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Machine learning-assisted retrosynthesis planning:Current status and future prospects

Chinese Journal of Chemical Engineering 2025年第1期77卷 273-292页

作者： Yixin Wei Leyu Shan Tong Qiu Diannan Lu Zheng Liu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Beijing 100084China

Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the *** this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis *** first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway ***,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest *** that,we specifically discuss large language models in retrosynthesis ***,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.

关键词： Retrosynthesis planning Machine learning Artificial intelligence Synthetic pathway Chemoinformatics

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A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

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Chinese Journal of Chemical Engineering 2023年第3期55卷 266-276页

作者： Yiming Bai Shuaiyu Xiang Feifan Cheng Jinsong Zhao Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China Sinopec Engineering Incorporation Beijing 100084China

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

关键词： Fault prognosis Process systems Safety Prediction Principal component analysis Long short term memory

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Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

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Chinese Journal of Chemical Engineering 2022年第10期50卷 398-411页

作者： Ruoshi Qin Jinsong Zhao State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China

Intelligent fault recognition techniques are essential to ensure the long-term reliability of *** to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating *** much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode *** overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern *** triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode *** benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed *** with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

关键词： Neural networks Multiscale Adaptive attentionmodule Triplet lossoptimization Fault diagnosis Chemical processes

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Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

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Chinese Journal of Chemical Engineering 2022年第4期35卷 29-40页

作者： Kexin Bi Mingyu Yan Shuyuan Zhang Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Beijing 100084China Fushun Petrochemical Company PetroChinaFushun 113008China

In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic *** optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and *** the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical *** sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are *** results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

关键词： Three-scale integrated optimization Cyclic scheduling Supply chain Mixed-integer linear programming Thermal cracking

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Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

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Chinese Journal of Chemical Engineering 2021年第10期34卷 1-17页

作者： Kexin Bi Shuyuan Zhang Chen Zhang Haoran Li Xinye Huang Haoyu Liu Tong Qiu Beijing Key Laboratory of Industrial Big Data System and Application Department of Chemical EngineeringTsinghua UniversityBeijing 100084China

applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and *** ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent *** paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial *** entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are *** control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise *** software packages are ***,an outlook in terms of future directions is presented.

关键词： Ethylene thermal cracking PSE Intelligent manufacturing Molecularization and digitization Modeling and optimization

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Facile synthesis of W-Mo bimetallic oxides with high adsorption properties from secondary resources

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Tungsten 2024年第4期6卷 806-820页

作者： Jia-Li Cheng Yang-Si Liu Xiao-Li Xi Zuo-Ren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and EngineeringBeijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing100124China

A simple solvothermal method was used to obtain W-Mo bimetallic oxides from W-Mo alloy scrap,and pure metal powders were also used as the raw materials to simulate *** products had a sea urchin-like structure with abundant oxygen vacancies and the products prepared at low temperatures forms a sosoloid resembling orthorhombic W_(0.4)Mo_(0.6)O_(3).The WMo bimetallic oxide prepared at the reaction temperature of 120℃exhibited excellent selective adsorption performance for methylene blue(MB),which the adsorption rate of MB reached 99%in 12 min and the adsorption rate reached 90%after6 adsorption *** the W-Mo molar ratio is 1:3,the maximum adsorption capacity of sample for MB can reach1148 mg·g^(-1).The adsorption process followed the Langmuir and pseudo-second-order models,which is surface-controlled monolayer *** experimental results show the feasibility of preparing W-Mo bimetal oxide products from pure materials and *** process is simple and effective,which offered a potential approach for secondary resource recycling and reusing.

