# Experimental Investigation and Prediction of Performance, Combustion, and Emission Features of a Diesel Engine Fuelled with Pumpkin-Maize Biodiesel using Different Machine Learning Algorithms

Authors: N. Magesh; T. Pushparaj; V. Vinoth Kannan; Deepak Thakur; Abhishek Sharma; Abdul Razak; Abdulrajak Buradi; Abiot Ketema
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The current study examines the usage of biodiesel as a diesel substitute that is produced through the transesterification of pumpkin and maize with the addition of a diethyl ether (DEE) additive. Pumpkin-maize (PM) biodiesel and addition of diethyl ether (DEE), as well as their blends of 10%, 20%, 30%, 40%, and 50% with diesel, were used in performance, combustion, and emission examinations under various load conditions. According to the experimental findings, adding 5 ml of the DEE boosts BTE by 31.91 percent (B20 blend) compared to diesel. While the diesel equivalent of BSFC decreases by 9.519%. NO emission dropped by 34.91 percent at peak loads, HC emissions were augmented by 32.43%, and smoke opacity improved by 27.24%. To enhance the engine performance, combustion, and emission features of the substitute biodiesel diesel, the study emphasises the precise mix proportions of PM biodiesel with DEE addition. Using ANN, BTE, and NO were predicted withR2 values of 0.93 and 0.95, respectively. As can be observed, the R2 value for the ANN model was almost one, suggesting that the ANN models had better predictive power than other machine learning (ML) models.


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## Body

## 1. Introduction

The energy crisis has enraged people all around the world owing to the rapid depletion of fossil fuel supplies and ecological damage. In addition to being a necessity in modern living, fuel significantly contributes to air pollution [1]. Concerns regarding the degradation of the world’s fossil fuel supplies have been expressed because of the vast usage of such resources [2]. On the other hand, during the last several decades, environmental deterioration caused by the burning of these fuels has proven to be a major worry for humanity. As a result, researchers are searching for substitutes for conventional fuels as petroleum-based fuel stocks are depleted and the environment is being damaged. Biodiesel, a fuel made from plant oils, is one such option [3]. Oils derived from plants are viewed as an ecologically responsible and sustainable energy source [4]. Numerous research has been done to evaluate the efficiency, combustion, and emission features of these plant biodiesels. Nath Verma and Shrivastava [5] examined various fuel properties of Jatropha oil in a CI engine. Analyses were performed on the impact of diesel concentrations in Jatropha oil. The mixture of biodiesel made with 20 percent Jatropha demonstrated the highest BTE performance. Roselle biodiesel and Karanja biodiesel were tested for performance under the same operating settings by Shrivastava et al. [6]. Although a large upsurge in NOx release was documented at greater CR’s, the reduced BSFC was reported at lower CR’s. Ranjitha et al. [7] conducted a study on biodiesel creation and performance using beef tallow oil. They found that compared to regular diesel, biodiesel’s engine characteristics showed a faster rate of heat release, a higher maximum cylinder pressure, and lower emission levels. Similarly, Anantha et al. [8] studied the creation and emission features of rapeseed oil biodiesel. According to the test findings, diesel engines (DE) may utilise the B25 blend without any modifications to achieve a satisfactory level of thermal performance and reduced emissions. Jatropha and Karanja oil-based biofuel blends were tested for combustion efficiency by Deepanraj et al. [9]. Blends of biodiesel fuel had ignition delays and extreme cylinder pressures that were remarkably similar to those of normal diesel. Compared to the Jatropha oil mix, the combustion of the Karanja oil blend took a longer time. Dimethoxymethane (DMM) blends effects on combustion parameters, fuel efficiency was evaluated by Deepanraj et al. [10]. They found that diesel that had 20% DMM added to it performed better. In another study, Deepanraj et al. [11] used palm oil biodiesel for performance and combustion analyses in a diesel engine (DE). It was observed that preheated oil produced greater exhaust temperatures and NOx emissions, though HC and CO emissions were low. A critical assessment of several methods for using hydrous ethanol in engines was conducted by Loyte et al. [12] They observed that hydrous ethanol greatly lowers NOx and soot emissions. This fuel can be used to produce complete combustion. Additionally, it demonstrated considerable fuel cost reductions. The impact of the pilot injection on the combustion features of DE running on a blend of waste cooking biodiesel was examined by Chacko and Thangaraja [13]. In comparison to the diesel running at a single injection, the experiment outcomes showed a substantial reduction in smoke, HC, and CO. Additionally, there was a 4.46 percent increase in BTE. A propane-induced diesel engine powered by waste seed biodiesel (WSBD) was explored by Raja et al. [14]. According to this study, adding propane enhanced performance patterns like reduced BSFC and 0.7 percent greater BTE of WSBD. The combined impact of CR and EGR on the combustion and performance of a CRDI engine using sapota methyl ester in diesel was studied by Jayabal et al. [15]. The outcome showed that a higher CR decreased the ignition delay. They also found lower NOx emissions. Vegetable waste collected from the market is converted to fuel by Arulprakasajothi et al. [16]. Using agricultural residues, solid biomass fuel for commercial heating is created. Thus, the effects of environmental issues are lessened. On engine performance and emissions, the impacts of adding various ratios of Ag nanoparticles and alcohols to milk scum oil were investigated by Asif et al. [17]. They found that the number of nanoparticles greatly reduces the BSFC and aids in a greater percentage reduction in emissions.Operating an engine under every possible operational circumstance and fuel condition is expensive and challenging. However, the output and exhaust pollution levels of diesel engines may be modeled using machine learning (ML) approaches such as ANN and ANFIS, which require significantly less time and resources. The most remarkable quality of these new strategies is their capacity to automatically learn and anticipate certain desired outcomes [18]. To provide a quick response and precise model to forecast the commencement of combustion in homogenous charged CI engines, Namar et al. [19] suggested 11 ML models. They found that the models provided a suitable response time for use in actual control applications because of the linear hypothesis.The aforementioned literature makes clear that response surface methodology (RSM) has been the main prediction approach for investigators to anticipate and augment the performance and emission parameters of biodiesel [20, 21]. There are few AI uses in bioenergy processes. Furthermore, only a small number of studies have discussed how machine learning (ML) approaches might be used to forecast and improve performance [22, 23]. However, studies indicate that ML has a great deal of promise for removing bioenergy growth roadblocks [24, 25]. Also, the study of hybrid biodiesel usage in CI engines is also limited. So, in this paper, these research gaps are filled by employing hybrid biodiesel. Performance, emission, and combustion parameters for the proportions of the hybrid biodiesel have been compared and discussed using the RSM technique. Advanced ML techniques such as support vector regression (SVR), K-nearest neighbor algorithm (k-NN), and deep learning are used for the prediction of these performance parameters.


