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Data-driven intelligent modeling framework for the steam cracking process

Chinese Journal of Chemical Engineering 2023年第9期61卷 237-247页

作者： Qiming Zhao Kexin Bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data Systems and Applications Tsinghua UniversityBeijing 100084China School of Chemical Engineering Sichuan UniversitySichuan 610065China Department of Bioprocess Engineering Institute of BiotechnologyTechnische Universität BerlinBerlin 10623Germany

steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.

关键词： Mathematical modeling Data-driven modeling Process systems steam cracking Clustering Multivariate adaptive regression spline

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Artificial Intelligence in steam cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction

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Engineering 2019年第6期5卷 1027-1040页

作者： Pieter PPlehiers Steffen HSymoens Ismaël Amghizar Guy B.Marin Christian V.Stevens Kevin M.Van Geem Laboratory for Chemical Technology Department of MaterialsTextiles and Chemical EngineeringGhent UniversityGent 9052Belgium SynBioC Research Group Department of Green Chemistry and TechnologyFaculty of Bioscience EngineeringGhent UniversityGent 9000Belgium

Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these elds, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning arti cial neural networks (DL ANNs) has been developed for the largest chemicals production process steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker ef uent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed char- acterization of a naphtha is predicted from three points on the boiling curve and paraf ns, iso-paraf ns, ole ns, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the ef uent predic- tion is 0.1 wt%. When combining all networks using the output of the previous as input to the next the ef uent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major bene t is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented frame

关键词： Artificial intelligence Deep learning steam cracking Artificial neural networks

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Targeted Catalytic cracking to Olefins(TCO):Reaction Mechanism,Production Scheme,and Process Perspectives

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Engineering 2023年第11期30卷 100-109页

作者： Youhao Xu Yanfen Zuo Wenjie Yang Xingtian Shu Wei Chen Anmin Zheng Research Institute of Petroleum Processing SinopecBeijing 100083China Economics and Development Research Institute SinopecBeijing 100029China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics National Center for Magnetic Resonance in WuhanKey Laboratory of Magnetic Resonance in Biological SystemsWuhan Institute of Physics and MathematicsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430071China

Light olefins are important organic building blocks in the chemicals industry.The main low-carbon olefin production methods,such as catalytic cracking and steam cracking,have considerable room for improvement in their utilization of hydrocarbons.This review provides a thorough overview of recent studies on catalytic cracking,steam cracking,and the conversion of crude oil processes.To maximize the production of light olefins and reduce carbon emissions,the perceived benefits of various technologies are examined.Taking olefin generation and conversion as a link to expand upstream and downstream processes,a targeted catalytic cracking to olefins(TCO)process is proposed to meet current demands for the transformation of oil refining into chemical production.The main innovations of this process include a multiple feedstock supply,the development of medium-sized catalysts,and a diameter-transformed fluidizedbed reactor with different feeding schemes.In combination with other chemical processes,TCO is expected to play a critical role in enabling petroleum refining and chemical processes to achieve low carbon dioxide emissions.

关键词： Light olefins steam cracking Catalytic cracking TCO process Oil processing revolution

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