Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models
Since fossil fuels are slowly depleting, bio and renewable energies are now given more attention. The main purpose of this research is to investigate and optimize the influencing parameters of bioenergy production through transesterification process. The application of artificial intelligence (AI) i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722022697 |
| _version_ | 1797901879713202176 |
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| author | Hulin Jin Yong-Guk Kim Zhiran Jin Anastasia Andreevna Rushchitc Ahmed Salah Al-Shati |
| author_facet | Hulin Jin Yong-Guk Kim Zhiran Jin Anastasia Andreevna Rushchitc Ahmed Salah Al-Shati |
| author_sort | Hulin Jin |
| collection | DOAJ |
| description | Since fossil fuels are slowly depleting, bio and renewable energies are now given more attention. The main purpose of this research is to investigate and optimize the influencing parameters of bioenergy production through transesterification process. The application of artificial intelligence (AI) in bioenergy production studies has become increasingly popular due to its capability of interpreting nonlinear relationships between inputs and outputs for complex systems. Here, after conducting library studies and carefully reviewing the existing methods, the multi-layer perceptron (MLP), K-nearest neighbors (KNN), Artificial neural network (ANN), and Gaussian processes regression (GPR) models were selected for simulation and prediction of the efficiency of fatty acid methyl ester (FAME) production. The main effective transesterification parameters on production of biodiesel including the temperature of reaction (°C), catalyst mass to oil mass ratio (wt.%), and the molar ratio of methanol to oil were set as the input variables in all studied models. For reaction between oil and short chain alcohols, wollastonite (a calcium metasilicate, CaSiO3) was utilized as a phase boundary catalyst. By carefully selecting the execution conditions of the algorithms in the model selection phase, all three models reached a result above 0.99 and close to 1 with the square R criterion. Also, the RMSE values for the studied models were 3.95 for MLP, 1.09 for KNN, 0.13 for ANN and 3.60 for GPR models. Therefore, it can be concluded that although the ANN model was to be a better model in process efficiency prediction in terms of error, but all three algorithms had high accuracy because of different generality types. The optimum yield of 97.8% for FAME production was observed at optimum methanol to oil molar ratio, reaction temperature, and catalyst mass to oil mass ratio 65°C, 15, and 9.21 wt%, respectively. |
| first_indexed | 2024-04-10T09:08:56Z |
| format | Article |
| id | doaj.art-8fea123970fa4d738c546d602fccb520 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:08:56Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-8fea123970fa4d738c546d602fccb5202023-02-21T05:14:14ZengElsevierEnergy Reports2352-48472022-11-0181397913996Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network modelsHulin Jin0Yong-Guk Kim1Zhiran Jin2Anastasia Andreevna Rushchitc3Ahmed Salah Al-Shati4School of Computer Science and Technology, Anhui University, Hefei 230031, China; Corresponding authors.Department of Computer Engineering, Sejong University, 3001, Seoul, South KoreaJianping Middle School, 201202, Shanghai, ChinaDepartment of Catering Technology and Organization, South Ural State University, Chelyabinsk, Russian FederationDepartment of Chemical Engineering and Petroleum Industries, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq; Corresponding authors.Since fossil fuels are slowly depleting, bio and renewable energies are now given more attention. The main purpose of this research is to investigate and optimize the influencing parameters of bioenergy production through transesterification process. The application of artificial intelligence (AI) in bioenergy production studies has become increasingly popular due to its capability of interpreting nonlinear relationships between inputs and outputs for complex systems. Here, after conducting library studies and carefully reviewing the existing methods, the multi-layer perceptron (MLP), K-nearest neighbors (KNN), Artificial neural network (ANN), and Gaussian processes regression (GPR) models were selected for simulation and prediction of the efficiency of fatty acid methyl ester (FAME) production. The main effective transesterification parameters on production of biodiesel including the temperature of reaction (°C), catalyst mass to oil mass ratio (wt.%), and the molar ratio of methanol to oil were set as the input variables in all studied models. For reaction between oil and short chain alcohols, wollastonite (a calcium metasilicate, CaSiO3) was utilized as a phase boundary catalyst. By carefully selecting the execution conditions of the algorithms in the model selection phase, all three models reached a result above 0.99 and close to 1 with the square R criterion. Also, the RMSE values for the studied models were 3.95 for MLP, 1.09 for KNN, 0.13 for ANN and 3.60 for GPR models. Therefore, it can be concluded that although the ANN model was to be a better model in process efficiency prediction in terms of error, but all three algorithms had high accuracy because of different generality types. The optimum yield of 97.8% for FAME production was observed at optimum methanol to oil molar ratio, reaction temperature, and catalyst mass to oil mass ratio 65°C, 15, and 9.21 wt%, respectively.http://www.sciencedirect.com/science/article/pii/S2352484722022697Transesterification processMachine learning methodOptimization and analysisBioenergy productionTraining and validation dataModeling and simulation |
| spellingShingle | Hulin Jin Yong-Guk Kim Zhiran Jin Anastasia Andreevna Rushchitc Ahmed Salah Al-Shati Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models Energy Reports Transesterification process Machine learning method Optimization and analysis Bioenergy production Training and validation data Modeling and simulation |
| title | Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models |
| title_full | Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models |
| title_fullStr | Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models |
| title_full_unstemmed | Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models |
| title_short | Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models |
| title_sort | optimization and analysis of bioenergy production using machine learning modeling multi layer perceptron gaussian processes regression k nearest neighbors and artificial neural network models |
| topic | Transesterification process Machine learning method Optimization and analysis Bioenergy production Training and validation data Modeling and simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722022697 |
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