Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.
Conventional sources of energy, such as crude oil and coal, have been dwindling at an alarming rate in recent years. This trend is most likely attributable to the increase in population that has been seen around the world[
Recently, biodiesel-based fuels are blended with a variety of nanoparticles presumably to supress the disadvantages associated with using it in diesel engine. Initially distilled water and nanoparticle are amalgamated thoroughly to produce whitish fluid which is later on added in biodiesel. The prime reason of amalgamating nanoparticles is the formation of structured layers in the fuel where these particles might occupy different locations and form heat generating pockets. This facilitates faster combustion due to increase in thermal conductivity of oil. This further reduces the overall delay time providing a wholesome combustion with smoother operation. Also, applying nanoparticles in fuels furnish higher atomization, thereby providing proper mixing as molecules are broken down into little ones with particles embedded between layers.
The use of artificially intelligent (AI) models, which forecast the performance-emission features of petro-diesel engine, is done so in order to get an understanding of the link between the many input parameters of a diesel engine and its many outputs. This effectively cuts down on the number of experimental runs, which in turn cuts down on both operating costs and the amount of time required for the assessment of performance (BTE and BSEC) and exhaust parameters (CO, UBHC and NOx). Through the use of AI in conjunction with various optimization strategies, the predictive models provide, in addition, the optimal combination of inputs that might possibly be generated. Various researchers have used the ANN approach in the course of earlier engine-related investigations in order to cut down on the number of operations and achieve noteworthy results[
Gopal et al.[
In light of the aforementioned developments, the scholars have come up with the following viewpoints, which are outlined below:
- Biodiesel derived from landfills, when combined with nanoparticles and applied to diesel engines, has the potential to successfully improve performance parameters while simultaneously lowering emissions from diesel engines. This is because the application of biodiesel derived from landfills boosts up the performance parameters of a diesel engine.
- The use of waste oils as a potential solution is shown to be a workable and practicable alternative on account of their simple accessibility and their little impact on the surrounding natural environment.
- The investigation of engine outputs for a variety of nanoparticles and their concentrations while using soft computing approaches such as ANFIS, ANFIS-GA, and ANFIS-NSGA-II has never been discussed in any of the prior literature.
- Previous research, conducted in other subfields of thermal engineering, has shed light on the significance of integrating exceptional forecast representations with optimization systems to produce exact petro-diesel engine variables while simultaneously reducing the amount of effort, cost, labour, time, and energy required.
To the authors fullest knowledge very few research work was available online regarding AI based optimization of diesel engine. Furthermore, application of meta-heuristic techniques in diesel engine still remains an unexplored domain while generating outputs for biodiesels. The authors keeping in mind the above trend were motivated to perform a performance and exhaust analysis of biodiesel while varying for prime input parameters with the aid of multiple hybrid metaheuristic techniques.
The current research applied the ANFIS model as output prediction and further integrated the objective functions by optimizing the outcomes with the aid of genetic algorithms (GA) and its higher version (NSGA-II). As a result, the influence of a number of input factors, such as load, blend%, nanoparticle concentration (NPC), and injection pressure (IP), may be investigated using these models in a way that is efficient and inexpensive. When the expected answers of ANFIS were put up against the results gained through experimental assessments, it was discovered that ANFIS's predictions were fairly accurate. The ANFIS-GA model developed is remarkably capable of replicating the conventional method for engine output evaluations with low error rate. Furthermore, another version of genetic algorithm called non-sorting genetic algorithm (NSGA-II) is employed to test its viability in diesel engine, thereby comparing output responses for all models.
This research is groundbreaking in its use of prognostic systems (ANFIS) and optimization practices (GA and NSGA-II) to enhance the performance of engines and decrease emissions by utilizing nanoparticles in landfill oils. Notably, no prior studies have investigated nanoparticle variation by prediction models in this area. The implementation of a novel model in the biofuel industry minimizes the number of engine runs required to achieve accurate predictions through rigorous data training, testing, and validation, underscoring the innovative and precise nature of this study. In the realm of diesel engines, the introduction of soft computing methods in conjunction with optimization approaches will provide results that are revolutionary.
The objectives of this research are as follows: Firstly, to conduct a performance and exhaust analysis of biodiesel by varying prime input parameters, namely load, blend %, nanoparticle concentration (NPC), and injection pressure (IP), using hybrid metaheuristic techniques. Next, to utilize the ANFIS model for output prediction and integrate objective functions for optimizing outcomes using Genetic Algorithms (GA) and its higher version (NSGA-II). Furthermore, to investigate the efficiency of the ANFIS-GA model in evaluating engine outputs by comparing its predictions against experimental assessments. Finally, to assess the viability of the non-sorting Genetic Algorithm (NSGA-II) in the context of diesel engine performance and exhaust analysis, while comparing its output responses with other models employed in the study. By achieving these objectives, the research aims to contribute to the advancement of AI-based optimization techniques for diesel engines and the understanding of biodiesel performance characteristics.
In order to get the best possible outcome, it is necessary to carry out preliminary functional operations such as specifying the input and output variables in advance. The experimental data set is created by varying four parameters: load (LD), blend percentage (BP), nanoparticle concentration (NPC), and injection pressure (IP). The proposed input will be measured against this standard to ensure it achieves the highest possible thermal efficiency in the brakes, the lowest possible specific fuel consumption, and the cleanest possible exhaust gas emissions (including CO, NOx, and UBHC). In this study, we provide several hybrid methods for comparing the experimental and anticipated data, which we do in four stages. The steps involved are as follows: (i) aggregating the acquired experimental data and grouping it based on training and testing; (ii) selecting the best performance model in the ANFIS & RSM data structure for assessing the performance and exhaust emission parameters; (iii) integrating the results of the ANFIS & RSM model with the GA and NSAGA-II optimisation technique; and (iv) finally, generalising the optimised results, including maximum BTE, minimum BSEC (CO, NOx and UHBC).
