorrosion

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

２. Pipeline image diagnosis—Relationship with Other Systems―
ML components
Input A
Image data of
piping beam
contact parts

Process

OutputClassification of
the degree ofcorrosion

Degree ofcorrosion
A/B/C

※

Final
Decision

Decision

Decision

In parallel with theinspection by patrol, check
whether the output is notoverlooked from the imageof A/B.

Judging the degree ofFinalcorrosion by visual
Check
Decision
inspection

Decision

Input B

Process

Overwriting
and correction
by logic

OutputExternal safety mechanism(E.g. Monitoring with rule-based indicators, monitoring with inspection
equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(E.g. Interlocks, alarm systems, etc.)
Input C

Alarm,
shutdown,
etc.

Process

9

Final
Decision

(1)

※

(2)
(3)

Human
*In a potential case where “no confirmationfrom maintenance engineers is to be madeto C judgment,"the evaluation of“avoidability by human" becomes (1) and(2).

2. Pipeline image diagnosis― Composition of ML Components and Setting the Quality in Use and the External Quality―<Composition of ML components>
Learning methodology

Classification (supervised)

Learning model

Classification model that classifies the degree of corrosion based on the piping
image characteristics

Input data in operation

Image data of piping beam contact parts

Training data in development Image data of piping beam contact parts + Label of the degree of corrosion
Test data in development

Image data of piping beam contact parts + Label of the degree of corrosion<Setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidanceJudge an appropriate degree of corrosion even in images
taken under different photographing conditions so that parts
requiring a detailed inspection are not overlooked.

Minimize the rate of false negatives, which judge A (Response
required)/B (Follow-up required) as C (No problem) because thejudgment of the 3 levels of A/B/C varies in the judgment of corrosion
using images taken under different photographing conditions as inputs.

Performance
Do not judge parts that do not require detailed inspection as
requiring inspection

When the degree of corrosion is judged with the 3 levels of A/B/C, keep
the false positives that C (No problem) is judged to be A (Response
required)/B (Follow-up required) within certain limits.

10

2. Pipeline image diagnosis ―Details of Case (Role of AI System and Effects of Its Adoption)―Role of<Role
of AI
AI>system•

In conventional corrosion management by patrol, there were concerns of potential oversights at inspection points due to thearea to inspect being extremely large, and the fact that corrosion is hard to notice. In addition, inspection has depended on theskills of the inspector because the corrosion is to be evaluated based on qualitative criteria.

•

Introducing an AI system that can automatically determine the degree of corrosion enables comprehensive primary inspections(screening of visual inspection objects) over a large area. At the same time, since a single set of corrosion judgment criteria
can be applied universally across inspections, oversights due to variations in the skills of inspectors can be avoided.

[Example of validation data]

Effects
•

•

•

•

•

Image A

By judging the degree of corrosion through image diagnosis, it ispossible to level out the judgment criteria and prevent
oversights due to variations in the skills of inspectors.
It is no longer necessary to carry out field visual inspections
sequentially in large areas, thereby reducing the workload of theinspectors.
By performing conventional corrosion management through patrol
at the same time, inspections can be double-checked to detecthuman errors.
Younger inspectors can view and consult the output of image
diagnosis constructed from inspection results of experienced
inspectors. In this sense, the system can be used in passing on
know-hows in corrosion inspection.
By accumulating image data and information on the judgment
results of the degree of corrosion, changes over time can besystematically managed. This leads to enhanced equipment
management by analyzing the corrosion tendency.
11

Image B

Image CNew system to inspect piping beam contact part
- Registration of inspection results -Temporarily
saveComplete theinspection

Date and time of inspection June 29, 2018
Pipe No(1)-1

Press [Complete theinspection] to return to thesummary screen

Judgment
Tap to activate the tablet camera
and register the image taken

Situation<No image registered>3. Equipment Deterioration Diagnosis

Yokogawa Electric Corporation

12

3. Equipment Deterioration Diagnosis —Case Overview― *The system currently targets catalysts among equipment.
•••This is an ML based system that determines whether catalysts have deteriorated, for the purpose of developing amaintenance plan based on catalysts deterioration patterns and shortening shutdown period.
The functional requirement set is “Predicting the deterioration of catalysts.”
The ML components judge whether catalysts have deteriorated, and if judged so, an alert is sent to themaintenance engineer. The maintenance engineer then judges whether catalysts need to be inspected or replaced
based on the sent information and the equipment operation data.