## 2. Experimental Setup and Procedure

In this work, a single cylinder, 4S, constant speed CI engine working at 1500 rpm was used to inspect the performance and emission attributes of a CI engine running on hybrid biodiesel with diethyl ether (DEE) an additive. The engine has a 17.5 : 1 compression ratio and can produce a maximum power of 5.2 kW. The experimental setup is shown in Figure1. Its parameters are shown in Table 1.Figure 1
Computerized test setup schematic layout [17].Table 1
Engine specification [26].
Sl. no.ParameterSpecification1EngineKirloskar TV1, water cooled, constant speed single cylinder, 4 S4Bore87.5 mm5Stroke110 mm6Compression ratio17.5 : 17Cylinder volume0.661 m3In order to prepare the fuel on a lab scale, biodiesel was extracted from pumpkin oil and maize oil separately and then combined in 50 : 50 ratio to form a pumpkin-maize biodiesel blend. The created mixed biodiesel was then combined with the diesel as follows. 100 ml of pumpkin-maize biodiesel and 900 ml of diesel are represented by a 10% mix. Similarly, other blends were prepared. For each mix, 5 ml of DEE additive was added in the same amount.


## 3. Uncertainty Evaluation

Examining the degree of uncertainty is necessary to demonstrate the test results correctness. Uncertainty assessment is conducted using the methodology outlined by Holman [27] and Moffat [28]. Table 2 provides a list of the trial-related apparatus together with information on its accuracy and uncertainty.Table 2
Apparatus list and related uncertainties [29].
ApparatusUncertaintySpeed measuring apparatus± 1.7%Biofuel burette± 1.8%Crank angle encoder± 0.4%NO± 0.4%HC± 0.4%CO2± 0.9%Smoke meter± 1.0%


### 3.1. Modelling Using ML Procedures

#### 3.1.1. Artificial Neural Networks (ANN)

ANNs are a type of logic programming that was created by mimicking how the human brain works to execute tasks like learning, memorizing, making decisions, and inferring conclusions without the use of external assistance. ANNs have various crucial characteristics, including the ability to generalise, learn from data, handle an infinite number of variables, etc. Artificial neural cells, which are the smallest building blocks of ANNs, function similarly to biological neurons in that they take input from many sources, integrate it in some fashion, execute a nonlinear operation, and produce the outcome.The three primary layers of an ANN are the input, hidden, and output layers (Figure2). Data from an outside source make up the inputs. Data from the outside source is sent to the hidden layer by the processing units, known as neurons, in the input layer [22]. The links between cells’ weights are their values. The input and hidden layer neurons’ data, along with the bias, summation, and activation functions, are used to create the outputs [18]. The network’s neurons calculate a weighted sum wixi of their input signal yi, for i = 0, 1, 2, ..., n hidden layer, and then use that information to create an output signal. Below is a definition of this function [24]:(1)u=b+∑i=1Nwixi.Figure 2
The fundamental of ANN model [30].


#### 3.1.2. Support Vector Regression (SVR)

SVR is a supervised algorithm and implementation of support vectors employed by investigators for regression models. A more powerful tool for engine management, emission minimization, and onboard diagnosis is produced by applying a straightforward but accurate prediction and emission model developed using a sophisticated machine learning (ML) technique. It can model complex and nonlinear relationships and can produce high-precision decision limits based on a significant subset of training data points [31]. The suggested SVR-based model also has a higher capability for convergence than other cutting-edge methods like Gaussian Processes or artificial neural networks (ANN). Equation (2) represents the SVR equation.(2)Qy=Yy+t,where Q (y) is the anticipated value, Y (y) is the feature space dimension’s weight vector, and t is the cutoff point.


#### 3.1.3. K-Nearest Neighbor Algorithm (k-NN)

It is the most widely employed algorithm in the ML toolkit. Analogical learning is the foundation of the k-NN data processing technique. By contrasting a given array with analogous training items, it gains knowledge. This approach is simple, but because of its efficiency, simplicity, intuitiveness, and competitiveness, it is capable of handling regression and classification problems with dimensions up to a sizeable one. This approach selects the main class among thek samples after identifying the k closest neighbors using the Euclidean distance equation (3) [32].Euclidean distance equation:(3)dp^,q^=p^1−q^12+p^2−q^22+⋯+p^n−q^n2=∑i=1np^i−q^i2.The Euclidian distance is defined by the expressiondp^,q^, where p^, and q^, the reference, are points with n dimensions.In this study,k values of 4 for BTE and 6 for NOx emissions provide the best forecasting outcomes.


#### 3.1.4. Deep Learning Algorithm (DL)

DL is a subset of ML methods that employ numerous computing layers to learn an abstraction hierarchy for data representation [33]. Contrary to traditional ML methods, DL may execute feature extraction on its own, even when only raw data is provided as input. In the DL technique, growing the dataset corresponds to growing learning performance. A rectifier was used in this study to represent the deep learning activity [34]. Epsilon is set to 1. E09 in the study, and all response loss functions are quadratic. For BTE, the best results were obtained when the epoch and rho values were 12 and 0.97, respectively.