The diesel engine test rig that was fueled with landfill food waste oils served as the source of the experimental dataset that was used in the development of the prediction ANFIS model. These oils were linked together with two nanoparticles at the same time, namely aluminium oxide (Al
Table 1 Specifications of measuring instruments.
Parameter Apparatus Measuring range Equipment model Brake power Dynamometer 0–1000 kW DynoTech 2000 Torque Dynamometer 0–5000 Nm TorqMaster Pro 5000 Fuel consumption Fuel flow meter 0–1000 L/h FlowTech FLM-1000 Exhaust gas temp Thermocouple 0–1200 °C TempProbe TC-1200 NOx emissions NOx analyzer 0–1000 ppm AVL gas analyser CO emissions CO analyzer 0–10% AVL gas analyser UBHC emissions Hydrocarbon analyzer 0–1000 ppm AVL gas analyser Cylinder pressure Pressure transducer 0–200 bar PressSureMaster PT-200 Injection pressure Pressure transducer 0–2000 bar InjexiTech IPX-2000
This section in particular covers the process of acquiring leachate oil and nanoparticles (Al
Graph: Figure 1Generation of landfill oils at a landfill site, landfill oil and landfill biodiesel sample.
In Fig. 2, the engine configuration that was utilised to calculate the performance and emission characteristics for biodiesel-based fuels is provided for the reader's convenience. The CRDI diesel engine served as the testing ground for the first experiments. The eddy current dynamometer is used to measure and adjust the amount of load that is placed on the engine. The voltmeter and the current metre are both components of the resistive type load panel. Controlling the engine torque in the range of 0 to 18 kg required the employment of a diesel engine and an eddy-current dynamometer in conjunction with each other. A thermocouple of the K type was used in order to get an accurate reading of the exhaust gas's temperature. The exhausts from the engine predominantly consist of UBHC, CO, and NOx emissions, all of which were shown on the screens of the gas analysers labelled "AVL Di Gas 444". The many technical particulars of the engine configuration are outlined in Table 2, which can be found below.
Graph: Figure 2Experimental setup of PETTER-AV engine.
Table 2 Mechanical particulars of the petro-diesel engine.
S. no. Module Description 1 Main engine model KIRLOSKAR 2 Engine design 4-stroke, petro-diesel engine 3 Bore of engine cylinder 90 mm 4 Length of cylinder 130 mm 5 Power production 17 BHP at 4000 rpm 6 Compress ability 18:1
In order to further examine the practicability of using the produced fuel in a CI engine, varied combinations of petro-diesel and nano-particles were added to the mixture. As can be shown in Table 3, the physio-chemical parameters of leachate oil are quite similar to those of regular diesel, which suggests that it would be suitable for use in an engine. The advantage of the fuel in the injection process was further increased by other advantages, such as a reduction in viscosity. A capillary viscometer was used in order to determine the thickness of the fuel mixtures. A bomb calorimeter equipment was used to measure the calorific value of the fuel while a hydrometer was used to estimate the density of the fuel. As can be seen in Table 3, the final test blends of aluminium oxide biodiesel (abbreviated as ABD) and copper oxide biodiesel (abbreviated as CBD) were determined to be in compliance with the ASTM criteria, as shown by the physiochemical parameters of the blend.
Table 3 Comparison for physiochemical properties of the test fuels.
Properties ABD CBD LFB test fuel Diesel ASTM limit Density at 15 °C (kg/m3) 888 893 975 841 860–900 Kinematic viscosity (cSt) 3.64 3.52 3.99 4.56 2.52–7.5 Calorific value (MJ/kg) 47.5 45.5 39.12 44.85 Min. 33 Flash point (°C) 69.83 65.71 70.45 51 Min. 130 FFA (%) – – 0.77 0.0014 Max. 2 Fire point (°C) 59 57 47 58 Min. 53
An early attempt to tackling the problem of connecting the input parameters with the output parameters is offered by the RSM method. In order to calculate the outputs and decide which equation best matches them, a specialised CCRD design was used, and sixty test runs were performed. In addition to this, the built architecture provides newly calculated extreme values (both low and high) for each variable[
Table 4 Level of experimental parameters.
S. no. Input parameters Unit Level 1 Level 2 Level 3 Level 4 Level 5 1 Load (L) (%) % 20 40 60 80 100 2 Blend (B) (%) % 0 5 10 15 20 3 Injection pressure (IP) (bar) Bar 180 190 200 210 220 4 Nano particles concentration (NPC) (%) % 0 5 10 15 20
The analysis makes use of a number of different control variables, numerical and coded data, and a CCRD array that was built specifically for it. In all, there were sixty different runs. A summary of the findings obtained from sixty separate test runs conducted using a variety of engine input parameters.
The analysis contains four variables, each of which is shown in Table 4 along with their respective ranges. The numerical values that were used in the array that was specifically developed for your application and consisted of a total of 60 runs Table 5 presents a summary of the findings obtained from sixty separate test runs conducted.
Table 5 Experimental outcomes from diesel engine.
