An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

Sharma, Prabhakar; Sahoo, Bibhuti Bhusan

doi:10.1016/j.ijhydene.2022.04.093

Cited by 43 publications

(20 citation statements)

References 49 publications

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“…The RMSE and MSE should be close to zero. The following expressions were employed to estimate these indices , In these expressions, the number of terms is “ n ”, while “ i ” represents the term being considered, “ x a ” denotes actual values, also the forecasted values depicted with “ x p ”, and “ x m ” is the mean of observed values.…”

Section: Methodsmentioning

confidence: 99%

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Le,

Pandey,

Sirohi

et al. 2023

Energy Fuels

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Biochar is found to possess a large number of applications in energy and environmental areas. However, biochar could be produced from a variety of sources, showing that biochar yield and proximate analysis outcomes could change over a wide range. Thus, developing a high-accuracy machine learning-based tool is very necessary to predict biochar characteristics. In this study, a hybrid technique was developed by blending modern machine learning (ML) algorithms with cooperative game theory-based Shapley Additive exPlanations (SHAP). SHAP analysis was employed to help improve interpretability while offering insights into the decision-making process. In the ML models, linear regression was employed as the baseline regression method, and more advanced methodologies like AdaBoost and boosted regression tree (BRT) were employed. The developed prediction models were evaluated on a battery of statistical metrics, and all ML models were observed as robust enough. Among all three models, the BRT-based model delivered the best prediction performance with R 2 in the range of 0.982 to 0.999 during the model training phase and 0.968 to 0.988 during the model test. The value of the mean squared error was also quite low (0.89 to 9.168) for BRT-based models. SHAP analysis quantified the value of each input element to the expected results and provided a more in-depth understanding of the underlying dynamics. The SHAP analysis helped to reveal that temperature was the main factor affecting the response predictions. The hybrid technique proposed here provides substantial insights into the biochar manufacturing process, allowing for improved control of biochar properties and increasing the use of this sustainable and flexible material in numerous applications.

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Section: Methodsmentioning

confidence: 99%

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Le,

Pandey,

Sirohi

et al. 2023

Energy Fuels

Self Cite

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“…During training, ANFIS takes advantage of the existing control and response variables data pairings. Following that, fuzzy IF–THEN rules are built to demonstrate that these portions are linked. , …”

Section: Most Relevant Machine Learning Techniquesmentioning

confidence: 99%

“…Following that, fuzzy IF−THEN rules are built to demonstrate that these portions are linked. 52,90 The fuzzy inference systems (FIS) are also referred to as fuzzy models, fuzzy rule systems, fuzzy controllers, or fuzzy associative memory in literature. A FIS is made up of five functional parts: a rule base that comprises multiple fuzzy if− then rules and also a database that specifies the membership functions of a processing unit that conducts the inference, combined with a unit that performs the inference.…”

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

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208

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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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“…Furthermore, different algorithms and methods have been so far developed for the calculation of ET ref , including artificial neural network (ANN) [ [24] , [25] , [26] , [27] , [28] ], SVM [ [29] , [30] , [31] ], ANFIS [ [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] ], multiple layer perceptron (MLP; [ [40] , [41] , [42] ]), generalized regression neural networks (GNN; [ 43 ]), extreme learning machine (ELM; [ 34 , 35 , [44] , [45] , [46] , [47] , [48] ]). Among the methods listed, numerous ET ref models have been evaluated by researchers using ELM methods.…”

Section: Introductionmentioning

confidence: 99%

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Sharafi¹,

Ghaleni²,

Scholz³

2023

Heliyon

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An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

Cited by 43 publications

References 49 publications

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

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

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

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