#### 3.1.5. Response Surface Methodology (RSM) Approach

The RSM approach has been the most popular and well-known statistical technique in recent years for experimental investigations. The RSM approach uses statistically planned experiments that are monitored by predicting the quantities in a mathematical model, measuring the model’s sufficiency, and forecasting the model response [35]. The RSM approach allows for an equal interpretation of the chosen response and the measurable pattern of correlation among independent input variables [36]. It determines the best combination of parameters to produce a minimum or maximum response value and may illustrate the interactive and direct impacts of operational parameters using three- and two-dimensional graphs [37]. We have used a 3-level full factorial design to select the order of experiments to be run. The input factors considered in this research are the load (KW), the biodiesel blends, and the different situations. The corresponding outputs that were measured were the performance, emission, and combustion parameters.


## 3.1. Modelling Using ML Procedures

### 3.1.1. Artificial Neural Networks (ANN)

ANNs are a type of logic programming that was created by mimicking how the human brain works to execute tasks like learning, memorizing, making decisions, and inferring conclusions without the use of external assistance. ANNs have various crucial characteristics, including the ability to generalise, learn from data, handle an infinite number of variables, etc. Artificial neural cells, which are the smallest building blocks of ANNs, function similarly to biological neurons in that they take input from many sources, integrate it in some fashion, execute a nonlinear operation, and produce the outcome.The three primary layers of an ANN are the input, hidden, and output layers (Figure2). Data from an outside source make up the inputs. Data from the outside source is sent to the hidden layer by the processing units, known as neurons, in the input layer [22]. The links between cells’ weights are their values. The input and hidden layer neurons’ data, along with the bias, summation, and activation functions, are used to create the outputs [18]. The network’s neurons calculate a weighted sum wixi of their input signal yi, for i = 0, 1, 2, ..., n hidden layer, and then use that information to create an output signal. Below is a definition of this function [24]:(1)u=b+∑i=1Nwixi.Figure 2
The fundamental of ANN model [30].


### 3.1.2. Support Vector Regression (SVR)

SVR is a supervised algorithm and implementation of support vectors employed by investigators for regression models. A more powerful tool for engine management, emission minimization, and onboard diagnosis is produced by applying a straightforward but accurate prediction and emission model developed using a sophisticated machine learning (ML) technique. It can model complex and nonlinear relationships and can produce high-precision decision limits based on a significant subset of training data points [31]. The suggested SVR-based model also has a higher capability for convergence than other cutting-edge methods like Gaussian Processes or artificial neural networks (ANN). Equation (2) represents the SVR equation.(2)Qy=Yy+t,where Q (y) is the anticipated value, Y (y) is the feature space dimension’s weight vector, and t is the cutoff point.


### 3.1.3. K-Nearest Neighbor Algorithm (k-NN)

It is the most widely employed algorithm in the ML toolkit. Analogical learning is the foundation of the k-NN data processing technique. By contrasting a given array with analogous training items, it gains knowledge. This approach is simple, but because of its efficiency, simplicity, intuitiveness, and competitiveness, it is capable of handling regression and classification problems with dimensions up to a sizeable one. This approach selects the main class among thek samples after identifying the k closest neighbors using the Euclidean distance equation (3) [32].Euclidean distance equation:(3)dp^,q^=p^1−q^12+p^2−q^22+⋯+p^n−q^n2=∑i=1np^i−q^i2.The Euclidian distance is defined by the expressiondp^,q^, where p^, and q^, the reference, are points with n dimensions.In this study,k values of 4 for BTE and 6 for NOx emissions provide the best forecasting outcomes.


### 3.1.4. Deep Learning Algorithm (DL)

DL is a subset of ML methods that employ numerous computing layers to learn an abstraction hierarchy for data representation [33]. Contrary to traditional ML methods, DL may execute feature extraction on its own, even when only raw data is provided as input. In the DL technique, growing the dataset corresponds to growing learning performance. A rectifier was used in this study to represent the deep learning activity [34]. Epsilon is set to 1. E09 in the study, and all response loss functions are quadratic. For BTE, the best results were obtained when the epoch and rho values were 12 and 0.97, respectively.


### 3.1.5. Response Surface Methodology (RSM) Approach

The RSM approach has been the most popular and well-known statistical technique in recent years for experimental investigations. The RSM approach uses statistically planned experiments that are monitored by predicting the quantities in a mathematical model, measuring the model’s sufficiency, and forecasting the model response [35]. The RSM approach allows for an equal interpretation of the chosen response and the measurable pattern of correlation among independent input variables [36]. It determines the best combination of parameters to produce a minimum or maximum response value and may illustrate the interactive and direct impacts of operational parameters using three- and two-dimensional graphs [37]. We have used a 3-level full factorial design to select the order of experiments to be run. The input factors considered in this research are the load (KW), the biodiesel blends, and the different situations. The corresponding outputs that were measured were the performance, emission, and combustion parameters.


## 3.1.1. Artificial Neural Networks (ANN)

ANNs are a type of logic programming that was created by mimicking how the human brain works to execute tasks like learning, memorizing, making decisions, and inferring conclusions without the use of external assistance. ANNs have various crucial characteristics, including the ability to generalise, learn from data, handle an infinite number of variables, etc. Artificial neural cells, which are the smallest building blocks of ANNs, function similarly to biological neurons in that they take input from many sources, integrate it in some fashion, execute a nonlinear operation, and produce the outcome.The three primary layers of an ANN are the input, hidden, and output layers (Figure2). Data from an outside source make up the inputs. Data from the outside source is sent to the hidden layer by the processing units, known as neurons, in the input layer [22]. The links between cells’ weights are their values. The input and hidden layer neurons’ data, along with the bias, summation, and activation functions, are used to create the outputs [18]. The network’s neurons calculate a weighted sum wixi of their input signal yi, for i = 0, 1, 2, ..., n hidden layer, and then use that information to create an output signal. Below is a definition of this function [24]:(1)u=b+∑i=1Nwixi.Figure 2
The fundamental of ANN model [30].


