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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 scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These 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

Structural survey of metal-covalent organic frameworks and covalent metal-organic frameworks

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International Journal of Minerals,Metallurgy and Materials 2023年第12期30卷 2297-2309页

作者： Chaozhi Xiong Zhenwu Shao Jia’nan Hong kexin bi Qingsong Huang Chong Liu School of Chemical Engineering Sichuan UniversityChengdu 610065China Beijing Changping Hospital of Integrated Chinese and Western Medicine Beijing 102208China Department of bioprocess Engineering Institute of BiotechnologyTechnische Universitat BerlinBerlin 10623Germany

This review offers an overview of the latest developments in metal-covalent organic framework(MCOF)and covalent metal-organic framework(CMOF)materials,whose construction entails a combination of reversible coordination and covalent bonding adapted from metal-organic frameworks(MOFs)and covalent organic frameworks(COFs),respectively.With an emphasis on the MCOF and CMOF structures,this review surveys their building blocks and topologies.Specifically,the frameworks are classified based on the dimensions of their components(building blocks),namely,discrete building blocks and one-dimensional infinite building blocks.For the first category,the materials are further divided into collections of two-and three-dimensional networks based on their topologies.For the second category,the recently emerging MCOFs with woven structures are covered.Finally,the state-of-the-art in MCOF and CMOF chemistry has been laid out for promising avenues in future developments.

关键词： metal–covalent organic frameworks covalent metal-organic frameworks topology building block

Extraction and analysis of risk factors from Chinese chemical accident reports

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Chinese Journal of Chemical Engineering 2023年第9期61卷 68-81页

作者： Xi Luo Xiayuan Feng Xu Ji Yagu Dang Li Zhou kexin bi Yiyang Dai School of Chemical Engineering Sichuan UniversityChengdu 610065China

Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.

关键词： Chemical processes Chemical process safety Natural language process Knowledge graph Neural networks Algorithm

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

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Chinese Journal of Chemical Engineering 2019年第8期27卷 1888-1894页

作者： kexin bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial big Data System and Application Beijing 100084China

Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

关键词： Intelligent properties prediction Support vector machine Hybrid GA-PSO TBP distillation curve fitting

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

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Frontiers of Environmental Science & Engineering 2023年第12期17卷 71-84页

作者： Min Cheng Zhiyuan Zhang Shihui Wang kexin bi Kong-qiu Hu Zhongde Dai Yiyang Dai Chong Liu Li Zhou Xu Ji Wei-qun Shi School of Chemical Engineering Sichuan UniversityChengdu 610065China Laboratory of Nuclear Energy Chemistry Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China College of Architecture and Environment Sichuan UniversityChengdu 610065China School of Carbon Neutrality Future Technology Sichuan UniversityChengdu 610065China Department of bioprocess Engineering Institute of BiotechnologyTechnische Universität BerlinBerlin 10623Germany

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture,as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing.Starting from the computation-ready experimental(CoRE)metal-organic frameworks(MOFs)database,grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs.A ranking list of MOFs based on their iodine uptake capabilities was generated,with the Top 10 candidates identified and their respective adsorption sites visualized.Subsequently,machine learning was used to establish structure-property relationships to correlate MOFs’various structural and chemical features with their corresponding performances in iodine capture,yielding interpretable common features and design rules for viable MOF adsorbents.The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine,and moreover,other volatile environmentally hazardous species.

关键词： Iodine capture Metal-organic framework Large-scale screening Molecular simulation Machine learning

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基于多尺度上下文的英文作文自动评分研究

计算机工程 2024年第3期50卷 259-266页

作者：于明诚党亚固吴奇林吉旭毕可鑫四川大学化学工程学院四川成都610041

目前作文自动评分模型缺乏对不同尺度上下文语义特征的提取,未能从句子级别计算与作文主题关联程度的特征。提出基于多尺度上下文的英文作文自动评分研究方法MSC。采用XLNet英文预训练模型提取原始作文文本单词嵌入和句嵌入,避免在处理... 详细信息