Before introduction

After introduction

SensorAmount of
impurities,
etc.

SensorEquipment
operationdata

Amount of
impurities,
etc.

Maintenanceengineer

In operation

Equipment
operation
data

Plant

Plant

Past process data

Decision on
replacement

Decision on
replacement

Maintenanceengineer

13

Input
Equipment
operationdata

In development
Training

Testing

Training
Data

Test data

ML components
OutputOutputJudgment on
catalysts’
deterioration

Judgment on
catalysts’
deterioration

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

3. Equipment Deterioration Diagnosis —Relationship with Other Systems―
ML components
Input A

Process

OutputDecision

Equipment
operation data

Trends indeterioration/
nondeterioration

Deteriorating
or notDecision

The timing of replacement
and maintenance isconsidered based on
results of predicting the
Checkcatalyst deterioration
status

Decision

Input B

Process

Overwriting andcorrection bylogic

OutputExternal safety mechanism(Example: Monitoring with rule-based indicators, monitoring with inspection equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(Example: Interlocks, alarm systems, etc.)
Input C

Alarm,
shutdown,
etc.

Process

14

Human

Final
decision
Finaldecision
Finaldecision

(1)
(2)
(3)

Develop a plan for anddetermine the necessity of
replacement/maintenance3. Equipment Deterioration Diagnosis—Composition of ML Components and Setting the Quality in Use and the External Quality―<Composition of ML components>
Learning methodology

Learning model
Input data in operation
Training data in development
Test data in development

Supervised classification
Learns the degree of deterioration of catalysts from process data at the time ofdeterioration and non-deterioration
Process data (reactor temperature, pressure, composition of feed, etc.)
Process data (reactor temperature, pressure, composition of feed, etc.),
maintenance information
Process data (reactor temperature, pressure, composition of feed, etc.),
maintenance information<Example of setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidance--

Performance
The state of deterioration should be estimatedcorrectly.

The classification errors between "deterioration" and"no deterioration" are kept to a certain level.
The result of judging the change from "nodeterioration" to "deterioration" is output before apredetermined time.

The progress of deterioration is judged earlyenough to develop a maintenance plan.
15

3. Equipment Deterioration Diagnosis―Details of Case (Role of AI System and Effects of Its Adoption)―Role of<Role
of AI
AI>system•

Maintenance for replacement and activation of catalysts is determined based on the amount of impurities and experience of
maintenance engineers, but it has been difficult to plan the maintenance timing in advance. The replacement of catalysts
requires a shutdown, during which the plant stands offline. Therefore, the shutdown period needs to be as brief as possible. In
order to achieve this, it is necessary to make a maintenance plan in advance and implement it systematically, but such measure
has not been feasible so far.

•

By using plant data to diagnose catalyst deterioration, the replacement timing can be considered based on the deterioration
tendency and a maintenance plan can be made in advance. Thus, the shutdown period can be shortened.

Effects•

By diagnosing the deterioration state of catalysts, a maintenance plan can be made in advance.

•

By making a maintenance plan in advance, the shutdown period can be shortened.

•

By using the system to assist inexperienced maintenance engineer, the judgments criteria between different engineers canbe standardized regardless of experience.
Maintenance period for replacement and activation of catalysts

State of catalysts

High catalytic
performance

Low catalytic
performance
Time

16

4-1. Prediction and Diagnosis of Abnormality

Chiyoda Corporation / Seibu Oil Company Limited17

4-1. Prediction and Diagnosis of Abnormality —Case Overview―<Case overview>•

By analyzing IoT sensors in combination with operational historian data (process data), abnormalities in devices
and instruments, as well as operations that could lead to abnormalities, are detected at the predictive stage withhigh accuracy.

•

The functional requirements set are “Detect abnormalities early with high accuracy under various operating
conditions” and “Identify the reason for abnormality.”•

ML components judge the degree of abnormality based on various operating conditions, and output signs ofabnormality to the operator. The components also determine which area and system within the process system issuffering abnormality. In response, the operator judges whether to change the operation or checks and repairs
devices and instruments considered abnormal to prevent accidents.
After introduction

Before introduction

Past process data

SensorSensorOperatingconditions

Instrument
failure, or
processabnormality

Plant
Plant
Repair of
instrumentsor change of
operation

In operation

Equipment
operation
data

Maintenanceengineer
Repair of
instrumentsor change of
operation

Identificationofabnormalities

Alert system andshutdown system

18

Maintenanceengineer

In development

Input

Training

Testing

Equipment
operation data

Training
data

Test
data

ML components
OutputOutputSigns of instrument
and processabnormality

Signs of instrument
and processabnormality

Limitation of
failure area

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

4-1. Prediction and Diagnosis of Abnormality —Relationship with Other Systems―
ML components
Input AIoT sensor data+
Operating data

