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作 者:赵学良 贾梦达 王显鹏[3] 苏丽杰[4] 刘东庆 ZHAO Xueliang;JIA Mengda;WANG Xianpeng;SU Lijie;LIU Dongqing(China Petrochemical Corporation,Beijing 100728,China;Petro-Cyber Works Information Technology Co.,Ltd.,Beijing 100007,China;National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,Liaoning,China;Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University),Ministry of Education,Shenyang 110819,Liaoning,China)
机构地区:[1]中国石油化工集团有限公司,北京100728 [2]石化盈科信息技术有限责任公司,北京100007 [3]东北大学工业智能与系统优化国家级前沿科学中心,辽宁沈阳110819 [4]东北大学智能工业数据解析与优化教育部重点实验室,辽宁沈阳110819
出 处:《化工进展》2024年第2期894-902,F0004,共10页Chemical Industry and Engineering Progress
基 金:国家自然科学基金重大项目(72192830,72192831);国家自然科学基金面上项目(72072029);111项目(B16009)。
摘 要:智能工厂是石化工业发展智能制造、实现绿色可持续转型的重要途径。本文从业务和技术两个方面出发,重点探讨了未来石化智能工厂建设中边缘云平台规划和需要构建的关键业务场景,并结合数据解析与人工智能提出了各场景的关键技术方法,主要包括基于数据与机理融合建模的石化生产计划与调度优化、石化生产过程多目标进化学习建模与操作优化和石化生产设备智能运行监测与故障诊断。本文的研究成果对于推动石化智能工厂建设,实现石化生产管控智能化、质量管控智能化、设备管控智能化的核心能力提升具有重要意义。Smart plant is an important way to develop smart manufacturing and achieve green and sustainable transformation in petrochemical industry.This paper proposed the planning of edge cloud platform and the key business scenarios to be built in the future construction of petrochemical smart plant from both business and technical aspects,and designed the technical methods for each scenario by combining data analysis and artificial intelligence,mainly including production planning and scheduling based on data and mechanism integration modeling,multi-objective evolutionary learning for modeling production process and operation optimization,smart operation monitoring and fault diagnosis for production equipment.The research results of this paper were of great significance to promote the construction of petrochemical smart plant and to realize the improvement of core capabilities such as intelligent petrochemical production control,intelligent quality control and intelligent equipment control.
分 类 号:TE6[石油与天然气工程—油气加工工程] TQ08[化学工程]
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