Input conditions ABD CBD Trial Appl Mix % IP NA BTE BSEC UBHC CO NOx BTE BSEC CO NOx 1 20 15 180 5 16.17 3.7149 7.9099 0.0143 170.3 15.561 4.32 0.018 197 2 40 5 190 0 13.93 3.5014 8.7676 0.0123 172.5 13.406 4.1 0.016 200 3 60 10 200 20 20.89 2.7328 6.0992 0.0185 145.3 20.11 3.2 0.024 168 4 80 0 210 15 18.65 2.5193 7.4334 0.0165 147.6 17.955 2.95 0.021 171 5 100 20 220 10 22.38 3.3306 5.9086 0.0198 204.3 21.546 3.9 0.025 237 6 20 5 180 5 14.18 3.3733 8.6723 0.0125 152.1 13.646 3.95 0.016 176 7 40 15 190 10 18.4 3.416 7.0522 0.0163 168 17.716 4 0.021 195 8 60 10 200 0 15.92 3.5868 8.0052 0.0141 190.7 15.322 4.2 0.018 221 9 80 0 210 15 18.65 2.5193 7.7193 0.0165 147.6 17.955 2.95 0.021 171 10 100 20 220 20 24.87 2.9036 4.9556 0.022 181.6 23.94 3.4 0.028 210 11 20 0 180 0 11.94 3.416 9.53 0.0106 154.4 11.491 4 0.013 179 12 40 5 190 5 15.17 3.2879 8.2911 0.0134 161.2 14.603 3.85 0.017 187 13 60 10 200 10 18.4 3.1598 7.0522 0.0163 168 17.716 3.7 0.021 195 14 80 15 210 20 22.88 2.8182 5.8133 0.0202 163.4 22.025 3.3 0.026 189 15 100 20 220 15 23.63 3.1171 8.1005 0.0209 193 22.743 3.65 0.027 224 16 20 0 180 0 11.94 3.416 9.53 0.0106 154.4 11.491 4 0.013 179 17 40 10 190 5 16.17 3.4587 7.9099 0.0143 170.3 15.561 4.05 0.018 197 18 60 5 210 10 17.66 3.0317 7.5287 0.0156 161.2 16.997 3.55 0.02 187 19 80 15 220 15 21.89 3.0744 6.3851 0.0194 177.1 21.067 3.6 0.025 205 20 100 20 200 20 24.37 2.8182 4.765 0.0216 177.1 23.461 3.3 0.027 205 21 20 0 190 0 12.19 3.4587 9.4347 0.0108 156.6 11.731 4.05 0.014 181 22 40 5 200 5 15.42 3.3306 8.1958 0.0136 163.4 14.843 3.9 0.017 189 23 60 10 180 10 17.91 3.0744 7.2428 0.0158 163.4 17.237 3.6 0.02 189 24 80 15 210 15 21.64 3.0317 6.2898 0.0191 174.8 20.828 3.55 0.024 203 25 100 20 220 20 24.87 2.9036 4.9556 0.022 181.6 23.94 3.4 0.028 210 26 20 0 180 0 11.94 3.416 9.1488 0.0106 154.4 11.491 4 0.013 179 27 40 15 200 5 17.41 3.6722 7.4334 0.0154 181.6 16.758 4.3 0.02 210 28 60 10 190 10 18.16 3.1171 7.1475 0.0161 165.7 17.476 3.65 0.02 192 29 80 20 210 15 22.63 3.2025 5.9086 0.02 183.9 21.785 3.75 0.025 213 30 20 5 220 20 18.9 2.9036 7.2428 0.0167 127.1 18.194 3.4 0.021 147 31 100 0 190 0 15.17 2.9463 8.2911 0.0134 183.9 14.603 3.45 0.017 213 32 40 5 180 5 14.92 3.2452 7.624 0.0132 158.9 14.364 3.8 0.017 184 33 60 10 200 10 18.4 3.1598 7.0522 0.0163 168 17.716 3.7 0.021 195 34 80 15 210 15 21.64 3.0317 6.2898 0.0191 174.8 20.828 3.55 0.024 203 35 100 20 220 20 24.87 2.9036 6.0992 0.022 181.6 23.94 3.4 0.028 210 36 20 0 180 20 16.91 2.562 7.624 0.015 109 16.279 3 0.019 126 37 40 5 190 5 15.17 3.2879 8.2911 0.0134 161.2 14.603 3.85 0.017 187 38 100 10 220 5 21.64 3.2025 7.1475 0.0191 197.5 20.828 3.75 0.024 229 39 80 15 210 10 20.39 3.2452 6.7663 0.018 186.1 19.631 3.8 0.023 216 40 60 20 200 0 17.91 3.9284 7.2428 0.0158 208.8 17.237 4.6 0.02 242 41 20 0 200 0 12.44 3.5014 9.3394 0.011 158.9 11.97 4.1 0.014 184 42 40 5 190 5 15.17 3.2879 8.2911 0.0134 161.2 14.603 3.85 0.017 187 43 100 10 180 10 19.4 2.8182 6.671 0.0172 177.1 18.673 3.3 0.022 205 44 60 15 210 15 20.89 3.1598 6.2898 0.0185 168 20.11 3.7 0.024 195 45 80 20 220 20 24.12 3.0317 7.4334 0.0213 174.8 23.222 3.55 0.027 203 46 20 0 180 0 11.94 3.416 9.53 0.0106 154.4 11.491 4 0.013 179 47 40 5 200 5 15.42 3.3306 7.2428 0.0136 163.4 14.843 3.9 0.017 189 48 60 10 190 15 19.4 2.9036 6.671 0.0172 154.4 18.673 3.4 0.022 179 49 80 15 210 10 20.39 3.2452 6.7663 0.018 186.1 19.631 3.8 0.023 216 50 100 20 220 20 24.87 2.9036 4.9556 0.022 181.6 23.94 3.4 0.028 210 51 20 0 190 0 12.19 3.4587 9.4347 0.0108 156.6 11.731 4.05 0.014 181 52 40 5 180 5 14.92 3.2452 8.3864 0.0132 158.9 14.364 3.8 0.017 184 53 80 10 200 10 19.15 3.0317 7.0522 0.0169 174.8 18.434 3.55 0.022 203 54 60 15 220 15 21.14 3.2025 6.3851 0.0187 170.3 20.349 3.75 0.024 197 55 100 20 210 20 24.62 2.8609 4.8603 0.0218 179.3 23.701 3.35 0.028 208 56 20 5 180 0 12.93 3.5868 9.1488 0.0114 163.4 12.449 4.2 0.015 189 57 40 0 190 5 14.18 3.1171 8.6723 0.0125 152.1 13.646 3.65 0.016 176 58 60 10 200 10 18.4 3.1598 7.0522 0.0163 168 17.716 3.7 0.021 195 59 80 15 210 20 22.88 2.8182 5.8133 0.0202 163.4 22.025 3.3 0.026 189 60 100 20 220 15 23.63 3.1171 5.4321 0.0209 193 22.743 3.65 0.027 224
The primary model employed in this study was the Takagi–Sugeno artificial neuro-fuzzy interface system of the first order approach that was used for the research (ANFIS). In order to assess the performance and exhaust characteristics, the experiment was designed on the basis of the model described above and shown in Fig. 3. These results are being taken into consideration as the primary goal functions for this study. Previous research have already created models that are conceptually comparable to this one. However, when applied to thermal engineering applications, these models were complicated, time-consuming, and erroneous owing to the restricted, nonlinear, and uncertain dataset[
