Precise prediction of performance and emission of a waste derived Biogas–Biodiesel powered Dual–Fuel engine using modern ensemble Boosted regression Tree: A critique to Artificial neural network

Sharma, Prabhakar; Sahoo, Bibhuti Bhusan

doi:10.1016/j.fuel.2022.124131

Cited by 50 publications

(13 citation statements)

References 41 publications

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“…As a part of our research, the engine operation points were determined on the basis of good engineering judgment and as equivalents of the operating points in which ISO 8178 [34] tests are carried out. In many cases found in the literature, the research program is prepared with the use of certain algorithms [28,31,35] however, the authors' experience shows that this type of methodology is not fully effective in the areas in which the authors of this work operate. The research presented in the paper is an investigation of an innovative, high-pressure dual fFuel installation.…”

Section: Methodsmentioning

confidence: 99%

“…Also, a gas used as a fuel corresponding to the properties of natural gas can be produced in many ways and has great potential as a clean alternative fuel for additional reduction of GHG emissions in the process of its combustion or production [29]. Such a gas can be produced under the same conditions as bio-oil from many plant and animal products [30] or waste [31] and individual final products will differ mainly in the concentration of methane and other components of this fuel.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Assessment of the Impact of Replacing Diesel Fuel with CNG on the Concentration of Harmful Substances in Exhaust Gases in a Dual Fuel Diesel Engine

2022

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The problem of global warming and related climate change, as well as rising oil prices, is driving the implementation of ideas that not only reduce the consumption of liquid fuels, but also reduce greenhouse gas emissions. One of them is the use of natural gas as an energy source. It is a hydrocarbon fuel with properties allowing the reduction of CO2 emissions during its combustion. Therefore, solutions are being implemented that allow natural gas to be supplied to means of transport, which are trucks of various categories and purposes. This article presents the results of tests of an engine from a used semi-truck, to which an innovative compressed natural gas (CNG) supply system was installed. This installation (both hardware and software), depending on the engine operating conditions, enables mass replacement by natural gas (up to 90%) of the basic fuel—diesel oil. During the tests, on the basis of the obtained results, the influence of the diesel fuel/CNG exchange ratio under various engine operating conditions on the concentration of toxic CO2, CO, NO, NO2, CH4, C2H6, NMHC, NH3 and exhaust smoke was assessed. The test results confirm that, compared to conventional fueling, the diesel/CNG-fueled engine allows for a significant reduction in CO2 concentration even in a car operated for several years with diesel fuel and with high mileage. The use of a non-factory installation significantly increased the concentration of methane CH4, nitrogen dioxide NO2 and carbon monoxide CO in the exhaust gas. It was found that the smoke content and the temperature of exhaust gases did not decrease with increasing ratio of fuel replacement. The concentration of CO, NOX, CH4 and NMHC was increased, while the concentration of CO2, C2H6, NH3 and the consumption of diesel fuel by the engine, decreased significantly. The innovation of the research is based on the use of a modern and unique engine gas fuel system control system where the original fuel supply system with unit pumps is able to reduce diesel oil consumption by up to 90%.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of the Impact of Replacing Diesel Fuel with CNG on the Concentration of Harmful Substances in Exhaust Gases in a Dual Fuel Diesel Engine

2022

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“…The counts of hidden neurons in the input and output layers in the MLP-ANN model are governed by the number of variables in the examined application. However, there is no proven approach for estimating the number or sizes of hidden layers. , The number of hidden layers can be modified depending on the scale of the problem, the quality, and the quantity of data. As a result, neurons are regularly adjusted depending on iterations during the training to achieve the optimal amount. , The construction of a predictive MLP-ANN model necessitates the establishment of a training stage.…”

Section: Most Relevant Machine Learning Techniquesmentioning

confidence: 99%

“…However, there is no proven approach for estimating the number or sizes of hidden layers. 82,83 The number of hidden layers can be modified depending on the scale of the problem, the quality, and the quantity of data. As a result, neurons are regularly adjusted depending on iterations during the training to achieve the optimal amount.…”

Section: Most Relevant Machine Learning Techniquesmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

Self Cite

182

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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“…It has been found that the efficiency of managing the comprehensive implementation of China's green buildings is low, but the tendency to develop has shown good performance. Sharma and Sahoo [21] used biodiesel from waste as experimental fuel and biogas from waste as gaseous fuel to power the diesel engine in dual combustion mode. A new approach called augmented tree regression has been used to model the performance and emission of a variable density diesel engine.…”

Section: Literature Reviewmentioning

confidence: 99%

Providing an Expert System to Manage Energy Consumption in Small- and Medium-Sized Industries Using Soft Computing

2022

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The increasing growth of electricity consumption, the need for its continuous supply, and the impossibility of storing this vital energy are among the primary and essential reasons for preventing any interruption in supply and proper planning regarding energy consumption management, especially in industry. The innovation of this research is the design of a decision-making system to help decision-making in energy management in small- and medium-sized industries using the knowledge of experts in small- and medium-sized industries and the electricity industry in the field of energy consumption management and extracting relevant expertise rules to create a knowledge base. Therefore, the main advantage of the research is to provide an expert system for the correct management of energy consumption in small and medium industries using soft computing. Another advantage of the research is the use of uncertain decision-making variables in this field; instead of using words and linguistic restrictions in this research, modeling them in the system using the principles of fuzzy logic is used. The main objectives of this research include determining the effective variables in determining the amount of energy production for small and medium industries, explaining the existing tacit knowledge and turning it into explicit knowledge, and providing an expert system for managing energy consumption in this field. Based on the results of field studies and the research literature, effective variables in determining the amount of electricity generation were identified. By extracting the knowledge of industry experts, the tacit knowledge available as explicit knowledge in the storage knowledge base and the system was designed to manage electrical charge consumption. One of the achievements of this system is its creation and measurement using various data and results obtained from small and medium industries. Finally, the results obtained from the expert system in comparing the output of expert opinions and the system’s performance in managing the amount of energy production showed that the usefulness of the expert system was designed. The system uses the knowledge base to make decisions such as an expert human. According to the main results of the research based on comparing the performance of the system with the Bayesian inference networks, it showed that there is an acceptable correlation between the data and the prediction system. Also, to evaluate the acquisition of data using a statistical test, it has been determined that the total number of blackout hours in different areas of Tehran in the base year was more than the current year.

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Precise prediction of performance and emission of a waste derived Biogas–Biodiesel powered Dual–Fuel engine using modern ensemble Boosted regression Tree: A critique to Artificial neural network

Cited by 50 publications

References 41 publications

Experimental Assessment of the Impact of Replacing Diesel Fuel with CNG on the Concentration of Harmful Substances in Exhaust Gases in a Dual Fuel Diesel Engine

Experimental Assessment of the Impact of Replacing Diesel Fuel with CNG on the Concentration of Harmful Substances in Exhaust Gases in a Dual Fuel Diesel Engine

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Providing an Expert System to Manage Energy Consumption in Small- and Medium-Sized Industries Using Soft Computing

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