Process

OutputJudge if it is asign ofabnormality

Presence/absen
ce of a sign ofabnormality

Final
decision

Decision

Decision

Check

Final
decision

Decision

Finaldecision

(1)
(2)

(3)

Abnormality

Input B

Process

Overwriting andjudgment
correction by
Human
logic

OutputExternal safety mechanism(Example: Monitoring with rule-based indicators, monitoring with inspection equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(Alert system/shutdown system, etc.)

Input C

Alarm,
shutdown,
etc.

Process

19

Repair of
instruments orchange ofoperation

4-1. Prediction and Diagnosis of Abnormality —Composition of ML Components and Setting
the Quality in Use and the External Quality―<Composition of ML components>
Learning methodology

Supervised regression

Learning model

Predict the value of the target sensor from the operation data and sensor data

Input data in operation

Maintenance data (vibration value and valve opening), operating data (pressure,temperature, flow rate, properties, physical calculation values, etc.)
Maintenance data (vibration value and valve opening), operating data (pressure,temperature, flow rate, properties, physical calculation values, etc.)
Maintenance data (vibration value and valve opening), operating data (pressure,temperature, flow rate, properties, physical calculation values, etc.)

Training data in development
Test data in development<Example of setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidance
Abnormalities are detected early and correctly
under various operating conditions

Minimize false negatives where abnormal cases areclassified as normal.

Performance
Minimize false alarms and the workload ofoperators and inspectors
A deterioration tendency is predicted early enoughto respond to abnormalities.
The cause of abnormality can be narrowed down
and reduce down time.
20

Maintain false positives where normal cases areclassified as abnormal under certain threshold.
Abnormality detection results are output before apredetermined time.
Maintain accurate prediction rates of the cause abovecertain threshold.

4-1. Prediction and Diagnosis of Abnormality—Details of Case (Role of AI System and Effects of Its Adoption)―Role of<Role
of AI
AI>system•

When an abnormality alarm is raised, determining the cause of the alarm remains difficult; as such, identifying the cause of thealarm and determining the response relies heavily on experienced operators. The existing diagnostic tools can hardly detect
abnormalities until occurrence of failure (the initial stage of failure), and a certain amount of false detection exists.

•

By introducing an AI system that learned operation data and safety data, and that quickly detects abnormalities under various
operating conditions, abnormality prediction in advance before initial signs of abnormality, can be achieved. This reduces risks
of failure and loss of production opportunity more than ever. The AI system allows extensive overview of safety data andoperation data combined and enables cause identification, determining the response, and early actions without relying on
experienced operators.

Effects••••

The whole system process abnormality monitoring and safetyassessment AI makes it possible to find not only failure ofdevices and instruments, but also abnormal behavior of thewhole process equipment, resulting in improved accuracy inlocating abnormalities (the cause of abnormality can be
narrowed down) and early response.
In addition, the system enables real-time monitoring of thestatus of devices and instruments in the same system,
reducing the frequency of operator patrol and field
workloads.
By monitoring and assessing the impacts of process fluctuations
on devices and instruments, incidence of device failures canbe reduced without reducing productivity, and this in turn
reduces risks of failure and opportunity costs.
When an alarm is raised, the judgment of whether it is aprocess abnormality or device/instrument abnormality has been
dependent on experienced operators. However, the AI allows
inexperienced operators to take accurate and early actions.
21

Detection and prediction of process abnormality(process imbalance)

Prediction
Early detection and prediction of abnormalities ofdevices and instruments
Current actions

Abnormality
prediction

Actions after starting operation

4.2 Prediction and Diagnosis of Abnormality

JGC Japan Corporation*This practical example has the features of use cases of not only "prediction anddiagnosis of abnormality" but also "prediction of pipe wall thickness," and the reliability
assessment is carried out by referring to both use cases.
22

4-2. Prediction and Diagnosis of Abnormality —Case Overview―<Case overview>• This is an ML based system that detects signs of abnormality in piping from operation data and environmental
changes in order to avoid trouble caused by piping abnormality.
• The functional requirements set are “Clarify a corrosion point and send an alarm when an abnormal sign isdetected” and “Output the variables and numerical values that caused the abnormality prediction.”• ML components judge the corrosion rate from operation data and environmental changes, and provide the result
to maintenance engineers. The maintenance engineers promptly take measures to prevent trouble in responseto the result (incl. changing the operation in coordination with operators). The system also allows maintenancebased on prediction, preventing accidents in advance.
Before introduction

After introduction

Past process data
Measured value

Sensor

Maintenanceengineer

Pipingcontents,
flow rate,etc.

