首页|Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the Al method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.Al methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.Al methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.Al methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results.

intelligent optimization algorithmneural networkscatalytic crackinglumped kinetics

Fan Yang、Mao Xu、Wenqiang Lei、Jiancheng Lv

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College of Computer Science,Sichuan University,Chengdu 610041,China

Algorithm and Big Data Center,New Hope Liuhe Co Ltd,Chengdu 610000,China

Data Intelligence Lab,New Hope Liuhe Co Ltd,Chengdu 610000,China

State Key Program of National Science Foundation of ChinaNational Natural Science Fund for Distinguished Young ScholarNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaKey Research and Development Project of Sichuan

618360066162520462106161616023282019YFG0494

2023

大数据挖掘与分析(英文版)

大数据挖掘与分析(英文版)

CSCDEI
ISSN:
年,卷(期):2023.6(3)
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