## 3.1.2. Support Vector Regression (SVR)

SVR is a supervised algorithm and implementation of support vectors employed by investigators for regression models. A more powerful tool for engine management, emission minimization, and onboard diagnosis is produced by applying a straightforward but accurate prediction and emission model developed using a sophisticated machine learning (ML) technique. It can model complex and nonlinear relationships and can produce high-precision decision limits based on a significant subset of training data points [31]. The suggested SVR-based model also has a higher capability for convergence than other cutting-edge methods like Gaussian Processes or artificial neural networks (ANN). Equation (2) represents the SVR equation.(2)Qy=Yy+t,where Q (y) is the anticipated value, Y (y) is the feature space dimension’s weight vector, and t is the cutoff point.


## 3.1.3. K-Nearest Neighbor Algorithm (k-NN)

It is the most widely employed algorithm in the ML toolkit. Analogical learning is the foundation of the k-NN data processing technique. By contrasting a given array with analogous training items, it gains knowledge. This approach is simple, but because of its efficiency, simplicity, intuitiveness, and competitiveness, it is capable of handling regression and classification problems with dimensions up to a sizeable one. This approach selects the main class among thek samples after identifying the k closest neighbors using the Euclidean distance equation (3) [32].Euclidean distance equation:(3)dp^,q^=p^1−q^12+p^2−q^22+⋯+p^n−q^n2=∑i=1np^i−q^i2.The Euclidian distance is defined by the expressiondp^,q^, where p^, and q^, the reference, are points with n dimensions.In this study,k values of 4 for BTE and 6 for NOx emissions provide the best forecasting outcomes.


## 3.1.4. Deep Learning Algorithm (DL)

DL is a subset of ML methods that employ numerous computing layers to learn an abstraction hierarchy for data representation [33]. Contrary to traditional ML methods, DL may execute feature extraction on its own, even when only raw data is provided as input. In the DL technique, growing the dataset corresponds to growing learning performance. A rectifier was used in this study to represent the deep learning activity [34]. Epsilon is set to 1. E09 in the study, and all response loss functions are quadratic. For BTE, the best results were obtained when the epoch and rho values were 12 and 0.97, respectively.


## 3.1.5. Response Surface Methodology (RSM) Approach

The RSM approach has been the most popular and well-known statistical technique in recent years for experimental investigations. The RSM approach uses statistically planned experiments that are monitored by predicting the quantities in a mathematical model, measuring the model’s sufficiency, and forecasting the model response [35]. The RSM approach allows for an equal interpretation of the chosen response and the measurable pattern of correlation among independent input variables [36]. It determines the best combination of parameters to produce a minimum or maximum response value and may illustrate the interactive and direct impacts of operational parameters using three- and two-dimensional graphs [37]. We have used a 3-level full factorial design to select the order of experiments to be run. The input factors considered in this research are the load (KW), the biodiesel blends, and the different situations. The corresponding outputs that were measured were the performance, emission, and combustion parameters.


## 4. Discussions on Results

### 4.1. Brake Thermal Efficiency (BTE)

The relationship between loads with biodiesel and vs BTE is shown in Figure3. The BTE is determined by dividing the amount of work completed by the fuel’s heating value. This crucial factor determines how effectively an engine transfers chemical energy into work. Additionally, it evaluates how well the engine produces heat using the fuel’s chemical energy. According to the graphs, the BTE was high at higher loads and nearly equivalent to that of diesel at low loads. For the B20 blend, the BTE was found to be the highest for the loads. At 80% load, adding a 20% blend increased the BTE by 31.91%. The boost in efficiency is brought on by the mixture’s increased heating value and higher oxygen content, which causes biodiesel to burn more quickly after the premixed combustion phase [38, 39]. The peak pressure and combustion temperature rise are also responsible for the rise in BTE [40]. As the blend percentage increases, BTE slightly decreases [41]. The blend’s density and cetane number value were reduced by mixing pumpkin-maize with diesel and adding DEE additive.Figure 3
BTE vs. load variations for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


### 4.2. Brake Specific Fuel Consumption (BSFC)

Figure4 displays the relationship between BSFC and load when using diesel fuel that has been mixed with 10 to 50 percent PM with the DEE additive. The BSFC factor must be carefully considered because it measures the charge efficiency of the engine. It shows how efficiently engine fuel is used to produce work. When compared to diesel, all mixed fuels had approximately identical BSFCs at low loads, and they all decreased with an increase in the loads [42]. At 80 percent load, the addition of 10 percent to 50 percent PM mixed with diesel, respectively, resulted in reductions in the BSFC of 1.464 percent, 7.197 percent, 8.343 percent, 8.821 percent, and 9.519 percent. BSFC’s for biodiesel blends were low at greater engine loads because the biodiesel oil has lower calorific values than diesel. Because of this, adding more biodiesel to the mixture would raise the fuel's calorific value compared to using pure diesel fuels, resulting in lower BSFCs than using plain diesel [43, 44].Figure 4
BSFC vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


### 4.3. Combustion Factors

#### 4.3.1. Heat Release Rate (HRR)

The term “heat release” refers to how gasoline burns within an engine. The high peak cylinder pressure is influenced by the heat discharge. Figure5 displays the HRR for the blend ratios of B20, B40, B60, B80, and B100. Due to the onset of vaporisation occurring during the ignition delay, the initial value of heat release was reported to be negative. However, as combustion began, the value changed to positive [27]. The ignition delay brought on by the PME delayed the onset of combustion. Due to sluggish heat release and lower maximum pressure, greater blend ratios produced less heat release than the B20 blend.Figure 5
Variation of HRR with crank angle: (a) half load; (b) full load.
(a)(b)


