Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm

Yang, Qingchun; Zhou, Jianlong; Bao, Runjie; Rong, Dongwen; Zhao, Lei; Zhang, Dawei

doi:10.1016/j.ces.2024.120295

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Kim et al 16 applied ML to conduct closed-loop optimization of CO 2 -based oxidative propane dehydrogenation catalysts and obtained the optimal catalyst composition as Cr 7.0 Ni 1.7 Co 0.5 −ZrO x . Yang et al 17 The successful cases demonstrate the potential of ML in expediting the optimization design of CO 2 methanation catalysts. However, the existing literature lacks a comprehensive exploration of ML approaches specifically tailored to CO 2 methanation catalyst development.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Yang,

Bao,

Rong

et al. 2024

Ind. Eng. Chem. Res.

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CO 2 methanation represents a promising technological pathway for achieving efficient carbon dioxide resource utilization and mitigation of greenhouse gas emissions. However, the development of CO 2 methanation catalysts with high activity at low temperatures (<250 °C) remains a formidable challenge. To address the time-consuming and costly nature of traditional catalyst development methods, this study proposes an interpretable machine learning (ML)-assisted reverse design framework for CO 2 methanation catalysts. This framework integrates the advantages of the ML, interpretability analysis, and multiobjective optimization methods to elucidate the intricate interplay among catalyst compositions, preparation conditions, reaction parameters, and catalyst activity. A data set containing 2777 data points is established to construct various ML models. After fine-tuning the key hyperparameters of the four models, a comprehensive comparison is conducted to evaluate their predictive performance. The light gradient boosting machine (LGBM) model demonstrates superior predictive accuracy, attributed to its minimal toot mean squared error of less than 0.31 and the highest R 2 value surpassing 0.90. An interpretable analysis is conducted to ascertain the most significant features and their impact on the outputs of the optimal LGBM model using postvalidation interpretation methods. It indicates that appropriately reducing active component content, first support content, calcination temperature, and inert gas content are favorable for the reaction. Finally, the LGBM model is coupled with the NGSA-III algorithm to maximize the CO 2 conversion ratio and CH 4 selectivity in CO 2 methanation reactions. Three Ru-and three Ni-based new CO 2 methanation catalysts have been successfully predicted and are recommended to have high activity at low temperatures. In particular, the optimized Ru−Ba/Cr 2 O 3 −SrO catalysts have a high CO 2 conversion ratio higher than 97.04% and a CH 4 selectivity higher than 72.22% at low temperatures.

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Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Yang,

Bao,

Rong

et al. 2024

Ind. Eng. Chem. Res.

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Machine learning predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based advanced oxidation processes

Liu,

Long,

Liu

et al. 2024

Journal of Water Process Engineering

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Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm

Cited by 2 publications

References 55 publications

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Machine learning predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based advanced oxidation processes

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Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm

Cited by 2 publications

References 55 publications

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

Machine learning predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based advanced oxidation processes

Contact Info

Product

Resources

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Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures