Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine |
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| Affiliation: | 1. Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China;2. School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China;3. School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China;1. Department of Chemical Engineering, Biotechnology and Materials, FCFM, University of Chile, Santiago, Chile;2. Department of Chemical Engineering, University of Notre Dame, Notre Dame, USA;3. Department of Physics, University of Concepción, Concepción, Chile;4. School of Chemical Sciences and Engineering, Yachay Tech University, Urcuquí, Ecuador;1. Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE-1410, Brunei Darussalam;2. University of Stuttgart, Institute of Chemical Technology, Faculty of Chemistry, D-70550, Stuttgart, Germany;3. South Ural State University (National Research University), Chelyabinsk, Russian Federation;4. Malaysia-Japan International Instute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia;5. Department of Physics and Materials Science and Engineering, Jaypee Institute of Information Technology, Noida 201309, India;1. College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, Hangzhou 310027, China;2. Institute of Zhejiang University-Quzhou, Quzhou 324000, China;1. Low-Carbon Technology and Chemical Reaction Engineering Laboratory, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China;2. Institute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu, 610207, China;1. State Key Laboratory of Photocatalysis on Energy and Environment, College of Chemistry, Fuzhou University, Fuzhou 350002, China;2. School of Mechanical Engineering and Automation, Fuzhou University, China;3. Fujian Provincial Collaborative Innovation Centre of High-End Equipment Manufacturing, Fuzhou, China;1. Collaborative Innovation Centre of the Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, 210044, Nanjing, PR China;2. College of Light Industry and Food Engineering, Nanjing Forestry University, Nanjing 210037, PR China;3. Jiangsu ShuangLiang Environmental Technology Co., Ltd., Jiangyin, 214400, PR China;4. Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, PR China;5. State Environmental Protection Key Laboratory of Atmospheric Physical Modeling and Pollution Control, China Energy Science and Technology Research Institute Co. Ltd., Nanjing 210023, China;6. School of Material Science and Engineering, University of Jinan, Jinan, 250022, PR China |
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| Abstract: | Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO2 content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions. |
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| Keywords: | Methanation reaction Reaction conditions Machine learning Extreme learning machine State transition simulated annealing algorithm |
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