#### 4.3.2. Pressure vs. Crank Angle

Figure6 shows the variation in cylinder pressure with a crank angle at full load and half load for various biodiesel blends. This factor investigates how combustion behaves in the combustion chamber and assesses the performance of the engine. The findings indicated that when the engine load increased, cylinder pressure also increased; as a consequence, a high quantity of gasoline was introduced and combusted at higher engine loads. The diagram also illustrates that for lesser biodiesel mixes, the pressure increase started quickly at lower loads [45], however, for B50, it took longer than it did for diesel fuel. The cylinder pressure for B20 was higher than that of the other fuels.Figure 6
Variation of pressure with crank angle for various biodiesel blends: (a) half load; (b) full load.
(a)(b)


#### 4.3.3. Ignition Delay

The period of time between the commencement of fuel addition and the time of the start of combustion at a certain crank angle is known as the ignition delay. It is a crucial measure for assessing the efficacy of ignition (cetane number) that directly affects the fuel's tendency for knocking and ignition delay. The primary factors that govern the delay duration were thought to be cylinder pressure and cetane index. Figure7 shows the variation of ignition delay with load for different mixes. In comparison to premixed combustion, a fuel with a greater cetane rating will result in a small ignition delay and better fuel diffusion. The fuel’s cetane rating has an inverse relationship with ignition delay [10, 46]. Generally, compared to diesel, biodiesel and its mixes have greater cetane values. The ignition delays were seen to be shorter for all biodiesel mixes than for diesel [47]. This may be because biodiesels and their mixes have greater cetane numbers and contain more oxygen.Figure 7
Ignition delay vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


### 4.4. Emission Features

#### 4.4.1. HC Emission

The graphs of HC emissions vs. load are shown in Figure8. According to the results, it was deduced from the graphs that the HC emissions rose for all mixed fuels. Additionally, the HC emissions increase along with the load. This is the result of a shortage of oxygen. For diesel, and the corresponding blends, the HC emissions were 32, 56, 37, 95, 123, and 130 ppm at 20 percent load. The decrease in operating temperature brought on by the longer delay time and the decrease in the cetane number value contributed to the increase in HC emissions. The amount of HC emission seen in this study is closer in value to that found in previous studies [48, 49]. The AVL 444DI gas analyzer, which has an HC measurement accuracy of ±15 ppm and a percentage uncertainty of ±0.2, was used to measure the HC emission.Figure 8
HC emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


#### 4.4.2. No Emission

Figure9 shows that using biodiesel results in higher NO emissions. NO emissions rose with load for different fuel mixes. The increase in NO emissions as the percentage of pumpkin-maize biodiesel rises may be explained by the high combustion. Additionally, since biodiesels contain more oxygen, they burn more thoroughly, producing greater peak combustion temperatures and higher NO emissions [50]. The B10, B20, B30, B40, and B50 release around 402, 363, 311, 301, and 250 ppm NO at 20% load, respectively, when compared to diesel. The NO emissions were found to be 17.2% lower than diesel at 100% load for the B20 blend with DEE additive-based alternative fuel.Figure 9
NO emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)The AVL 444DI gas analyzer, whose accuracy in measuring NOx is ±0.20 ppm and whose percentage uncertainty is ±0.2, was used to measure the emissions.


#### 4.4.3. Smoke Opacity (%) Emission

Figure10 displays the smoke level for various load conditions using different PME mixes with DEE additives. The smoke opacity of diesel engine exhaust is used to determine how much smoke will be produced. The primary factors that determine smoke opacity are fuel composition and oxygen [51]. The viscosity, volatility, and quality of the fuel air combination are other factors, which affect the fuel’s ability to atomize and that lead to smoke generation. The graph demonstrates that, as the load rises, smoke opacity also increases for all the fuels. The smoke opacity was greater for all biodiesels as contrasted to diesel, except for B20 biodiesel, which shows nearly the same smoke opacity as that of diesel fuel [52]. Slow combustion and hence higher smoke levels are caused by the poor atomization and vaporisation behavior of biodiesel mixes. When contrasted with diesel at 100% load, the B20 mix produced approximately the same amount of smoke (3.6%). This is because the B20 mix aids in the oxidation and full combustion of the fuel, reducing the amount of smoke.Figure 10
Smoke opacity vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


### 4.5. ANN Modelling

Tables3 and 4 show the correlation coefficient R2 for training, preparation, and validation data of ANN models. Figure 11 shows the setup, validation, analysis, and general regression studies of the trained network model. By training the ANN on a massive amount of experimental data, the mean squared error (MSE) efficiency measure between the ANN replies and the target data was systematically decreased. Less hidden nodes in ANN modelling have been shown to be more effective. However, in order to mimic the results of the ANN modelling for BTE (related to Figure 3) and NOx emissions (details are given in Figure 9), forty synapses and thirty synapses in the hidden node’s framework were employed, respectively (Figure 11). The anticipated data from the ANN was compared to the test data. As can be seen, utilising ANN significantly improves the outcome of the estimation. Therefore, ANN was effective in predicting engine performance and emission features of different biodiesels.Table 3
ANN outcomes for forecasting BTE.
Number of hidden layer neuronsMSEAll data100.90526150.91528200.91530300.92534400.93419500.92534Table 4
ANN outcomes for forecasting NOx emissions.
Number of hidden layer neuronsMSEAll data100.91305150.92307200.93309250.94313300.95099400.94312Figure 11
ANN modelling with an optimal performance by using (a) 40 hidden layer neurons for BTE and (b) 40 hidden layer neurons for NO emissions.
(a)(b)Using different ML regression models, BTE and NOx predictions were also made. These regression models modelling coefficients for predicting BTE and NOx, respectively, are shown in Tables5 and 6. R2 values for all of these models were, as can be seen, marginally lower than ANN. Figure 12 shows the prediction of BTE using machine learning (ML) algorithms.Table 5
Modelling coefficient for different regression models to forecast BTE.
Regression modelR2MAEMSERMSESVM0.9262.765415.773.59KNN0.9156.903752.561.25Deep learning (RNN)0.9367.675726.175.67Table 6
Modelling coefficient for different regression models to forecast NOx.
Regression modelR2MAEMSERMSESVM0.9463.8724054.163.67KNN0.9361.42501870.83Deep learning (RNN)0.9582.9966888.363.75R2 = coefficient of determination, MAE = mean absolute error, MSE = mean squared error, and RMSE = relative root mean squared error.Figure 12
Prediction of BTE using machine learning (ML) algorithms: (a) SVM results, (b) KNN results, and (c) DL results.
(a)(b)(c)