In operation

Sensordata

Predict piping
abnormality

Input
Operatingenvironment data

In development
Training

Testing

Training
Data

Test
data

ML components

Plant
OperationchangeJudge whether
to replacepipingMaintenanceengineer

Plant
Operator

23

OutputOutputPrediction of pipe
wall thickness, andcorrosion rate

Results of predicting
pipe wall thickness,and corrosion rate

Existing
corrosion
calculation logic

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

4-2. Prediction and Diagnosis of Abnormality —Relationship with Other Systems―
ML components
Input A
Operatingenvironment data

Input B

Process

OutputCorrosion rate
calculation

Process

Predicted value
from AI

Final
decision

Decision

Decision

External safety mechanism(Existing corrosion calculation logic)

Plant facilities

Safety-related systems independent of the ML based system(Examples: Interlocks, alarm systems, etc.)

Input C

Alarm,
shutdown,
etc.

Process24(2)

Output advice on the corrosion
Checkstate on the dashboard.

Finaldecision

Decision

Finaldecision

Human

Operationchangejudgment/piping
replacement

Overwriting
and correction
by logic

Output(1)(3)

4-2. Prediction and Diagnosis of Abnormality—Composition of ML components, Quality in use and Setting the external quality―<Composition of ML components>
Learning methodology

Regression (supervised learning) *To predict continuous values of wall thickness

Learning model

Actual measurements of corrosion rate in the past rate in the past are set as an objective
variable, and piping specifications and process data as explanatory variables.

Input data in operation

Process data (contents, flow rate, flow velocity, pressure, temperature, pH, etc. of piping)Training data in development

Process data (contents, flow rate, flow velocity, pressure, temperature, pH, etc. of piping)
Records of thickness measurement

Test data in development

Process data (contents, flow rate, flow velocity, pressure, temperature, pH, etc. of piping)
Records of thickness measurement<Examples of setting quality in use and external quality>
Quality in Use

External Quality

Risk avoidance
Not to make a prediction on the hazard side with actually
To minimize errors of predicting a thickness value larger than themeasured C.R. larger than predicted C.R. (C.R: Corrosion Rate) actual value.
The variables that have affected the prediction value are
To minimize errors that erroneously outputs variables that affected asuggested so that the cause of the abnormality prediction can beprediction value.
identified.
When an abnormal sign is detected, the timing of the alarm will
be set to ensure safety in such a way as to prevent a serious
Alarm is issued within a set time (○ hours in advance).
incident from occurring.
Performance
Set the alert frequency to a reasonable level that operators andinspectors do not need to spend too much time resource inchecking the contents of alerts.

25

Keep errors in predicting wall thickness to be thinner than it actually is
below certain limits.

4-2. Prediction and Diagnosis of Abnormality—Details of Case (Role of AI System and Effects of its Adoption)―Role of<Role
of AI
AI>system•

Traditionally, control items and values in operation conditions were set based on data such as analysis from simulators. However,from a plant-extensive point of view, the issue remained that the progression of corrosion was unclear, due to lack ofknowledge on the relationship between fluctuations in real operation data and corrosivity.

•

By introducing an AI system that can predict the progression of piping corrosion, condition-based predictions remain available
even when corrosivity increases due to changes in the environment, allowing for more reliable decisions at all times. If sudden
corrosion is predicted, maintenance engineers and operators can respond accordingly to prevent problems.

Effects
•

•

•

•
•

By conducting analysis using AI, control items and criteria for
operation can be set after clarifying the correlation between
process fluctuations and corrosion. This not only improves
operation efficiency but also reduces the frequency ofissues.
Early warning can be made possible by clarifying operating
conditions in normal and non-normal states, and conducting
HOME screen
predictive diagnosis for pipe corrosion.
Decisions of high accuracy becomes possible at all times,
with automatic data collection not relying on human labor andcondition-based predictions.
Equipment
By providing support using AI, even young maintenanceselectionengineers can make highly accurate decisions at all times.
Decisions of experienced maintenance engineers will be fed into
the AI, allowing for the passing down of their expertise andtechniques.
Prediction
result26

Planning support

5-1. Optimization of Operation
ENEOS Corporation / Preferred Networks, Inc.