目前作文自动评分模型缺乏对不同尺度上下文语义特征的提取,未能从句子级别计算与作文主题关联程度的特征。提出基于多尺度上下文的英文作文自动评分研究方法MSC。采用XLNet英文预训练模型提取原始作文文本单词嵌入和句嵌入,避免在处理长序列文本时无法准确捕捉到符合上下文语境的向量嵌入,提升动态向量语义表征质量,解决一词多义问题,并通过一维卷积模块提取不同尺度的短语级别嵌入。多尺度上下文网络通过结合内置自注意力简单循环单元和全局注意力机制,分别捕捉单词、短语和句子级别的作文高维潜在上下文语义关联关系,利用句向量与作文主题计算语义相似度提取篇章主题层次特征,将所有特征输入融合层通过线性层得到自动评分结果。在公开的标准英文作文评分数据集ASAP上的实验结果表明,MSC模型平均二次加权的Kappa值达到了80.5%,且在多个子集上取得了最佳效果,优于实验对比的深度学习自动评分模型,证明了MSC在英文作文自动评分任务上的有效性。

关键词：英文作文自动评分预训练模型多尺度上下文全局注意力主题层次特征

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 intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This 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 practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.

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

考虑植被根系深度动态变化的VIC径流模拟模型

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水资源保护 2024年第3期40卷 28-34,51页

作者：张珂戴钰刘林鑫牛杰帆毕可心李志琳河海大学水文水资源学院江苏南京210098 河海大学水灾害防御全国重点实验室江苏南京210098 河海大学长江保护与绿色发展研究院江苏南京210024 中国气象局水文气象重点开放实验室江苏南京210024 水利部水利大数据重点实验室江苏南京210098

为了提高干旱半干旱地区植被变化影响下的水文模型模拟精度,结合植物根系的垂直分布与气候、土壤条件,提出了基于流域气象和土壤性质等下垫面特征的流域根系平均深度估算方法,以黄土高原地区无定河流域为例,构建了考虑植被根系深度动态... 详细信息

为了提高干旱半干旱地区植被变化影响下的水文模型模拟精度,结合植物根系的垂直分布与气候、土壤条件,提出了基于流域气象和土壤性质等下垫面特征的流域根系平均深度估算方法,以黄土高原地区无定河流域为例,构建了考虑植被根系深度动态变化的可变下渗容量(VIC)径流模拟模型(VIC-DR模型)。将该模型径流模拟结果与有汇流演算的VIC模型(VIC-R模型)结果进行比较。结果表明:与VIC-R模型相比,VIC-DR模型模拟精度显著提高,VIC-DR模型对无定河流域月尺度、日尺度径流量模拟结果的纳什效率系数分别提高了10.6%和8.7%,相对误差分别下降了15.4%和5.1%,与实测值相关系数均大于0.6;VIC-DR模型的模拟结果符合流域实际情况,模型适用性良好。

关键词： VIC模型动态植被根系参数径流模拟无定河流域

用于蒸汽裂解产物成分预测的集成迁移学习框架

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化工进展 2024年第5期43卷 2880-2889页

作者：郑可欣江雨欣毕可鑫赵祺铭陈少臣王冰冰任俊宇吉旭邱彤戴一阳四川大学化学工程学院四川成都610065 帝国理工化学工程学院英国伦敦SW72AZ 清华大学化学工程系北京100084

回顾了蒸汽裂解过程建模的方法,阐述了工业实际情况中面临的数据匮乏问题。面对石油化工行业大量的小数据集建模需求,充分利用历史生产数据,提出了一种集成迁移学习框架。首先,利用充足的数据在特定工况下建立了基本的深度学习模型。然... 详细信息

回顾了蒸汽裂解过程建模的方法,阐述了工业实际情况中面临的数据匮乏问题。面对石油化工行业大量的小数据集建模需求,充分利用历史生产数据,提出了一种集成迁移学习框架。首先,利用充足的数据在特定工况下建立了基本的深度学习模型。然后,利用小数据集将迁移学习技术应用于新的工况,源域的专家知识通过基于参数的方法转移到目标领域。最后,引入集成学习来整合获得的迁移学习模型,从而提高性能。在几个实际案例上进行实践,研究了该模型框架的性能。为了更好地理解模型,还进一步实施了层可迁移性分析和SHapley Additive exPlanation(SHAP)特征重要性分析。结果说明该方法训练出的模型具有良好的准确性、稳定性、计算效率和可解释性,可以满足工业需求。

关键词：模型迁移学习集成学习算法模型可解释性石油预测神经网络