## 4.1. Brake Thermal Efficiency (BTE)

The relationship between loads with biodiesel and vs BTE is shown in Figure3. The BTE is determined by dividing the amount of work completed by the fuel’s heating value. This crucial factor determines how effectively an engine transfers chemical energy into work. Additionally, it evaluates how well the engine produces heat using the fuel’s chemical energy. According to the graphs, the BTE was high at higher loads and nearly equivalent to that of diesel at low loads. For the B20 blend, the BTE was found to be the highest for the loads. At 80% load, adding a 20% blend increased the BTE by 31.91%. The boost in efficiency is brought on by the mixture’s increased heating value and higher oxygen content, which causes biodiesel to burn more quickly after the premixed combustion phase [38, 39]. The peak pressure and combustion temperature rise are also responsible for the rise in BTE [40]. As the blend percentage increases, BTE slightly decreases [41]. The blend’s density and cetane number value were reduced by mixing pumpkin-maize with diesel and adding DEE additive.Figure 3
BTE vs. load variations for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.2. Brake Specific Fuel Consumption (BSFC)

Figure4 displays the relationship between BSFC and load when using diesel fuel that has been mixed with 10 to 50 percent PM with the DEE additive. The BSFC factor must be carefully considered because it measures the charge efficiency of the engine. It shows how efficiently engine fuel is used to produce work. When compared to diesel, all mixed fuels had approximately identical BSFCs at low loads, and they all decreased with an increase in the loads [42]. At 80 percent load, the addition of 10 percent to 50 percent PM mixed with diesel, respectively, resulted in reductions in the BSFC of 1.464 percent, 7.197 percent, 8.343 percent, 8.821 percent, and 9.519 percent. BSFC’s for biodiesel blends were low at greater engine loads because the biodiesel oil has lower calorific values than diesel. Because of this, adding more biodiesel to the mixture would raise the fuel's calorific value compared to using pure diesel fuels, resulting in lower BSFCs than using plain diesel [43, 44].Figure 4
BSFC vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.3. Combustion Factors

### 4.3.1. Heat Release Rate (HRR)

The term “heat release” refers to how gasoline burns within an engine. The high peak cylinder pressure is influenced by the heat discharge. Figure5 displays the HRR for the blend ratios of B20, B40, B60, B80, and B100. Due to the onset of vaporisation occurring during the ignition delay, the initial value of heat release was reported to be negative. However, as combustion began, the value changed to positive [27]. The ignition delay brought on by the PME delayed the onset of combustion. Due to sluggish heat release and lower maximum pressure, greater blend ratios produced less heat release than the B20 blend.Figure 5
Variation of HRR with crank angle: (a) half load; (b) full load.
(a)(b)


### 4.3.2. Pressure vs. Crank Angle

Figure6 shows the variation in cylinder pressure with a crank angle at full load and half load for various biodiesel blends. This factor investigates how combustion behaves in the combustion chamber and assesses the performance of the engine. The findings indicated that when the engine load increased, cylinder pressure also increased; as a consequence, a high quantity of gasoline was introduced and combusted at higher engine loads. The diagram also illustrates that for lesser biodiesel mixes, the pressure increase started quickly at lower loads [45], however, for B50, it took longer than it did for diesel fuel. The cylinder pressure for B20 was higher than that of the other fuels.Figure 6
Variation of pressure with crank angle for various biodiesel blends: (a) half load; (b) full load.
(a)(b)


### 4.3.3. Ignition Delay

The period of time between the commencement of fuel addition and the time of the start of combustion at a certain crank angle is known as the ignition delay. It is a crucial measure for assessing the efficacy of ignition (cetane number) that directly affects the fuel's tendency for knocking and ignition delay. The primary factors that govern the delay duration were thought to be cylinder pressure and cetane index. Figure7 shows the variation of ignition delay with load for different mixes. In comparison to premixed combustion, a fuel with a greater cetane rating will result in a small ignition delay and better fuel diffusion. The fuel’s cetane rating has an inverse relationship with ignition delay [10, 46]. Generally, compared to diesel, biodiesel and its mixes have greater cetane values. The ignition delays were seen to be shorter for all biodiesel mixes than for diesel [47]. This may be because biodiesels and their mixes have greater cetane numbers and contain more oxygen.Figure 7
Ignition delay vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.3.1. Heat Release Rate (HRR)

The term “heat release” refers to how gasoline burns within an engine. The high peak cylinder pressure is influenced by the heat discharge. Figure5 displays the HRR for the blend ratios of B20, B40, B60, B80, and B100. Due to the onset of vaporisation occurring during the ignition delay, the initial value of heat release was reported to be negative. However, as combustion began, the value changed to positive [27]. The ignition delay brought on by the PME delayed the onset of combustion. Due to sluggish heat release and lower maximum pressure, greater blend ratios produced less heat release than the B20 blend.Figure 5
Variation of HRR with crank angle: (a) half load; (b) full load.
(a)(b)


