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 8 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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Recent Advances in the Large‐Scale Production of Photo/Electrocatalysts for Energy Conversion and beyond

Li,

Sun

et al. 2024

Advanced Energy Materials

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Photocatalysis and electrocatalysis have emerged as promising technologies for addressing the energy crisis and environmental issues. However, the widespread application of these technologies is hampered by the challenge of scaling up the production of photo/electrocatalysts that are not only highly active and stable but also cost‐effective and environmentally benign. This review delves into the latest advancements in the large‐scale synthesis of photo/electrocatalysts. The factors to be considered in the large‐scale production of catalysts are discussed first. The synthesis methods for batch preparation of photo/electrocatalysts are then comprehensively introduced, with a thorough discussion of their respective advantages and limitations. Moreover, the data analysis via machine learning techniques, which not only accelerates the identification and refinement of potential new catalysts but also offers insights for enhancing the high‐throughput synthesis of catalysts, is introduced in detail. Then the representative examples are presented to illustrate the applications of large‐scale catalysts in the field of industrial‐level photo/electrocatalysis. Finally, the challenges and prospects in the development of large‐scale production of photo/electrocatalysts are discussed. By bridging the gap between laboratory research and industrial application, this review aims to provide a reference for the future of large‐scale preparation of photo/electrocatalysts in sustainable energy conversion and beyond.

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An automatic recognition of target product and multiparameter collaborative regulation-based machine learning framework for dimethyl oxalate hydrogenation catalysts optimization and prediction

Yang,

Zhou,

Bao

et al. 2025

Chemical Engineering Science

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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 8 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

Recent Advances in the Large‐Scale Production of Photo/Electrocatalysts for Energy Conversion and beyond

An automatic recognition of target product and multiparameter collaborative regulation-based machine learning framework for dimethyl oxalate hydrogenation catalysts optimization and prediction

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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 8 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

Recent Advances in the Large‐Scale Production of Photo/Electrocatalysts for Energy Conversion and beyond

An automatic recognition of target product and multiparameter collaborative regulation-based machine learning framework for dimethyl oxalate hydrogenation catalysts optimization and prediction

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Resources

About

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