# Influences of Aqueous Nanofluid Emulsion on Diesel Engine Performance, Combustion, and Emission: IoT (Emission Monitoring System)

Authors: B. Venkatesh; J. Chinna Babu; Sandeep Kumar Mathivanan; Prabhu Jayagopal; S. Prasanna; Md Salah Uddin
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

Pollution is the primary ecological threat to nature and it also raises the temperature of the Earth. The primary cause of air pollution is exhaust emissions from the combustion chamber. This study aims to focus on controlling emissions using an aqueous nanoemulsion method and to analyze positive vibrations for the reduction of pollutants in diesel engines. In this study 3% NWED (nano-based water emulsion diesel), 7% NWED, and 12% NWED emulsion blended diesel were mixed with Tween-20 as a solvent. In addition, this work was accomplished with a multiuser remote control system for the continuous monitoring of emissions with IoT technology applied to diesel engines. Experimental results have been measured with an IoT kit and five gas analyzers and reported that emission of oxides of nitrogen and hydrocarbons can be reduced by using a 7% NWED blend when compared with diesel. In addition, the experimental result also shows that the brake-specific fuel consumption is reduced with an improvement in thermal efficiency at 7% NWED when compared with other blends.


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## Body

## 1. Introduction

As the rate of fossil fuel depletion is increasing daily, there is a need to fulfill the energy demand for alternative fuels [1]. Since the 1970s, the use of energy resources such as oil, gas, coal, nuclear and hydro, has increased tremendously to meet energy demand [2]. The transport, industry, non-composted, and building sectors depend mainly on petroleum fuels [3]. Combustion emissions such as CO, CO2, HC, NOx, and smoke are released into the atmosphere through exhaust pipes [4]. These emissions generate chemical reactions in the atmosphere, creating a greenhouse effect [5]. Controlling pollution mixture formation in diesel engines has been the prime objective of many researchers, who have found alternative fuels that have not shown any modifications to the engine, by reducing emissions and improving engine performance [6]. Biodiesel has been studied worldwide as the dominant diesel fuel. To achieve better performance and lower pollution, researchers have selected different biodiesel feedstocks or varying engine parameters such as injecting opening pressures (IOPs), fuel injection timings, compression ratio (CR), and additives, such as diethylether and antioxidants [7]. As a result that biodiesel blends produced lower CO, HC, and smoke emissions than diesel fuel, while combustion exhaust gas temperature, CO2, and NOx emissions were higher [8]. Researchers based on several studies on diesel engines using alternative fuels have claimed that NOx emissions have increased owing to the high combustion and heat release rate inside the combustion chamber [9].In addition, several scholars have found that additives such as nanoparticles and water emulsions in diesel offer useful performance improvements by reducing the emissions of a diesel engine. In most cases, additives such as metal oxide particles are added to the base fuels to achieve secondary atomization and improve thermal efficiency. Additionally, these particles reduce unburnt hydrocarbon and carbon monoxide emissions [10]. Graphene, carbon nanotubes, cerium oxide, manganese, nickel oxide, and other metal oxides have been studied extensively. The presence of these dissolved metal oxide nanoparticles enhances combustion by promoting cleaner combustion and reducing ignition delay. Consequently, the engine-specific consumption is reduced [11, 12].Water and diesel are framed into emulsion mixtures by using surfactants [13, 14]. O/W, W/O, and multiple (O/W/O and W/O/W) emulsion methods are used to stabilize water and diesel [15]. W/O emulsion is a strong bonding for the stabilization of water emulsions [16]. During the combustion process, these suspended water particles absorb heat from the cylinder walls and reduce the cylinder temperature, resulting in a reduction in the oxides of nitrogen and particulate matter [17]. Water emulsion leads to a longer delay, resulting in potholed engine operation and a longer ignition delay [18, 19].Nanoparticles and Nanofluids act as a fuel additive to enrich the combustion rate and reduce emissions [20]. The addition of a nanocatalyst to diesel fuel improves fuel oxidation and reduces the ignition delay period time [21]. The doping of nanoparticles and the thermal brake efficiency of engine performance can be improved by reducing the emissions of CO, HC, and NOx [22]. In diesel engines, improved events in the heat transfer rate, and thermal conductivity of nano fluid and diesel blends are helpful and the blend preposition helps to form long-term stability and good dielectric property. When using nanofluids, NOx rates increase because of the high latent heat of vaporization [23].The Internet of Things technology is the most efficient system for continuous monitoring of emissions, but there have been many techniques and precautions implemented in order to control emissions. Internet of Things (IoT) technology is a revolutionary, wireless health monitoring tool that integrates embedded sensors, actuators, and RFID tags to improve operations, enhance daily activities, and monitor health care [24]. A wireless communication system is well suited to measure and interpret urban environmental indicators and natural resources. In the European Union, smart IoT technology is currently being used to monitor and update emission standards. Smart IoT systems continuously collect and transmit data to detect natural threats. Using web applications, it reports back to the user whether they are threats. Intelligent mobility (e.g., route stability, smart metro traffic, vehicle tracking, and Internet of vehicles), smart living (e.g., healthcare, tourism, education, buildings, and public safety), smart environment (e.g., monitoring of city air quality, air pollution, trees, water quality, green spaces, weather, waste collection, vehicles parking emission monitoring, management, and energy efficiency), smart citizens, smart government, smart economy, and smart architecture and technologies all benefit from it [25]. IoT can be used in various applications, such as monitoring and controlling press shop assembly, weather forecasting, prediction of plant leaf diseases, and food processing units [26, 27].The aforementioned literature concludes that a reduction of NOx for diesel engines has different methodologies. Among that nanofluid was used as a fuel additive because very rarely implemented in the engine literature studies for the reduction of NOx. Furthermore, this study was carried out to reduce emissions and continuously monitor the emissions with IoT as a future scope in a different way.The main objective of this work is to focus on controlling the emissions of oxides of nitrogen, hydrocarbons, and carbon monoxide as well as improving combustion by blending nanofluids in a diesel engine. The secondary objective of this work is to develop specific IoT technology aids for the continuous monitoring of emissions in diesel engines at regular intervals of time.


## 2. Experimental Setup and Process

### 2.1. Emulsion Fuel Preparation

Water is a polar substance, whereas diesel is nonpolar in nature. Both polar and nonpolar molecules are unbondable in nature. To obtain a homogenous mixture, a process called emulsification was implemented. Al2O3 nanoparticles dispersed in water emulsions significantly improve the properties of thermophysical combustion. The incorporation of nanoparticles into a base fluid is a vital process. Therefore, special care was required. The prepared nanofluid must be an agglomerate-free stable suspension for extended periods without sedimentation.Generally, for the preparation of nanofluids, the two-step method is optimistic, easy, and less expensive. The steps involved in preparing the nanofluid were as follows: a specific amount ofγ-Al2O3 nanoparticles, Tween-20, and distilled water. Distilled water was poured into a beaker and nanoparticles and Tween-20 were slowly poured and stirred with a magnetic stirrer. Water and Al2O3 were thoroughly mixed using an ultrasonicator for at least one hour to obtain a nanofluid. Figure 1 shows a flow diagram for the preparation of the nano-fluid using a two-step method. Figure 2 shows SEM and TEM Images of the aluminum nanoparticles.Figure 1
Procedure to prepare nanofluids using the two-step method.Figure 2
SEM and TEM image of Alumina nanoparticle.
(a)(b)After the preparation of the nanofluid based on the blending ratio, the aqueous nanofluids were titrated with diesel fuel. A 3% NWED, 7% NWED, and 12% NWED emulsion blended ratio was prepared for experimentation. The properties of the diesel and emulsion blend are shown in Table1.Table 1
Properties of fuel.
Test descriptionReferenceDieselAl2O3 water emulsion blendUnitLimitB03% NWED7% NWED12% NWEDDensityGm/cc0.800–0.9000.8320.8330.8460.854Calorific valueMJ/kg34–4542.7041.8539.3338.89