Graph: Figure 3Framework of ANFIS model.
Table 6 ANFIS framework for training the diesel engine-based model.
Over-all quantity of nodes 203 Quantity of linear limitations 104 Amount of non-linear strictures 27 Sum of training information pairs 51 Amount of rules that are fuzzy 99 Relationship role Triangular
In order to develop a variety of reactions via modelling, the following ANFIS equations were implemented.
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In most cases, the process of modelling and optimising an engine based on its features starts with the creation of a precise fitness function that is appropriate for the level of difficulty of the problem statement. The traditional approaches that are used to construct objective function for a number of input and output parameters need a significant amount of time and effort to complete. However, since it is able to produce the data without the need for any prior model history, the ANFIS methodology is able to provide an adequate objective function. This is the primary reason why this method is recommended. Utilizing a genetic algorithm in the processing of output replies allows for further refining of estimations and predictions that have been derived using the ANFIS method, which results in improvements in both accuracy and efficiency.
When it comes to multi-objective optimization, it is often seen that the outputs of the ANFIS approach are trapped inside the local optima, which indicates that the technique may not be 100% correct[
Table 7 GA algorithm framework.
Category of assortment technique Roulette wheel Populace gauge 85 Repetitions 3400 Switch-over (%) 0.85 Alteration proportion (%) 0.85 Equal of alteration 0.9 Assortment weight 12
Graph: Figure 4Algorithm of RSM-GA (left) and ANFIS-NSGA-II algorithm (right).
To enhance its performance in meeting the general norms for petro-diesel machines, the model undergoes further optimization using the NSGA-II algorithm. This algorithm, known for its elitist structure, has demonstrated success in engineering applications for multi-function optimization, top to quicker meeting and priority-based categorization[
In the optimization process, the input variables are evaluated for Pareto optimality and then fed into the Fuzzy Interface System (FIS) framework[
Table 8 NSGA-II optimization process requirements and parameters.
Parameter Value Objectives Max BTE, Min BSEC, Min NOx, Min CO and Min UBHC Variables X1 = load, X2 = blend percentage, X3 = ignition pressure, X4 = nanoparticle concentration Bounds 20 ≤ X1 ≤ 100, 0 ≤ X2 ≤ 20, 180 ≤ X3 ≤ 220, 0 ≤ X4 ≤ 20 Populace type Double vector Populace size 300 Collection function Tournament Cusp portion 0.8 Transformation portion 0.1 Ending norms Generations: 1000/stall generations: 100
Primary aim of developing an optimization model is to maximize the BTE and minimize BSEC, NOx, UBHC and CO simultaneously A detailed flowchart is presented below Fig. 5 highlighting the steps involved in ANFIS-NSGA-II approach in generating a multi-objective output for performance and exhaust emission of landfill waste biodiesel.
Graph: Figure 5Flowchart for the applied prediction model of ANFIS.
A combined flow chart representing the various processes accompanied in this research is shown in Fig. 6.
Graph: Figure 6Combined flow chart for various processes accompanied in this research.
The NSGA-II algorithm was utilized to optimize the interdependency matrix and generate optimal responses. The following steps were undertaken in the application of NSGA-II algorithm:
- Step one: Random primary population is created of size N.
- Step two: The population formed in step one sorted by means of fast non-dominated sorting until the entire population is classified into numerous fronts.
- Step three: Crowding distance task is performed for every evaluation and crowded tournament assortment is allocated. This picks a combination at a improved rank if the combinations belong to various fronts or a answer with a higher crowding distance if they belong to the identical front.
- Step four: Crossover and mutation is applied to the parent population obtained above to yield child population. To generate new offspring's, simulated binary crossover (SBX) operator and polynomial mutation operators are used.
- Step five: The parent and child population are joint together to yield a population of size 2N.
- Step six: Stopping criteria is checked. If the Pareto optimal front is accomplished, then algorithms is stopped else repeat and move to step two.
