Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Shen, Yadi; Dong, Yingchao; Han, Xiaoxia; Wu, Jinde; Xue, Kaiping; Jin, Meizhu; Xie, Gang

doi:10.1016/j.ijhydene.2022.10.031

Cited by 10 publications

(2 citation statements)

References 47 publications

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“…The state space transition equation, a core concept in dynamic system theory, describes the evolution of system states over time, as shown in Figure 9. The design of the state transition equation in this research is based on the following mathematical representation [77][78][79]:…”

Section: State Space Transition Equationmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

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In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.

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Section: State Space Transition Equationmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

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“…Pérez et al [9] introduced a novel microstructured reactor design, offering improved performance in exothermic reactions like the Sabatier process. Shen et al [10] focused on methanation optimization, developing a supervised learning based on an extreme learning machine algorithm, demonstrating accurate CO2 conversion rate predictions.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Co2 Methanation Reaction for Carbon Capture

Okur

2023

Preprint

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Among various processes, catalytic CO2 methanation has emerged as a promising method for carbon capture and utilization. Therefore, a CO2 methanation reaction studied with experimentally. The extent of coke formation on the catalyst after the reaction was quantitatively studied. Then, four different machine learning model were employed in order to model CO2 methanation process and to predict CO2, CO, CH4, H2 and N2 concentrations at outlet. The results of the machine learning models were consistent with the experimental data. Performances of the proposed regression methods are evaluated with 10-fold cross validation. Random forest and decision tree regression performed the best among other methods by achieving R >0.98 for the output concentrations and were able to outperform other modeling approaches. It was shown that the best result among the literature studies is reached in this study. The results have demonstrated the possibility of simulation of the methanation process via machine learning techniques. These techniques can be used control and optimization of carbon capture.

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Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things

Zhang,

Wang,

et al. 2024

Environmental Research

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Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Cited by 10 publications

References 47 publications

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Predictive Modeling of Co2 Methanation Reaction for Carbon Capture

Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things

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