### 2.2. Experimental Equipment Specification

The investigation was conducted on a single cylinder naturally aspirated vertical water-cooled diesel engine with a rated power of 5.12 kW at 1500 rpm and a compression ratio of 17.5 : 1. Figure3 shows a detailed view of the diesel engine and explains the details of the engine that had been used for experimentation. The specifications and model names of the emission analyzers are also mentioned in detail in Tables 2 and 3.(i)
Diesel engine specification(ii)
Five gas analyzersFigure 3
Photographic views of diesel engine.Table 2
Engine model: Kirloskar engine TV 1.
TypeFour stroke, single cylinder vertical water-cooled diesel engineRated power5.12 kWRated speed1500 rpmBore dia (D)87.5 mmStroke (L)110 mmCompression ratio17.5 : 1DynamometerEddy current dynamometer make: Techno mech Model: TMEC-10Table 3
Details of five gas analyzer.
ModelAVL DI GAS 444 NMeasurement dataResolutionCo-0–15% vol0.0001% volHC-0-20000 ppm vol1 ppm/10 ppmNOx 0–6000 ppm vol1 ppm vol


### 2.3. Statistical Analysis

(a)
Brake-specific fuel consumption(1)Bsfc=Fuelflowinkg/HrBrakepowerkWX100.(b)
Brake thermal efficiency(2)BTE=BrakePowerkWX3600FuelFlowKg/HrXCalorificValueKJ/KgX100.The aforementioned equations were used for statistical analysis of brake-specific fuel consumption and brake thermal efficiency.


### 2.4. IoT Emission Setup

The Internet of Things is the ability to transmit data over a network, without the need for a computer, for humans or a human, provided by interconnected computing devices, mechanical and digital machines, objects, animals, or unique identifiers to each other. Initial deigning of the IoT kit used, Thinker CAD helps in executing the weather the algorithm suits the program. Figure4 shows the circuit diagram for multiple gas sensors connected to the emission monitoring device layout. Finally, using the Arduino board all emission sensors are connected, and the code is uploaded to the board which executes the program easily. Figure 5 shows the IoT setup of the emission monitor device.Figure 4
The circuit diagram for multiple gas sensors connected emission monitor device.Figure 5
Emission monitors device IoT setup.IoT requires a cloud for storing and sharing the data obtained from the kit. ThingSpeak is an open-source server that helps to transmit live emission data stored in the cloud and to visualize and analyze diesel emission results for analysts.


### 2.5. Uncertainty Analysis

There was no error-free experimental setup. Errors may occur owing to the inaccuracy of devices or instruments, inaccurate calibration, and lack of humanity; therefore, before starting any experimental work, uncertainty analysis is mandatory. To obtain the uncertainties of the independent and performance characteristics, a sequential perturbation technique was employed. These include the air flow rate (1.1%), engine load (0.1%), fuel flow rate (0.1%), and engine speed (1.3%). As a result, the calculated accuracy of the performance and combustion study for both nano-based water emulsion and diesel-run engines were found to be within ±3.6. However, the emission accuracy was within ±2.1%. Generally, the uncertainty calculation value is obtained using the following equation:(3)R=∑Xi2,where R= root mean square value of the uncertainty and Xi = parameter’s uncertainty values.The standard deviation formula is given as follows:(4)σ=∑i=1nxi−x−2N,where σ= standard deviation, xi = terms given in the data, x¯ = mean, and N = total number of terms.The standard error formula is given as follows:(5)SE=σN,where SE= standard error, σ= standard deviation, and N = total number of terms.Table4shows the error chart for brake thermal efficiency, brake-specific fuel consumption and emissions at regular intervals of time operation.Table 4
Error chart.
Brake powerBrake thermal efficiencyBrake-specific fuel consumptionCarbon monoxideHydro carbonsOxide of nitrogenSmokeTime of operation(kW)(%)(kg\kWh)(%)(Ppm)(Ppm)(%)(min)2.6127.730.310.0252799114.7302.6127.730.320.0262599014.6602.6127.740.290.0252799114.7902.6127.730.310.0252799214.71202.6127.740.310.0242699114.8180Mean2.6127.730.310.02526.499114.7Standard deviation00.0050.0110.00070.890.710.07Standard error00.0020.0050.00030.40.320.03Upper control limit2.6127.7450.3430.027129.67992.1314.91Lower control limit2.6127.7240.2950.0242599014.6