All major data applied and generated in the ANFIS models. Statistical tools such as the coefficient of determination (R
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where,
RMSE = Root Mean Square Error, R
In this research, output responses were measured by employing measuring apparatuses, chemical formulas and empirical relations depending upon the type of response. In the present study six output parameters were considered and their measurement method is described as follows:
This maybe defined as the ratio of the applied brake power (BP) attained at the crankshaft to total energy (E) available to diesel engine for combustion process as shown in Eq. (
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where, η is the brake thermal efficiency (in %); BP is the Brake power (in kW) and E is the fuel energy (in kW).
This quantity is a hypothetical tool which indicates the total energy required from the fuel to produce unit power. Over the years it has become a powerful tool and somewhat replaced the BSFC value. Fuels are compared for fuel efficiency with this tool only. BSEC is the calorific value (CV) times brake specific fuel consumption (BSFC) and used to prepare a comparison among various fuels. The specific energy consumption is a more accurate estimate in comparison to specific fuel consumption. It is given by Eq. (
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where, CV is the calorific value (in kJ/kg) and BSFC is the brake specific fuel consumption (in kg/kWh).
Absence of the necessary quantity of oxygen, hastens improper combustion process, which moreover results in identified effluent gases such as unburned hydrocarbons (UBHC), carbon oxide (CO) and oxides of nitrogen (NOx). The AVL DIGAS 444 exhaust gas analyzer was employed to measure the volumes of CO emissions in terms of percentage (%) and UBHC and NOx emissions as ppm.
The occurrence of ambiguity during experimentation is probably attributable to a wide variety of causes, some of which may be categorised as instrument error, measurement error, surrounding circumstances, measurement methods, and the kind of instrument. Therefore, in order to determine and provide a feeling of clarity in the measured output answers, each attribute is counted twice for each inquiry run. The error analysis was carried out with the submission of squares for each and every discrete piece of equipment that was measured during the investigation[
Table 9 Errors and uncertainties associated with all instruments.
Measurements Instrument Range Accuracy Engine load Strain gauge type load cell 0–25 kg ± 0.1 kg Speed Speed sensor 0–10,000 rpm ± 20 rpm Engine power – 0–50 kW ± 1.0% Fuel consumption Level sensor – ± 1.0% Air consumption Turbine flow type – ± 1.0% BTE – – ± 1.0% BSEC – – ± 1.5% UBHC Gas analyzer Ppm ± 0.1% CO Gas analyzer g/kWh ± 0.2% NOx Gas analyzer Ppm ± 0.1%
The complete proportion of error was valued during the investigation using Eq. (
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The general percentage of uncertainty (U) = square root of [(Error level in BTE value)
The overall percentage uncertainty = Square root of [ (1.0)
The general fraction of uncertainty = ± 2.38%.
The entire uncertainty level throughout experimentation is projected to be ± 2.38%, thus lying-in acceptable range.
The research employs the ANFIS model which is capable of establishing a feasible relationship between considered inputs and engine performance such as BTE and BSEC and emissions parameters such as UBHC, NOx and CO for both the nanoparticles. Blends of landfill food waste oil was prepared and amalgamated with various nanoparticles in different concentrations. The following input variables combinations might lead to a substantially large data set which consequently furnishes generation of enormous experimental responses eventually consuming time, labour and energy and fuel. The research suggests implementation of a fusion strategy (ANFIS) which facilitates efficient and effective output prediction even for a smaller dataset with negligible errors. The input dataset developed through RSM were trained and validated according to the Sugeno-type fuzzy inference system which primarily works on a complex algorithm employing least square model and the back- propagation gradient descent procedure[
Graph: Figure 7Developed FIS framework with 81 rules for various engine outcomes.
Graph: Figure 8Primary ANFIS data development for a random model (NOx).
Graph: Figure 9Separate system for each outcome constraints is separately premeditated for set of constarints.
To simplify the bend tracing amongst constraints and outcomes for both nano-additives (ABD and CBD), 3-D graphs are established to indicate the inter-relationship amongst any 2 constraints out of four and a one specific outcome, as displayed in various figures. Figure 10 shows plot for BTE versus various input parameters. Figure 11 shows plot for BSEC versus various input parameters. The blend percentage of biodiesel-diesel and the concentration of nanoparticles affect the combustion characteristics and energy release, influencing BSEC. Additionally, the injection pressure plays a crucial role in fuel atomization and distribution, which can alter BSEC. The load percentage directly affects the engine's power requirements and overall efficiency, thus impacting BSEC. Figure 12 shows plot for CO versus various input parameters. The injection pressure plays a critical role in fuel atomization, which can impact the combustion efficiency and subsequently affect CO emissions. Additionally, the load percentage directly influences the engine's power demand and combustion conditions, further affecting CO emissions. Figure 13 shows plot for NOx versus various input parameters nanoparticle concentration affect the combustion process, altering the combustion temperature and oxygen availability, which can lead to variations in NOx formation. Moreover, the load percentage directly influences the engine's power demand and combustion conditions, further affecting NOx emissions. While Fig. 14 is for UBHC as the blend percentage of biodiesel-diesel and nanoparticle concentration affect the fuel–air mixing, which can impact the wholesome combustion process and lead to variations in UBHC emissions. Additionally, the load percentage directly affects the engine's power demand and combustion conditions. Application of ANFIS technique has prognostic efficient outputs identical to those previous experimental generated values employed for training and testing the system for all output responses. The forecasted values for all outcomes, estimated by ANFIS model are graphed in comparison to conventional experimented data in Figs. 15, 16, 17, 18, and 19 respectively to justify systems reliability and correctness.
