复杂工业过程特征建模方法及应用研究

Research on feature modeling method for complex industrial process and its application

针对复杂工业过程的特征建模是研究其优化控制的基础.复杂工业过程普遍具有强干扰、非线性、大时变等诸多不确定性特征,部分工艺涉及复杂的生化反应并伴有强污染和高危性,检测数据具有维度高、噪声大等特性,这均为建立精准的工业模型提出了更急迫的需求和更高的标准.鉴于此,总结并归纳当前复杂工业过程的建模思路和研究进展,旨在从多个视角分析不同建模方法的适用性和有效性,为先进的优化控制理论指导实际工业生产奠定模型基础.首先,从机理建模、数据驱动建模和混合建模3个方向对目前主流的工业建模方法进行划分和综述;然后,阐述各类建模方法的具体设计思路,并分析模型结构和算法特点;接着,调研不同建模策略在解决实际工业过程中的指标建模、被控对象建模、全流程建模等问题的具体应用情况;最后,结合目前工业智能化建设趋势及其面临的挑战性问题,指出未来的研究思路和发展方向.

Feature modeling for complex industrial processes is the basis for studying their optimal control.Complex industrial processes generally have uncertain characteristics such as strong interference,nonlinearity,and large time-varying.Some of the processes involve complex biochemical reactions with strong contamination and high risk,and the detection data is highly dimensional and noisy,which all put forward more urgent needs and higher standards for the establishment of accurate industrial models.This paper summarizes the current modeling ideas and research progress of complex industrial processes,and aims to analyze the applicability and effectiveness of different modeling methods from multiple perspectives,so as to lay the modeling foundation for the advanced optimal control theory to guide the actual industrial production.First,the main industrial modeling methods are divided and summarized from three aspects:mechanism modeling,data-driven modeling and hybrid modeling.Second,the specific design ideas of various modeling methods are described,and the model structure and algorithm characteristics are analyzed.Then,the specific applications of different modeling strategies in solving the problems of index modeling,controlled object modeling,and full-scale modeling in the actual industrial processes are investigated.Finally,combined with the current trend of industrial intelligent construction and its challenging problems,the future research ideas and development directions are pointed out.