## 2.1. Emulsion Fuel Preparation

Water is a polar substance, whereas diesel is nonpolar in nature. Both polar and nonpolar molecules are unbondable in nature. To obtain a homogenous mixture, a process called emulsification was implemented. Al2O3 nanoparticles dispersed in water emulsions significantly improve the properties of thermophysical combustion. The incorporation of nanoparticles into a base fluid is a vital process. Therefore, special care was required. The prepared nanofluid must be an agglomerate-free stable suspension for extended periods without sedimentation.Generally, for the preparation of nanofluids, the two-step method is optimistic, easy, and less expensive. The steps involved in preparing the nanofluid were as follows: a specific amount ofγ-Al2O3 nanoparticles, Tween-20, and distilled water. Distilled water was poured into a beaker and nanoparticles and Tween-20 were slowly poured and stirred with a magnetic stirrer. Water and Al2O3 were thoroughly mixed using an ultrasonicator for at least one hour to obtain a nanofluid. Figure 1 shows a flow diagram for the preparation of the nano-fluid using a two-step method. Figure 2 shows SEM and TEM Images of the aluminum nanoparticles.Figure 1
Procedure to prepare nanofluids using the two-step method.Figure 2
SEM and TEM image of Alumina nanoparticle.
(a)(b)After the preparation of the nanofluid based on the blending ratio, the aqueous nanofluids were titrated with diesel fuel. A 3% NWED, 7% NWED, and 12% NWED emulsion blended ratio was prepared for experimentation. The properties of the diesel and emulsion blend are shown in Table1.Table 1
Properties of fuel.
Test descriptionReferenceDieselAl2O3 water emulsion blendUnitLimitB03% NWED7% NWED12% NWEDDensityGm/cc0.800–0.9000.8320.8330.8460.854Calorific valueMJ/kg34–4542.7041.8539.3338.89


## 2.2. Experimental Equipment Specification

The investigation was conducted on a single cylinder naturally aspirated vertical water-cooled diesel engine with a rated power of 5.12 kW at 1500 rpm and a compression ratio of 17.5 : 1. Figure3 shows a detailed view of the diesel engine and explains the details of the engine that had been used for experimentation. The specifications and model names of the emission analyzers are also mentioned in detail in Tables 2 and 3.(i)
Diesel engine specification(ii)
Five gas analyzersFigure 3
Photographic views of diesel engine.Table 2
Engine model: Kirloskar engine TV 1.
TypeFour stroke, single cylinder vertical water-cooled diesel engineRated power5.12 kWRated speed1500 rpmBore dia (D)87.5 mmStroke (L)110 mmCompression ratio17.5 : 1DynamometerEddy current dynamometer make: Techno mech Model: TMEC-10Table 3
Details of five gas analyzer.
ModelAVL DI GAS 444 NMeasurement dataResolutionCo-0–15% vol0.0001% volHC-0-20000 ppm vol1 ppm/10 ppmNOx 0–6000 ppm vol1 ppm vol


## 2.3. Statistical Analysis

(a)
Brake-specific fuel consumption(1)Bsfc=Fuelflowinkg/HrBrakepowerkWX100.(b)
Brake thermal efficiency(2)BTE=BrakePowerkWX3600FuelFlowKg/HrXCalorificValueKJ/KgX100.The aforementioned equations were used for statistical analysis of brake-specific fuel consumption and brake thermal efficiency.


## 2.4. IoT Emission Setup

The Internet of Things is the ability to transmit data over a network, without the need for a computer, for humans or a human, provided by interconnected computing devices, mechanical and digital machines, objects, animals, or unique identifiers to each other. Initial deigning of the IoT kit used, Thinker CAD helps in executing the weather the algorithm suits the program. Figure4 shows the circuit diagram for multiple gas sensors connected to the emission monitoring device layout. Finally, using the Arduino board all emission sensors are connected, and the code is uploaded to the board which executes the program easily. Figure 5 shows the IoT setup of the emission monitor device.Figure 4
The circuit diagram for multiple gas sensors connected emission monitor device.Figure 5
Emission monitors device IoT setup.IoT requires a cloud for storing and sharing the data obtained from the kit. ThingSpeak is an open-source server that helps to transmit live emission data stored in the cloud and to visualize and analyze diesel emission results for analysts.