Graph: Figure 10Surface plots for BTE vs Input conditions for ABD (aa–af) and CBD (ba–bf).
Graph: Figure 11Graphs for BSEC vs constraints ABD (ca–cf) and CBD (da–df).
Graph: Figure 12Graphs for CO vs constraints for ABD (ea–ef) and CBD (fa–ff).
Graph: Figure 13Graphs for NOx vs constraints for ABD (ga–gf) and CBD (ha–hf).
Graph: Figure 14Graphs for UBHC vs Constraints for ABD (ia–if) and CBD (ja–jf).
Graph: Figure 15(a) Comparative study of observational and expected outcome for BTE Training. (b) Comparative study of observational and expected outcome for BTE Testing.
Graph: Figure 16(a) Comparative study of observational and expected outcome for BSEC training. (b) Comparative study of observational and expected outcome for BSEC testing.
Graph: Figure 17(a) Comparative study of observational and expected outcome for CO training. (b) Comparative study of observational and expected outcome for CO testing.
Graph: Figure 18(a) Comparative study of observational and expected outcome for UBHC training. (b) Comparative study of observational and expected outcome for UBHC testing.
Graph: Figure 19(a) Comparative study of observational and expected outcome for NOx Training. (b) Comparative study of observational and expected outcome for NOx testing.
Implementing a genetic algorithm in the output answers of the RSM approach allows for additional fine-tuning, which results in better accuracy and efficiency. Estimations and predictions produced using the RSM technique may be used in this way. The use of RSM-GA eradicates the inaccuracies that are caused by the steep descent approach, which causes the results to get trapped inside the local optimum. Utilization of a GA algorithm that randomly searches for solutions enables replies that are both cost-effective and efficient. The genetic algorithm is a kind of adaptive combinatorial search algorithm that is based on the fundamental idea behind biological evolution. This means that the permutations are generated based on the combination of a parent and a kid. The model optimises the fitness functions that are created by the RSM, and as a result, it is able to acquire the most optimal combination of inputs that will result in the best results. In the past, researchers in the field of thermal engineering have confirmed the use of GA by comparing it to other multivariate approaches that take longer to provide results[
A fitness function value was generated and defined in the workspace of the software. The development of the fitness equation involved considering the mean statistical error values for all datasets between the predicted and experimental responses. The fitness function was recalled when inputting values into the GA toolbox. The combination of soft computing techniques with an optimization model (GA) provides a faster and more efficient architecture procedure, resulting in the best of both systems. Comparative results are displayed in Figs. 15, 16, 17, 18, and 19. To validate the fitting function, R
Initially input data conditions are uploaded in the FIS model, which generates separate outcomes individually for each model. In particular, current setup is again reoptimized with the help of using capable NSGA-II procedure which will establish the outcomes of the study in a much more clear way. The constraints of the study conform to global ethics for petro-diesel engine operation. The advanced system of NSGA-II system can be excellently functional in multiple domains for variable-functional convergence procedures, pertaining to nearer approximations[
The ANFIS-NSGA-II model is a hybrid model that combines the advantages of both soft computing techniques and optimization models. It was successful in forecasting exact outcomes within advanced oversimplification ability for performance-emission parameters for petro-diesel machines. This model provides a Pareto optimal front set that represents the finest counter and validation among the considered objective functions predefined within the system. The improved outcomes extracted with the aid of above procedure can be explained through values presented Table 10, where ideal load application for engine can be 100%, ideal mix ratio can be in following ratio which is 20%, ideal nano-additive requirement in the blend is close to 20 ppm, while ideal pressure inserted within the cylinder can be 200 bar. These conditions yield BTE to be 24.45 kW, BSEC to be 2.761784 NOx to be 159.5488 ppm, UBHC to be 4.687807 ppm, and CO to be 0.020243%. A favourite prioritization is established in case of the attained outputs of the study where BTE is assigned with zenith while others are assumed with utmost similarly.
Table 10 Pareto optimal front set.
Trial run Load Blend % IP NPC BTE BSEC NOx UBHC CO 26 100 20 220 20 24.86 2.74 163.63 4.87 0.020 49 60 10 190 15 13.35 2.11 138.93 6.57 0.015 37 20 0 180 20 16.89 2.51 98.18 7.50 0.014 21 100 20 200 20 24.43 2.76 159.54 4.68 0.020 47 20 0 180 0 11.11 3.36 138.93 9.38 0.010
Moreover, the developed fitness equation was processed by taking into consideration mean numerical fault standards for above datasets amongst the investigational and prognostic outputs. While alimentation data's are fed into the GA, the appropriateness function was recalled. This hybrid model provides the finest of mutually organizations, permitting a sooner and well-organized planning procedure. Additionally, the ANFIS-NSGA-II model was compared with other models such as ANFIS and RSM-GA, and it aligned itself closest to the generated experimental values, furnishing superiority to the other models for data prediction. Thus, the overhead declaration rationalizes and confirms the appropriateness of the perto-diesel machine characteristics analysed by commissioning the ANFIS-NSGA system to investigate and augment variables.
The outcomes predicted by hybrid models (ANFIS, RSM-GA and ANFIS-NSGA-II) were evaluated on the basis of regression formulas such as root mean square error (RMSE) and fraction of variance R
Table 11 Comparative study of various BTE models.
ANFIS RSM-GA ANFIS-NSGA Training Testing Training Testing Training Testing ABD RMSE 0.30287 0.438 0.2838 0.3915 0.2105 0.3793 R2 0.9075 0.8872 0.9856 0.9037 0.9985 0.9273 CBD RMSE 0.325 0.511 0.320 0.491 0.313 0.473 R2 0.941 0.893 0.955 0.915 0.967 0.921
Table 12 Comparative study of various BSEC models.