## 4.3.2. Pressure vs. Crank Angle

Figure6 shows the variation in cylinder pressure with a crank angle at full load and half load for various biodiesel blends. This factor investigates how combustion behaves in the combustion chamber and assesses the performance of the engine. The findings indicated that when the engine load increased, cylinder pressure also increased; as a consequence, a high quantity of gasoline was introduced and combusted at higher engine loads. The diagram also illustrates that for lesser biodiesel mixes, the pressure increase started quickly at lower loads [45], however, for B50, it took longer than it did for diesel fuel. The cylinder pressure for B20 was higher than that of the other fuels.Figure 6
Variation of pressure with crank angle for various biodiesel blends: (a) half load; (b) full load.
(a)(b)


## 4.3.3. Ignition Delay

The period of time between the commencement of fuel addition and the time of the start of combustion at a certain crank angle is known as the ignition delay. It is a crucial measure for assessing the efficacy of ignition (cetane number) that directly affects the fuel's tendency for knocking and ignition delay. The primary factors that govern the delay duration were thought to be cylinder pressure and cetane index. Figure7 shows the variation of ignition delay with load for different mixes. In comparison to premixed combustion, a fuel with a greater cetane rating will result in a small ignition delay and better fuel diffusion. The fuel’s cetane rating has an inverse relationship with ignition delay [10, 46]. Generally, compared to diesel, biodiesel and its mixes have greater cetane values. The ignition delays were seen to be shorter for all biodiesel mixes than for diesel [47]. This may be because biodiesels and their mixes have greater cetane numbers and contain more oxygen.Figure 7
Ignition delay vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.4. Emission Features

### 4.4.1. HC Emission

The graphs of HC emissions vs. load are shown in Figure8. According to the results, it was deduced from the graphs that the HC emissions rose for all mixed fuels. Additionally, the HC emissions increase along with the load. This is the result of a shortage of oxygen. For diesel, and the corresponding blends, the HC emissions were 32, 56, 37, 95, 123, and 130 ppm at 20 percent load. The decrease in operating temperature brought on by the longer delay time and the decrease in the cetane number value contributed to the increase in HC emissions. The amount of HC emission seen in this study is closer in value to that found in previous studies [48, 49]. The AVL 444DI gas analyzer, which has an HC measurement accuracy of ±15 ppm and a percentage uncertainty of ±0.2, was used to measure the HC emission.Figure 8
HC emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


### 4.4.2. No Emission

Figure9 shows that using biodiesel results in higher NO emissions. NO emissions rose with load for different fuel mixes. The increase in NO emissions as the percentage of pumpkin-maize biodiesel rises may be explained by the high combustion. Additionally, since biodiesels contain more oxygen, they burn more thoroughly, producing greater peak combustion temperatures and higher NO emissions [50]. The B10, B20, B30, B40, and B50 release around 402, 363, 311, 301, and 250 ppm NO at 20% load, respectively, when compared to diesel. The NO emissions were found to be 17.2% lower than diesel at 100% load for the B20 blend with DEE additive-based alternative fuel.Figure 9
NO emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)The AVL 444DI gas analyzer, whose accuracy in measuring NOx is ±0.20 ppm and whose percentage uncertainty is ±0.2, was used to measure the emissions.


### 4.4.3. Smoke Opacity (%) Emission

Figure10 displays the smoke level for various load conditions using different PME mixes with DEE additives. The smoke opacity of diesel engine exhaust is used to determine how much smoke will be produced. The primary factors that determine smoke opacity are fuel composition and oxygen [51]. The viscosity, volatility, and quality of the fuel air combination are other factors, which affect the fuel’s ability to atomize and that lead to smoke generation. The graph demonstrates that, as the load rises, smoke opacity also increases for all the fuels. The smoke opacity was greater for all biodiesels as contrasted to diesel, except for B20 biodiesel, which shows nearly the same smoke opacity as that of diesel fuel [52]. Slow combustion and hence higher smoke levels are caused by the poor atomization and vaporisation behavior of biodiesel mixes. When contrasted with diesel at 100% load, the B20 mix produced approximately the same amount of smoke (3.6%). This is because the B20 mix aids in the oxidation and full combustion of the fuel, reducing the amount of smoke.Figure 10
Smoke opacity vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.4.1. HC Emission

The graphs of HC emissions vs. load are shown in Figure8. According to the results, it was deduced from the graphs that the HC emissions rose for all mixed fuels. Additionally, the HC emissions increase along with the load. This is the result of a shortage of oxygen. For diesel, and the corresponding blends, the HC emissions were 32, 56, 37, 95, 123, and 130 ppm at 20 percent load. The decrease in operating temperature brought on by the longer delay time and the decrease in the cetane number value contributed to the increase in HC emissions. The amount of HC emission seen in this study is closer in value to that found in previous studies [48, 49]. The AVL 444DI gas analyzer, which has an HC measurement accuracy of ±15 ppm and a percentage uncertainty of ±0.2, was used to measure the HC emission.Figure 8
HC emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.4.2. No Emission

Figure9 shows that using biodiesel results in higher NO emissions. NO emissions rose with load for different fuel mixes. The increase in NO emissions as the percentage of pumpkin-maize biodiesel rises may be explained by the high combustion. Additionally, since biodiesels contain more oxygen, they burn more thoroughly, producing greater peak combustion temperatures and higher NO emissions [50]. The B10, B20, B30, B40, and B50 release around 402, 363, 311, 301, and 250 ppm NO at 20% load, respectively, when compared to diesel. The NO emissions were found to be 17.2% lower than diesel at 100% load for the B20 blend with DEE additive-based alternative fuel.Figure 9
NO emission vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)The AVL 444DI gas analyzer, whose accuracy in measuring NOx is ±0.20 ppm and whose percentage uncertainty is ±0.2, was used to measure the emissions.