## 2.5. Uncertainty Analysis

There was no error-free experimental setup. Errors may occur owing to the inaccuracy of devices or instruments, inaccurate calibration, and lack of humanity; therefore, before starting any experimental work, uncertainty analysis is mandatory. To obtain the uncertainties of the independent and performance characteristics, a sequential perturbation technique was employed. These include the air flow rate (1.1%), engine load (0.1%), fuel flow rate (0.1%), and engine speed (1.3%). As a result, the calculated accuracy of the performance and combustion study for both nano-based water emulsion and diesel-run engines were found to be within ±3.6. However, the emission accuracy was within ±2.1%. Generally, the uncertainty calculation value is obtained using the following equation:(3)R=∑Xi2,where R= root mean square value of the uncertainty and Xi = parameter’s uncertainty values.The standard deviation formula is given as follows:(4)σ=∑i=1nxi−x−2N,where σ= standard deviation, xi = terms given in the data, x¯ = mean, and N = total number of terms.The standard error formula is given as follows:(5)SE=σN,where SE= standard error, σ= standard deviation, and N = total number of terms.Table4shows the error chart for brake thermal efficiency, brake-specific fuel consumption and emissions at regular intervals of time operation.Table 4
Error chart.
Brake powerBrake thermal efficiencyBrake-specific fuel consumptionCarbon monoxideHydro carbonsOxide of nitrogenSmokeTime of operation(kW)(%)(kg\kWh)(%)(Ppm)(Ppm)(%)(min)2.6127.730.310.0252799114.7302.6127.730.320.0262599014.6602.6127.740.290.0252799114.7902.6127.730.310.0252799214.71202.6127.740.310.0242699114.8180Mean2.6127.730.310.02526.499114.7Standard deviation00.0050.0110.00070.890.710.07Standard error00.0020.0050.00030.40.320.03Upper control limit2.6127.7450.3430.027129.67992.1314.91Lower control limit2.6127.7240.2950.0242599014.6


## 3. Results and Discussion

### 3.1. Performance Analysis

The brake-specific fuel consumption (BSFC) of the engine using diesel and the three types of nano-W/D under different load conditions with constant speed are illustrated in Figure6. As shown in the graph, all three nano-W/D ratios show that BSFC at initial loads is high owing to the lower heating value in the presence of nano-based water emulsion and gradually reduces when the load is increasing, which indicates that the fuel burns in the engine effectively at higher engine loads. While running the engine with nano-W/D no identities are found in misfiring or unusual behavior. The potential benefit of water in diesel nanoemulsion fuels are as follows: (i) The overall impact of the secondary atomization and microexplosion phenomenon, resulting in strong evaporation and better mixing of air and changes in improving the combustion reactions inside the engine cylinders, (ii) Improved air-penetration in the spray due to increased spray momentum, (iii) Better-premixed combustion due to ignition delay, (iv) Increase in excess air ratio due to the presence of water in the emulsion, (v) Reduction of cooling loss due to a decrease in combustion temperature, (vi) Quash of thermal dissociation as a result of dropping in combustion temperature, and (vii) The presence of Al2O3 nanofluid in diesel emulsion that triggers less combustion gas release. Both 7% and 12% NWED blends show better fuel consumption, and it was observed that 7% NWED showed better load conditions. If the percentage of emulsion increases, the cylinder temperature decreases owing to the water percentage and its effect on combustion and emissions.Figure 6
Load vs. BSFC.Figure7 shows the variations in brake thermal efficiency of γ-Al2O3 with water and diesel test fuels under different engine loads. Compared with diesel and nano-based water emulsion blends, the blends showed an improvement in performance at higher loads. This is due to the addition of γ-Al2O3 in water and diesel at high temperatures; these mixtures turn into hydro-liquid fuel and emit high energy in the combustion process. Figure 8 clearly shows that the brake thermal efficiency increased at different concentrations of aqueous nanofluid emulsion on diesel engines with different loads. The percentage of aqueous nanofluid increased the brake thermal efficiency at higher loads.Figure 7
Load vs. BTHE.Figure 8
Load vs. CO.


### 3.2. Combustion Analysis

Figure9 shows the crank vs. cylinder pressure at full load with diesel, 3%, 7%, and 12% aqueous γ-Al2O3 emulsion blends. The peak pressures at full load on diesel are 71.99 bar, 71.82 bar, 72.17 bar, and 71.75 bar at 23°. BTDC injection timing. In addition, the cylinder pressure is improved owing to the better atomization of air and nano-W/D fuel particles during ignition, which helps to improve the heat release rate inside the engine cylinder.Figure 9
Crank angle vs. cylinder pressure.Figure10 shows the rate of heat released inside the combustion chamber with different fuel blends under full load conditions in diesel engines. The addition of γ-Al2O3 with water and diesel at the time of surface flame formation causes, water and nanoparticles to penetrate and convert water into hydrogen gas, and γ-Al2O3 procures a sufficient amount of oxygen for better atomization to improve the combustion rate. When compared with aqueous blends, the 7% NWED blend showed the best results.Figure 10
Crank angle vs. net heat release rate.


### 3.3. Emission Analysis

According to Zeldovich’s approach, the formation of nitrogen oxides depends on oxygen density, the time required for formation, and temperature. In water with high oxygen content, the water content within the inner phase absorbs the calorific heat charge of the emulsion. Consequently, this reduces the burning gasoline temperature during the combustion and consequently restricts the generation of NOx. At the time of combustion, the aqueous solution vaporized and tended to reduce the flame temperature. Figure11 shows the variation between NOx and the load. Compared with diesel, the emulsion blends showed little difference in the graph. This is because γ-Al2O3 reduces the cylinder wall temperature inside the engine chamber, thus restricting the NOx formation. An increase in the percentage of aqueous emulsions reduced the NOx level. The average reduction of 3%, 7%, and 12% NWED for every load were 31.46%, 38.76%, and 45.48%, respectively.Figure 11
Load vs. NOx.Figure12 shows the CO formation at various loads. The formation of CO depends on the air-fuel ratio. The lean mixture led to the low CO formation in the CI engines. By the addition of γ-Al2O3 in water emulsion diesel formation of OH levels, leads to a clean and high combustion rate and clearly shows that incomplete combustion is reduced. When the percentage of aqueous γ-Al2O3 increased, it enhanced the rate of combustion, and the nanoparticles provided a sufficient amount of oxygen to help reduce CO and HC emissions. Figure 13 shows HC versus load. In the 7% NWED blend, the diesel engine showed a reduction in HC and CO emissions compared with diesel. Nanoparticles provide a surplus of oxygen molecules at a higher load to complete the combustion process.Figure 12
Load vs. CO.Figure 13
Load vs. HC.


### 3.4. Emission Analysis with IoT

Emissions are monitored with the help of AVL DI GAS 444 N. AVL compulsorily needs a human presence for collecting the emission data and it is an expensive process. In the 20th century Internet of things (IoT), there are tremendous changes in the usage of mechanical equipment and the monitoring system. With the help of IoT and the arrangement of its auxiliary components without the presence of human need emissions would be recorded and sent to the server. By comparing the IoT kit and AVL gas analyzer the emission values are aqua-rated. Figure 14 shows the emission monitoring data visualized in the system. The comparison of HC, CO, and NOx with both analyzers shows that the variations vary around ±0.2 as shown in Figures 8, 15, 16.Figure 14
The channel opens the access to read the data stored (portrait mode).Figure 15
Load vs. HC.Figure 16
Load vs. NOx.


#### 3.4.1. Carbon Monoxide (CO)

A comparison of CO emissions with the five gas emission analyzer and IoT emission kit is shown in Figure8. Carbon monoxide is produced during incomplete combustion. Anything that leads to incomplete combustion increases CO production. In other words, as the load increases, CO emissions increase, as a lower combustion temperature results in higher CO emissions.


#### 3.4.2. Hydrocarbons (HC)

In an engine, there is a small amount of fuel oil, rich in hydrocarbons, which pass through unburnt, while other hydrocarbons are produced during combustion. As a result, hydrocarbons are mainly particles of unburned or partially burned fuels. Additionally, lubrication oil emits hydrocarbons. The variation in hydrocarbon emissions with load is shown in Figure15.


#### 3.4.3. Oxide of Nitrogen (NOx)

At high temperatures, combining oxygen with nitrogen results in nitrogen oxides (NOx) released into the atmosphere. The two types of emission analyzers are shown in Figure16 to illustrate the variation in NOx emissions.


