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Song C, Shi Y, Li M, He Y, Xiong X, Deng H, Xia D. Prediction of g-C 3N 4-based photocatalysts in tetracycline degradation based on machine learning. CHEMOSPHERE 2024; 362:142632. [PMID: 38897319 DOI: 10.1016/j.chemosphere.2024.142632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 06/08/2024] [Accepted: 06/14/2024] [Indexed: 06/21/2024]
Abstract
Investigating the effects of g-C3N4-based photocatalysts on experimental parameters during tetracycline (TC) degradation can be helpful in discovering the optimal parameter combinations to improve the degradation efficiencies in general. Machine learning methods can avoid the problems of high cost, time-consuming and possible instrumental errors in experimental methods, which have been proven to be an effective alternative for evaluating the entire experimental process. Eight typical machine learning models were explored for their effectiveness in predicting the TC degradation efficiencies of g-C3N4 based photocatalysts. XGBoost (XGB) was the most reliable model with R2, RMSE and MAE values of 0.985, 4.167 and 2.900, respectively. In addition, XGB's feature importance and SHAP method were used to rank the importance of features to provide interpretability to the results. This study provided a new idea for developing g-C3N4-based photocatalysts for TC degradation and intelligent algorithms for predicting the photocatalytic activity of g-C3N4-based photocatalysts.
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Affiliation(s)
- Chenyu Song
- Engineering Research Center Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan, 430073, PR China.
| | - Yintao Shi
- Engineering Research Center Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan, 430073, PR China; School of Environmental Engineering, Wuhan Textile University, Wuhan, 430073, PR China
| | - Meng Li
- Engineering Research Center Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan, 430073, PR China; Textile Pollution Controlling Engineering Centre of Ministry of Ecology and Environment, College of Environmental Science and Engineering, Donghua University, Shanghai, 201620, PR China
| | - Yuanyuan He
- Engineering Research Center Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan, 430073, PR China
| | - Xiaorong Xiong
- School of Computing, Huanggang Normal University, Huanggang, 438000, PR China
| | - Huiyuan Deng
- Hubei Provincial Spatial Planning Research Institute, Wuhan, 430064, PR China
| | - Dongsheng Xia
- Engineering Research Center Clean Production of Textile Dyeing and Printing, Ministry of Education, Wuhan, 430073, PR China.
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Mou L, Han T, Smith PES, Sharman E, Jiang J. Machine Learning Descriptors for Data-Driven Catalysis Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301020. [PMID: 37191279 PMCID: PMC10401178 DOI: 10.1002/advs.202301020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/07/2023] [Indexed: 05/17/2023]
Abstract
Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.
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Affiliation(s)
- Li‐Hui Mou
- Hefei National Research Center for Physical Sciences at the MicroscaleSchool of Chemistry and Materials ScienceUniversity of Science and Technology of ChinaHefeiAnhui230026China
| | - TianTian Han
- Hefei JiShu Quantum Technology Co. Ltd.Hefei230026China
| | | | - Edward Sharman
- Department of NeurologyUniversity of CaliforniaIrvineCA92697USA
| | - Jun Jiang
- Hefei National Research Center for Physical Sciences at the MicroscaleSchool of Chemistry and Materials ScienceUniversity of Science and Technology of ChinaHefeiAnhui230026China
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Tapan NA, Günay ME, Yıldırım N. Application of Machine Learning for the Determination of Damaged Starch Ratio as an Alternative to Medcalf and Gilles Principle. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02442-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Guggari S, Kadappa V, Umadevi V, Abraham A. Music rhythm tree based partitioning approach to decision tree classifier. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.03.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ashok A, Kumar A, Saad MAS, Al-Marri MJ. Electrocatalytic conversion of CO2 over in-situ grown Cu microstructures on Cu and Zn foils. J CO2 UTIL 2021. [DOI: 10.1016/j.jcou.2021.101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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An Analysis of Research on Membrane-Coated Electrodes in the 2001–2019 Period: Potential Application to CO2 Capture and Utilization. Catalysts 2020. [DOI: 10.3390/catal10111226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The chemistry and electrochemistry basic fields have been active for the last two decades of the past century studying how the modification of the electrodes’ surface by coating with conductive thin films enhances their electrocatalytic activity and sensitivity. In light of the development of alternative sustainable ways of energy storage and carbon dioxide conversion by electrochemical reduction, these research studies are starting to jump into the 21st century to more applied fields such as chemical engineering, energy and environmental science, and engineering. The huge amount of literature on experimental works dealing with the development of CO2 electroreduction processes addresses electrocatalyst development and reactor configurations. Membranes can help with understanding and controlling the mass transport limitations of current electrodes as well as leading to novel reactor designs. The present work makes use of a bibliometric analysis directed to the papers published in the 21st century on membrane-coated electrodes and electrocatalysts to enhance the electrochemical reactor performance and their potential in the urgent issue of carbon dioxide capture and utilization.
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Erdem Günay M, Yıldırım R. Recent advances in knowledge discovery for heterogeneous catalysis using machine learning. CATALYSIS REVIEWS-SCIENCE AND ENGINEERING 2020. [DOI: 10.1080/01614940.2020.1770402] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- M. Erdem Günay
- Department of Energy Systems Engineering, Istanbul Bilgi University, Istanbul, Turkey
| | - Ramazan Yıldırım
- Department of Chemical Engineering, Boğaziçi University, Istanbul, Turkey
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A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process. METALS 2020. [DOI: 10.3390/met10050685] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.
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Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I, Shimizu KI. Machine Learning for Catalysis Informatics: Recent Applications and Prospects. ACS Catal 2019. [DOI: 10.1021/acscatal.9b04186] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Satoru Takakusagi
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Kamachi
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
- Department of Life, Environment and Materials Science, Fukuoka Institute of Technology, 3-30-1Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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Yıldız MG, Davran-Candan T, Günay ME, Yıldırım R. CO2 capture over amine-functionalized MCM-41 and SBA-15: Exploratory analysis and decision tree classification of past data. J CO2 UTIL 2019. [DOI: 10.1016/j.jcou.2019.02.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes.
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