## 4.4.3. Smoke Opacity (%) Emission

Figure10 displays the smoke level for various load conditions using different PME mixes with DEE additives. The smoke opacity of diesel engine exhaust is used to determine how much smoke will be produced. The primary factors that determine smoke opacity are fuel composition and oxygen [51]. The viscosity, volatility, and quality of the fuel air combination are other factors, which affect the fuel’s ability to atomize and that lead to smoke generation. The graph demonstrates that, as the load rises, smoke opacity also increases for all the fuels. The smoke opacity was greater for all biodiesels as contrasted to diesel, except for B20 biodiesel, which shows nearly the same smoke opacity as that of diesel fuel [52]. Slow combustion and hence higher smoke levels are caused by the poor atomization and vaporisation behavior of biodiesel mixes. When contrasted with diesel at 100% load, the B20 mix produced approximately the same amount of smoke (3.6%). This is because the B20 mix aids in the oxidation and full combustion of the fuel, reducing the amount of smoke.Figure 10
Smoke opacity vs. load for different biodiesel blends: (a) experimental results; (b) contour plot; (c) surface plot; (d) actual vs. predicted graph.
(a)(b)(c)(d)


## 4.5. ANN Modelling

Tables3 and 4 show the correlation coefficient R2 for training, preparation, and validation data of ANN models. Figure 11 shows the setup, validation, analysis, and general regression studies of the trained network model. By training the ANN on a massive amount of experimental data, the mean squared error (MSE) efficiency measure between the ANN replies and the target data was systematically decreased. Less hidden nodes in ANN modelling have been shown to be more effective. However, in order to mimic the results of the ANN modelling for BTE (related to Figure 3) and NOx emissions (details are given in Figure 9), forty synapses and thirty synapses in the hidden node’s framework were employed, respectively (Figure 11). The anticipated data from the ANN was compared to the test data. As can be seen, utilising ANN significantly improves the outcome of the estimation. Therefore, ANN was effective in predicting engine performance and emission features of different biodiesels.Table 3
ANN outcomes for forecasting BTE.
Number of hidden layer neuronsMSEAll data100.90526150.91528200.91530300.92534400.93419500.92534Table 4
ANN outcomes for forecasting NOx emissions.
Number of hidden layer neuronsMSEAll data100.91305150.92307200.93309250.94313300.95099400.94312Figure 11
ANN modelling with an optimal performance by using (a) 40 hidden layer neurons for BTE and (b) 40 hidden layer neurons for NO emissions.
(a)(b)Using different ML regression models, BTE and NOx predictions were also made. These regression models modelling coefficients for predicting BTE and NOx, respectively, are shown in Tables5 and 6. R2 values for all of these models were, as can be seen, marginally lower than ANN. Figure 12 shows the prediction of BTE using machine learning (ML) algorithms.Table 5
Modelling coefficient for different regression models to forecast BTE.
Regression modelR2MAEMSERMSESVM0.9262.765415.773.59KNN0.9156.903752.561.25Deep learning (RNN)0.9367.675726.175.67Table 6
Modelling coefficient for different regression models to forecast NOx.
Regression modelR2MAEMSERMSESVM0.9463.8724054.163.67KNN0.9361.42501870.83Deep learning (RNN)0.9582.9966888.363.75R2 = coefficient of determination, MAE = mean absolute error, MSE = mean squared error, and RMSE = relative root mean squared error.Figure 12
Prediction of BTE using machine learning (ML) algorithms: (a) SVM results, (b) KNN results, and (c) DL results.
(a)(b)(c)


## 5. Conclusions

The performance, combustion, and emission features of PM hybrid biodiesel with DEE additive were experimentally assessed in this paper. To forecast these features, powerful ML algorithms such as ANN, SVR, K-NN, and deep learning were used. The result is described as follows:(1)
Blending PM with DEE additive increases the BTE at medium and high load ranges; the BSFC and BTE trends for the PM biodiesel blend with DEE addition were decreasing and increasing, respectively. In particular, BTE increased by 31.91% when DEE additive was added to the B20 mix, whereas BSFC, HC, NO emission, and smoke opacity were decreased.(2)
As the load increases, the BSFC declines for the biodiesel mixtures. The B20 blend has a BSFC that is practically equivalent to diesel, whereas other blends have less BSFC. This is due to the biodiesel's high density and high viscosity characteristics.(3)
The addition of biodiesel to diesel decreased the operating temperature of the engine at all load levels; as a result, the HRR for the B20 blend fuel sample at 5° CA was 7.94% lesser than diesel.(4)
The reduction in HC emissions from diesel at a medium load of 60% ranges was achieved by blending alternative fuel (B20 with DEE additive).(5)
The NO emission was increased across all load ranges by mixing pumpkin-maize with diethyl ether. The NO emissions were found to be 17.2% lower than diesel at 100% load for the B20 blend with DEE additive-based alternative fuel.(6)
At half load of 60% ranges, the addition of alternative fuel (B20 with DEE additive) to diesel increases the smoke emission by 3.6%. Compared to other mixes of PM biodiesel, samples of 20 percent blended biodiesel emit almost the same amount of diesel.(7)
The experimental value and the predicted value from the RSM technique were close to each other, implying a better fit of the RSM models, which can be used for optimizing the parameters.(8)
Using ANN, BTE, and NO were predicted withR2 values of 0.93 and 0.95, respectively. As can be observed, the R2 value for the ANN model was almost one, suggesting that the ANN models had better predictive power than other ML models.



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*Source: 9505424-2022-10-08.xml*