27

5-1. Optimization of Operation —Case Overview―<Case overview>• This is an ML based system that automatically outputs optimum operation parameters from process data for the
purpose of realizing automatic operation with production efficiency and energy efficiency equal to or better thanoperations of experienced operators. This is achieved by stabilizing equipment fluctuations due to disturbances,such as outside air temperature and weather, at all times.
• The functional requirements set are “Operate steadily during equipment fluctuations due to disturbances” and“Operate with optimized production and energy efficiency during normal conditions.”• From process data, ML components output operation parameters that realizes overall optimization of production
efficiency and energy efficiency within a range of allowable safety operation. The output is directly reflected inplant operation.
Before introduction

After introduction

Past process data
Simulator

SensorSensorState ofequipment andthe environmentState ofequipment andthe environment

Process
data, etc.

Plant
Operator

Plant

Interlock, safetyvalves, etc.
Statusmonitoring

Interlock, safetyvalves, etc.

28

Statusmonitoring

Parametersandenvironment

In operation

In development

Input

Training

Testing

Process
data, etc.

Training
Data

Test data

ML components
OutputOutputOperatingparameters

Operatingparameters

Numbers (1) to (3) indicate "Avoidability byhuman." See Figure 2-7 in Guideline item 2.2.3.

5-1. Optimization of Operation —Relationship with Other Systems―
ML components
Input A

Process data

Input B

Process
Calculate the optimum valueof an operating parameterthat stabilizes fluctuations
caused by disturbances andoptimizes production volumes
and energy efficiency.

Process(1)

OutputOptimum values
in operating
parameters

Decision

Overwriting andcorrection bylogic

OutputExternal safety mechanism(Example: Monitoring with rule-based indicators, monitoring with inspection equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(Example: Interlocks, alarm systems, etc.)

Input C

Alarm,
shutdown,
etc.

Process

29

Check

Final
decision

Decision

Finaldecision

Human

(2)

(3)

5-1. Optimization of Operation —Composition of ML components and setting the external quality―<Composition of ML components>
Learning methodology Regression (supervised)
Learn the relationship between historical process data and future objective functions (product properties,
Learning model
flow rate, etc.)
Input data inProcess data (time series data of fluid properties, and the flow rate, temperature, pressure, and liquid level
operationof equipment)
Training data inProcess data (time series data of fluid properties, and the flow rate, temperature, pressure, and liquid level
developmentof equipment)
Test data inProcess data (time series data of fluid properties, and the flow rate, temperature, pressure, and liquid level
developmentof equipment)<Setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidance
Not to perform an operation that exceeds the equipment design
limitation.

To limit the output operation range of AI to a range of allowable safety
operation.

To indicate that the present operation zone is within or outside
the trained range of AI. (output "this is an unknown field")

To reduce to a certain level the misjudgment rate where the systemjudges that the operation zone is within trained range when it isactually not.

Performance
To minimize the number of times the operator responds toalarms.
To realize optimum control in consideration of both production
efficiency and energy efficiency when conditions are steady.

30

To take the number of whistle alarm blows of the existing system(DCS) caused by fluctuations due to disturbances to as close to zero
as possible.
To indicate an operation parameter that keeps an indicator, calculated
by converting production efficiency and energy efficiency into amonetary value, higher than a certain level.

5-1. Optimization of Operation —Details of Case (Role of AI System and Effects of its Adoption)―Role of AI system•

The number of fires and leakage accidents at dangerous facilities in Japan remain unchanged, and reducing equipment
troubles was an urgent issue to be addressed. In addition to a decline in technical capabilities due to an expected decrease in
the number of experienced operators in the future, there was a concern that it securing qualified operators will be difficult
due to a fall in the domestic workforce.

•

By using the ML based system for monitoring and operation, human-factor-based troubles in equipment (e.g. caused byoperation mistakes) can be reduced. In addition, by using the ML based system, it will be possible to maintain optimum
operation better than that of experienced operators with minimum human resources.

Effects
•
•

•
•

Now

Equipment troubles through human monitoring and operation
can be reduced.
By allocating human resources freed up by shift to automation
to equipment abnormality detection, equipment troubles
caused by physical factors can also be reduced.
It will be possible to maintain optimized operation in a waythat is better than that of experienced operators.
Since the plant will be able to operate with a smaller number ofpeople, continuous operation can be expected even in an
emergency such as when an infectious disease spreads.

