{"title":"Bridging uncertainty gaps with artificial intelligence-assisted syngas precise prediction in coal gasification","authors":"","doi":"10.1016/j.ces.2024.120734","DOIUrl":null,"url":null,"abstract":"<div><p>Coupling of green hydrogen with the coal chemical industry is pivotal for clean coal utilization and low-carbon transition. This study aims to predict syngas composition efficiently using artificial intelligence-assisted machine learning models, particularly the BP-MLPNN model, addressing raw material diversity and process uncertainties. BP-MLPNN model demonstrates superior reliability and robustness in syngas component prediction, as indicated by significantly lower MSE and RMSE values ranging from 0.002 to 11.61 and 0.05 to 3.41, respectively, along with R<sup>2</sup> values ranging from 0.84 to 1.00. This performance surpasses other models without overfitting. Subsequently, the BP-MLPNN model underwent SHAP analysis to elucidate the internal mechanism of “black box” model. A simple interface input APP was developed to achieve human–machine interaction. This model can mitigate uncertainties in analyzing the integrated coal chemical industry and green hydrogen production system, providing technical guidance and references to quantify its advantages and potential in producing various chemical products.</p></div>","PeriodicalId":271,"journal":{"name":"Chemical Engineering Science","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0009250924010340/pdfft?md5=3ddd57945bda2ed28c1116360ee50cd8&pid=1-s2.0-S0009250924010340-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical Engineering Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009250924010340","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Bridging uncertainty gaps with artificial intelligence-assisted syngas precise prediction in coal gasification
Coupling of green hydrogen with the coal chemical industry is pivotal for clean coal utilization and low-carbon transition. This study aims to predict syngas composition efficiently using artificial intelligence-assisted machine learning models, particularly the BP-MLPNN model, addressing raw material diversity and process uncertainties. BP-MLPNN model demonstrates superior reliability and robustness in syngas component prediction, as indicated by significantly lower MSE and RMSE values ranging from 0.002 to 11.61 and 0.05 to 3.41, respectively, along with R2 values ranging from 0.84 to 1.00. This performance surpasses other models without overfitting. Subsequently, the BP-MLPNN model underwent SHAP analysis to elucidate the internal mechanism of “black box” model. A simple interface input APP was developed to achieve human–machine interaction. This model can mitigate uncertainties in analyzing the integrated coal chemical industry and green hydrogen production system, providing technical guidance and references to quantify its advantages and potential in producing various chemical products.
期刊介绍:
Chemical engineering enables the transformation of natural resources and energy into useful products for society. It draws on and applies natural sciences, mathematics and economics, and has developed fundamental engineering science that underpins the discipline.
Chemical Engineering Science (CES) has been publishing papers on the fundamentals of chemical engineering since 1951. CES is the platform where the most significant advances in the discipline have ever since been published. Chemical Engineering Science has accompanied and sustained chemical engineering through its development into the vibrant and broad scientific discipline it is today.