ANFIS RSM-GA ANFIS-NSGA Training Testing Training Testing Training Testing ABD RMSE 0.3349 0.541 0.2951 0.5121 0.2721 0.4971 R2 0.9294 0.8815 0.9318 0.9151 0.9464 0.9584 CBD RMSE 0.3411 0.4544 0.339 0.4293 0.3252 0.3999 R2 0.8834 0.8756 0.9067 0.8921 0.9481 0.9061
Table 13 Comparative study of various UBHC models.
ANFIS RSM-GA ANFIS-NSGA Training Testing Training Testing Training Testing ABD RMSE 0.4715 0.315 0.4578 0.2848 0.4153 0.2726 R2 0.9513 0.9437 0.9739 0.9569 0.9838 0.9851 CBD RMSE 0.4309 0.3534 0.4080 0.3133 0.3981 0.2954 R2 0.921 0.890 0.929 0.911 0.9415 0.925
Table 14 Comparative study of various CO models.
ANFIS RSM-GA ANFIS-NSGA Training Testing Training Testing Training Testing ABD RMSE 0.5825 0.2411 0.5342 0.2251 0.4703 0.2204 R2 0.9281 0.9171 0.9372 0.9283 0.9389 0.9303 CBD RMSE 0.5706 0.3621 0.5666 0.3397 0.5606 0.3320 R2 0.9201 0.910 0.9510 0.919 0.9555 0.935
Table 15 Comparative study of various NOx models.
ANFIS RSM-GA ANFIS-NSGA Training Testing Training Testing Training Testing ABD RMSE 0.7531 0.7877 0.7328 0.7515 0.6981 0.7407 R2 0.8641 0.8301 0.8901 0.8751 0.9011 0.9001 CBD RMSE 0.7309 0.801 0.7245 0.7696 0.7099 0.7511 R2 0.8525 0.8441 0.8695 0.8691 0.8909 0.8899
Graph: Figure 20Comparative study of various BTE models.
Graph: Figure 21Comparative study of various BSEC models.
Graph: Figure 22Comparative study of various UBHC models.
Graph: Figure 23Comparative study of various CO models.
Graph: Figure 24Comparative study of various NOx models.
Four input parameters were chosen: Blend percentage (B), nano-particle concentration (NPC), engine load (LD), and ignition pressure (IP). The feasibility of these parameters was confirmed through experimental results from previous research. The researchers considered the feasible range of each parameter that strongly influenced the output responses. For Blend percentage, the range was set between 5 and 20%, as beyond 20% would result in high viscosity and low density, requiring modifications to the original engine design and making the process infeasible and expensive. Similarly, NPC was limited to 20 ppm, as exceeding this concentration would lead to excessive deposition and increased aggregation of nano-additives, causing segregation among fuel blends. Regarding Load, levels below 20% were not considered as they presented small negligible variations in engine performance and exhaust analysis, and thus were not deemed influential enough to be included in the study. Finally, IP was restricted to the range of 180–220 bars, as values above 220 bars would lead to abnormal engine temperatures, potentially resulting in engine seizure or malfunction.
In the quantitative analysis, the researchers utilized statistical and mathematical methods to analyze the relationship between the selected input parameters and the output responses. They conducted numerous experimental runs by varying the levels of blend percentage (B), nano-particle concentration (NPC), engine load (LD), and ignition pressure (IP) as per the chosen discrete levels within their feasible ranges. The performance-emission characteristics of the biodiesel engine were recorded for each experimental run, generating a comprehensive dataset. Statistical techniques such as regression analysis, analysis of variance (ANOVA), and correlation analysis were employed to identify the significant effects of individual parameters and their interactions on the engine outputs. This quantitative analysis provided valuable insights into the quantitative contributions of each input parameter to the performance and emission characteristics of the biodiesel engine, allowing for a deeper understanding of their influence and potential optimization strategies.
In the qualitative analysis, the researchers focused on understanding the practical implications of the selected input parameters in real-world engine applications. They considered the physical limitations and engineering constraints associated with the chosen input parameter ranges. For instance, the decision to limit the biodiesel blend percentage (B) to 20% was based on the consideration that higher concentrations would lead to unfavorable engine properties, making it impractical for conventional engine designs. Similarly, the restriction on nano-particle concentration (NPC) to 20 ppm was due to potential complications in the engine's valve timing diagram and particle deposition issues. By qualitatively analyzing the effects of each parameter, the researchers ensured that the experimental conditions were not only scientifically relevant but also practically achievable in diesel engine applications. This qualitative assessment provided valuable insights into the feasibility of implementing the findings in real-world scenarios and helped avoid potential engineering challenges or performance issues.
In the part before this one, the intelligent model ANFIS-NSGA showed a lower error rate (RSME) in contrast to the single ANFIS model and the RSM-GA model. In addition to this, a zenith R
Table 16 Comparison of prediction capability of various models and developed ANFIS-NSGA-II.
References Model Fuel RMSE R2 Seraj et al. ANFIS-GA Eucalyptus 3.470 0.38 Khan ANFIS Eichhornia Crassipes 6.426 0.24 Aghbashlo et al. ANFIS-ALFIMO Waste cooking oil 0.423 0.92 BTE (current study) (ABD) ANFIS-NSGA-II Waste food oil 0.210 0.99 BSEC (current study) (ABD) ANFIS-NSGA-II Waste food oil 0.272 0.93 NOx (current study) (ABD) ANFIS-NSGA-II Waste food oil 0.698 0.90 CO (current study) (ABD) ANFIS-NSGA-II Waste food oil 0.470 0.93 UBHC (current study) (ABD) ANFIS-NSGA-II Waste food oil 0.415 0.94
In conclusion, the study employed a comprehensive approach, utilizing both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. Through quantitative analysis, the researchers analyzed a rich dataset obtained from experimental runs, employing statistical techniques such as regression analysis, ANOVA, and correlation analysis. This enabled the identification of significant effects and interactions among the input parameters, providing valuable quantitative insights into their contributions to engine outputs. The results of the quantitative analysis shed light on the optimization potential of input parameters allowing for informed decision-making in engine design and operation.
Simultaneously, the qualitative analysis considered practical aspects and engineering constraints related to the selected input parameter ranges. By evaluating the feasibility of applying the chosen parameters in real-world engine applications, the researchers ensured that their findings were not only scientifically sound but also practically viable. Limiting the biodiesel blend percentage (B) to 20% due to viscosity and density concerns, capping the nano-particle concentration (NPC) at 20 ppm to avoid valve timing and particle deposition issues, and restricting the engine load (LD) below 20% to account for negligible variations in engine performance exemplify the qualitative assessment's importance. The qualitative analysis provided valuable insights into the practicability of implementing the study's findings in practical diesel engine setups, helping prevent potential engineering challenges and performance limitations.
The contemporary research explored the potential of landfill food waste oils for various input conditions such as blend percentage (BP), load (LD), ignition pressure (IP) and nanoparticle concentration (NPC). Also, two types of nanoparticles namely aluminium oxide and copper oxide were employed so as to predict the best engine characteristics obtained among them. The engine experimental responses such as BTE, BSEC, UBHC, CO and NOx were generated and compared with those obtained by hybrid soft computing techniques. In addition, the optimization techniques provided the optimal combination of engine inputs, which resulted in the best possible conditions for the utilization of landfill waste biodiesel fuel in a diesel engine. Here are some of the most important results of the study:
- The Landfill waste (leachate) oils have been established as a novel feedstock after qualitative research.
- Intelligent computational system is developed by integrating the attributes of ANFIS prediction and GA optimization (GA and NSGA-II) to prepare a comparative analysis between generated outcomes and experimental values for diesel engine characteristics.
- Statistical tools such as RSME and R
2 were employed to estimate the error rate. - ANFIS-NSGA-II hybrid model forecasted outcomes with better efficiency since MSE and RSME values were lower in comparison to conventional ANFIS model.
- Qualitative research has been prepared to develop hybrid models which can predict engine characteristics with minimum experimentation dataset, quickly and efficiently.
- Outcomes generated by ANFIS-NSGA-II were more precise and efficient in comparison to other models.
- Optimum results were achieved after employing multi-objective function optimisation (ANFIS-NSGA-II) for BTE, BSEC, NOx, UBHC and CO which were 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.021%.
All researches are bound to have some constraints or flaws in the methodology research design, technique, materials, etc., and these factors may impact the findings of your study. It is necessary to acknowledge any limitations in the research paper in order to aware the readers of the potential shortcomings which might affect the conclusions drawn from the research. Like other studies, following are the limitations of the present study:
- Biofuel amalgamation was only explored for metallic nanoparticles which somewhat restricts the comparisons.
- Fewer number of operating conditions (four) were considered for conducting experimental investigation which might not be sufficient for a variety of engines such as aeroplane and ship engines.
- Combustion analysis could draw out more comparisons between the two nanofluids.
- Limited number of outcomes were explored.
The boundaries or restrictions of the research explores more domains directing forthcoming studies and guide to future examiners to strategize and accomplish experimental work in diesel engine. Subsequent points explain possible prospects for future investigators to go through:
- A comparative analysis between several nanoparticles (of both nature metallic and non-metallic) needs to be carried out and ranked from best to worst with the aid of multi-criteria decision methods for diesel engine performance and emission.
- A broader experimental dataset might be prepared in future having higher number of input parameters.
- More output responses including other combustion parameters might be measured and included for better comparison in future.
O.K.; M.P.: Conceptualization, Methodology, Analysis, Writing—original draft, Writing—editing. P.K., A.K.Y.: Methodology. M.W.S., S.A.: Software. S.P.: Methodology, Sampling. M.J.I.: Supervision, Writing—reviewing and editing.
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
The authors declare no competing interests.
• BSEC
- Brake specific energy consumption
• B0
- 0% Blending (landfill waste biodiesel) with diesel
• B5
- 5% Blending (landfill waste biodiesel) with diesel
• UBHC
- Unburnt hydrocarbons
• B10
- 10% Blending (landfill waste biodiesel) with diesel
• B15
- 15% Blending (landfill waste biodiesel) with diesel
• CO
- Carbon monoxide
• B20
- 20% Blending (landfill waste biodiesel) with diesel
• LFB
- Landfill waste biodiesel
• RSM
- Response surface methodology
• ANFIS
- Adaptive neuro-fuzzy inference system
• ANN
- Artificial neural network
• GA
- Genetic algorithm
• NOx
- Oxides of nitrogen
• NSGA
- Non-sorting genetic algorithm
• ABD
- Aluminium oxide biodiesel
• CBD
- Copper oxide biodiesel
• BTE
- Brake thermal efficiency
• FFA
- Free fatty acids
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By Osama Khan; Mohd Parvez; Pratibha Kumari; Ashok Kumar Yadav; Wasim Akram; Shadab Ahmad; Samia Parvez and Mohammad Javed Idrisi
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