关键词： W-Mo bimetallic oxide Oxygen vacancy Selective adsorption W-Mo alloy scrap Solvothermal

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Comprehensive recovery of W,V,and Ti from spent selective reduction catalysts

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Rare Metals 2023年第10期42卷 3518-3531页

作者： Li-Wen Ma Xiao-Li Xi Jia-Peng Chen Fan Guo Zi-Jie Yang Zuo-Ren Nie Key Laboratory of Advanced Functional Materials Ministry of EducationFaculty of Materials and ManufacturingBeijing University of TechnologyBeijing100124China Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China

In this study,spent WO_(3)/V_(2)O_(5)-TiO_(2) catalysts used for selective catalytic reduction were treated by a hydrometallurgical process to comprehensively recover valuable metallic elements,such as W,V,and *** and Si impurities were preferentially removed by selective micro wave-assisted alkali leaching.W and V were leached by enhanced high-pressure leaching with efficiencies estimated at 95% and 81%.The leaching of W and V followed the nuclear shrinkage model controlled by the combination of product layer diffusion and interfacial chemical reaction.A synergistic extraction was applied to separate W and V using an extractant mixture of di-(2-ethylhexyl)phosphoric acid P204 and the primary amine *** extraction efficiencies of V and W reached 86.5% and 6.3%,respectively,with a separation coefficient(V/W) of *** product was precipitated after extraction to yield ammonium paratung state(APT) and NH_(4)VO_(3).The TiO_(2)catalyst carrier residue meets commercial specifications for *** comprehensive recovery process with the characteristics of high-pressure leaching and synergistic extraction realizes the resourceful utilization of the spent catalysts.

关键词： Spent selective catalytic reduction(SCR)catalysts Microwaveleaching Pressure leaching Synergistic extraction Comprehensive recovery

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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Process Drift Detection in Event Logs with Graph Convolutional Networks 28th

Process Drift Detection in Event Logs with Graph Convoluti...

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28th International Conference on database systems for Advanced applications, DASFAA 2023

作者： Lin, Leilei Jin, Yumeng Wen, Lijie Chen, Wenlong Di, Ying Xu, Yusong Wang, Jianmin School of Management Capital Normal University Beijing China Information Engineering College Capital Normal University Beijing China School of Software Tsinghua University Beijing China Beijing Key Laboratory of Industrial Big Data System and Application Beijing China

ISBN: (纸本)9783031306778

The only constant is change. Drift detection in process mining is a family of methods to detect changes by analyzing event logs to ensure the accuracy and reliability of business processes in process-aware information systems (e.g., ERP systems). However, artificial feature selection is still a mountain to climb in existing methods, which requires high domain knowledge for users. In this paper, we propose a novel approach by using Graph convolutional networks for Drifts Detection in the event log, we name it GDD. Specifically, 1) we transform event sequences into two directed graphs by using two consecutive time windows, and construct the line graphs for the directed graphs to capture the orders between different activities;2) we use graph convolutional networks to capture the features in these graphs, and augment the original graphs with virtual nodes to represent the latent aspects of the graphs;3) we calculate the distances between virtual nodes, and use the K-means algorithm to find the outliers that are considered as candidate change points. Then, a filter mechanism is used to confirm the actual change points. The experiments on simulated event logs and real-life event logs confirmed the improvements of GDD compared with the baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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Improvement in oxidation resistance of Cantor alloy through microstructure tailoring

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China Foundry 2022年第6期19卷 503-510页

作者： Bao-lin Pang Man Wang Yue-gang Shen Xiao-li Xi Zuo-ren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology Faculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

A method of improving the oxidation resistance of Cantor alloy through microstructure tailoring was *** with distinctive microstructures were achieved by different annealing treatments on the cold-rolled Cantor *** test was then carried out on the various annealed samples at 800°C for 24 h in *** oxidation behavior was evaluated in terms of oxidation rate,surface and cross-section microstructure *** stratified oxide layers including outer Mn_(2)O_(3) and inner Cr_(2)O_(3) were observed in all the annealed samples,the thickness of Mn_(2)O_(3) and Cr_(2)O_(3) layers was ***,the samples exhibited different oxidation *** results indicate that the oxidation resistance of various annealed samples is closely related with grain size as well as twin density.

关键词： Cantor alloy annealing temperature grain size twins oxidation behavior

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