: Monitored and operated by people on a 24/7 basis.
Grasp theongoing
situation

Prediction&
Decision

Operator
FuturevisionPrediction&
Decision

Operation
panel

Equipment
data

Equipment

Operation
signalOperation
input: Automatically controlled by AI
Equipment
data

Operation
input

Operation
panel

Equipment
data

Equipment

Operation
signalCurrent Status and Future Vision of Plant Operation31

5-2. Optimization of Operation
Yokogawa Electric Corporation / JSR Corporation32

5-2. Optimization of Operation —Case Overview―<Case overview>•••

This is an ML based system that automatically outputs optimum operating parameters from process data for the purpose ofmaintaining quality by detailed operation and achieving higher energy efficiency.
The functional requirements set are “Use AI control to perform operations equivalent to manual operations” and “Aim for higher
energy efficiency while maintaining quality through automatic operation in more detail than the operator’s manual operations.”
From process data, ML components calculate operating parameters optimum for energy-saving operation within a range
corresponding to allowable safety operating specifications, and provide them to the operator. The operator judges whether the
output of the ML components is appropriate based on experience and determines the operation of the plant.

Before introduction

After introduction
Past process data

Equipment
data, etc.

Equipment
data, etc.

Sensor

Simulator

In operation

SensorState ofequipment
and theenvironmentState ofequipment
and theenvironmen
t

DCS

DCS

Input

Training

Testing

Equipment
data, etc.

Training
Data

Test data

ML components

Operator

Plant

In development

Plant
OutputDecision
Operation

Operatingparameters
Operator
Statusmonitorin

Statusmonitorin

33

Decision
Operation

OutputOperatingparameters

Numbers (1) to (3) indicate "Avoidability by human."See Figure 2-7 in Guideline item 2.2.3.

5-2. Optimization of Operation —Relationship with Other Systems―
ML components
Input A

Process data

Input B

Process
Calculate optimum
operating parametervalues that realize
energy saving

Process

OutputDecision

Optimum values inoperatingparameters

Decision

Is a presented
operating parameter
appropriate?

Overwriting
and correction
by logic

OutputExternal safety mechanism(Example: Monitoring with rule-based indicators, monitoring with inspection equipment, etc.)

Plant facilities

Safety-related systems independent of the ML based system(Example: Interlocks, alarm systems, etc.)

Input C

Alarm,
suspension,
etc.

Process

34(1)

Determine the actual
Final
operatingdecisionparameters

Check

Final
decision

Decision

Finaldecision

Human

(2)

(3)

5-2. Optimization of Operation—Composition of ML Components and Setting the Quality in Use and the External Quality―<Composition of ML components>
Learning methodology

Reinforcement learning

Learning model

Learns optimum operating methods for energy efficiency based on process data

Input data in operation

Process data (data of actual equipment, such as the amount of feed to the
distillation column, temperature, and the amount of generated materials)

Training data in development Process data (data of the process simulator)
Test data in development

Process data (data of actual equipment for the past year)<Example of setting the quality in use and the external quality>
Quality in Use

External Quality

Risk avoidance
Operating conditions that exceed the allowablesafety operating specifications of the equipment
are not caused.

The output range is limited to a range corresponding toallowable safety operating specifications.
The prediction accuracy of operating parameters is
kept at a certain level or higher.

Output is stable under various conditions.

Performance
Higher energy efficiency than pre-AI operation isachieved.

Operating parameters that keep energy efficiency
indicators above a certain level are presented.
35

5-2. Optimization of Operation —Details of Case (Role of AI System and Effects of Its Adoption)―Role of<Role
of AI
AI>system•

In the operation of this plant, PID control and multivariable model predictive control are used for automation andstabilization. However, some processes vulnerable to disturbances require manual control by human operators. This
leaves some elements human-dependent.

•

If the operation can be automated by using ML components, continued high-quality operation with high energy efficiency
can be achieved.

Effects•••

It is possible to reduce the number of human resources
currently deployed 24 hours a day, thereby reducing theburden on people.
It eliminates factors that depend on individual skills causedby human operation, making it possible to maintain
operation quality.

AI control model generated bytrial and error using simulator
Operated as a reference system;
the decision to use indicatedvalues is made by humans
Plant simulator

By maintaining high-quality and energy-efficient operation,
the energy consumption rate can be reduced further,
contributing to the society's effort of energy conservation.

Optimization of plant operation
by reinforcement learning AI control algorithm36