## 3.1. Performance Analysis

The brake-specific fuel consumption (BSFC) of the engine using diesel and the three types of nano-W/D under different load conditions with constant speed are illustrated in Figure6. As shown in the graph, all three nano-W/D ratios show that BSFC at initial loads is high owing to the lower heating value in the presence of nano-based water emulsion and gradually reduces when the load is increasing, which indicates that the fuel burns in the engine effectively at higher engine loads. While running the engine with nano-W/D no identities are found in misfiring or unusual behavior. The potential benefit of water in diesel nanoemulsion fuels are as follows: (i) The overall impact of the secondary atomization and microexplosion phenomenon, resulting in strong evaporation and better mixing of air and changes in improving the combustion reactions inside the engine cylinders, (ii) Improved air-penetration in the spray due to increased spray momentum, (iii) Better-premixed combustion due to ignition delay, (iv) Increase in excess air ratio due to the presence of water in the emulsion, (v) Reduction of cooling loss due to a decrease in combustion temperature, (vi) Quash of thermal dissociation as a result of dropping in combustion temperature, and (vii) The presence of Al2O3 nanofluid in diesel emulsion that triggers less combustion gas release. Both 7% and 12% NWED blends show better fuel consumption, and it was observed that 7% NWED showed better load conditions. If the percentage of emulsion increases, the cylinder temperature decreases owing to the water percentage and its effect on combustion and emissions.Figure 6
Load vs. BSFC.Figure7 shows the variations in brake thermal efficiency of γ-Al2O3 with water and diesel test fuels under different engine loads. Compared with diesel and nano-based water emulsion blends, the blends showed an improvement in performance at higher loads. This is due to the addition of γ-Al2O3 in water and diesel at high temperatures; these mixtures turn into hydro-liquid fuel and emit high energy in the combustion process. Figure 8 clearly shows that the brake thermal efficiency increased at different concentrations of aqueous nanofluid emulsion on diesel engines with different loads. The percentage of aqueous nanofluid increased the brake thermal efficiency at higher loads.Figure 7
Load vs. BTHE.Figure 8
Load vs. CO.


## 3.2. Combustion Analysis

Figure9 shows the crank vs. cylinder pressure at full load with diesel, 3%, 7%, and 12% aqueous γ-Al2O3 emulsion blends. The peak pressures at full load on diesel are 71.99 bar, 71.82 bar, 72.17 bar, and 71.75 bar at 23°. BTDC injection timing. In addition, the cylinder pressure is improved owing to the better atomization of air and nano-W/D fuel particles during ignition, which helps to improve the heat release rate inside the engine cylinder.Figure 9
Crank angle vs. cylinder pressure.Figure10 shows the rate of heat released inside the combustion chamber with different fuel blends under full load conditions in diesel engines. The addition of γ-Al2O3 with water and diesel at the time of surface flame formation causes, water and nanoparticles to penetrate and convert water into hydrogen gas, and γ-Al2O3 procures a sufficient amount of oxygen for better atomization to improve the combustion rate. When compared with aqueous blends, the 7% NWED blend showed the best results.Figure 10
Crank angle vs. net heat release rate.


## 3.3. Emission Analysis

According to Zeldovich’s approach, the formation of nitrogen oxides depends on oxygen density, the time required for formation, and temperature. In water with high oxygen content, the water content within the inner phase absorbs the calorific heat charge of the emulsion. Consequently, this reduces the burning gasoline temperature during the combustion and consequently restricts the generation of NOx. At the time of combustion, the aqueous solution vaporized and tended to reduce the flame temperature. Figure11 shows the variation between NOx and the load. Compared with diesel, the emulsion blends showed little difference in the graph. This is because γ-Al2O3 reduces the cylinder wall temperature inside the engine chamber, thus restricting the NOx formation. An increase in the percentage of aqueous emulsions reduced the NOx level. The average reduction of 3%, 7%, and 12% NWED for every load were 31.46%, 38.76%, and 45.48%, respectively.Figure 11
Load vs. NOx.Figure12 shows the CO formation at various loads. The formation of CO depends on the air-fuel ratio. The lean mixture led to the low CO formation in the CI engines. By the addition of γ-Al2O3 in water emulsion diesel formation of OH levels, leads to a clean and high combustion rate and clearly shows that incomplete combustion is reduced. When the percentage of aqueous γ-Al2O3 increased, it enhanced the rate of combustion, and the nanoparticles provided a sufficient amount of oxygen to help reduce CO and HC emissions. Figure 13 shows HC versus load. In the 7% NWED blend, the diesel engine showed a reduction in HC and CO emissions compared with diesel. Nanoparticles provide a surplus of oxygen molecules at a higher load to complete the combustion process.Figure 12
Load vs. CO.Figure 13
Load vs. HC.


## 3.4. Emission Analysis with IoT

Emissions are monitored with the help of AVL DI GAS 444 N. AVL compulsorily needs a human presence for collecting the emission data and it is an expensive process. In the 20th century Internet of things (IoT), there are tremendous changes in the usage of mechanical equipment and the monitoring system. With the help of IoT and the arrangement of its auxiliary components without the presence of human need emissions would be recorded and sent to the server. By comparing the IoT kit and AVL gas analyzer the emission values are aqua-rated. Figure 14 shows the emission monitoring data visualized in the system. The comparison of HC, CO, and NOx with both analyzers shows that the variations vary around ±0.2 as shown in Figures 8, 15, 16.Figure 14
The channel opens the access to read the data stored (portrait mode).Figure 15
Load vs. HC.Figure 16
Load vs. NOx.


### 3.4.1. Carbon Monoxide (CO)

A comparison of CO emissions with the five gas emission analyzer and IoT emission kit is shown in Figure8. Carbon monoxide is produced during incomplete combustion. Anything that leads to incomplete combustion increases CO production. In other words, as the load increases, CO emissions increase, as a lower combustion temperature results in higher CO emissions.


### 3.4.2. Hydrocarbons (HC)

In an engine, there is a small amount of fuel oil, rich in hydrocarbons, which pass through unburnt, while other hydrocarbons are produced during combustion. As a result, hydrocarbons are mainly particles of unburned or partially burned fuels. Additionally, lubrication oil emits hydrocarbons. The variation in hydrocarbon emissions with load is shown in Figure15.


### 3.4.3. Oxide of Nitrogen (NOx)

At high temperatures, combining oxygen with nitrogen results in nitrogen oxides (NOx) released into the atmosphere. The two types of emission analyzers are shown in Figure16 to illustrate the variation in NOx emissions.


## 3.4.1. Carbon Monoxide (CO)

A comparison of CO emissions with the five gas emission analyzer and IoT emission kit is shown in Figure8. Carbon monoxide is produced during incomplete combustion. Anything that leads to incomplete combustion increases CO production. In other words, as the load increases, CO emissions increase, as a lower combustion temperature results in higher CO emissions.


## 3.4.2. Hydrocarbons (HC)

In an engine, there is a small amount of fuel oil, rich in hydrocarbons, which pass through unburnt, while other hydrocarbons are produced during combustion. As a result, hydrocarbons are mainly particles of unburned or partially burned fuels. Additionally, lubrication oil emits hydrocarbons. The variation in hydrocarbon emissions with load is shown in Figure15.


## 3.4.3. Oxide of Nitrogen (NOx)

At high temperatures, combining oxygen with nitrogen results in nitrogen oxides (NOx) released into the atmosphere. The two types of emission analyzers are shown in Figure16 to illustrate the variation in NOx emissions.


## 4. Conclusion

In this study, emphasis was placed on the impact of an aqueous nanofluid diesel emulsion on diesel engine performance, combustion, and emission characteristic in comparison with diesel. Based on the results and investigation, the following conclusions were drawn:(1)
It has been observed that the brake-specific fuel consumption is reduced to 4.2% when compared with diesel on the addition ofγ-Al2O3 in water emulsion. However, the engine brake thermal efficiency slightly improved when compared with aqueous nanofluids diesel emulsion, and diesel fuel.(2)
With the addition of aqueousγ-Al2O3 to diesel, the combustion rate, and cylinder pressure exhibited almost the same peaks. Further, increases the blend percentage of aqueous γ-Al2O3 it affects in-cylinder temperature.(3)
In the combustion process, the addition of aqueousγ-Al2O3 to diesel produces a surplus amount of oxygen atoms to complete the effective combustion inside a diesel engine, which helps to reduce HC, NOx, and CO.(4)
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*Source: 8470743-2022-09-28.xml*