Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network  

作　　者：Jiahui Liu Nan Guo Yixin Peng Wenlu Li Junfei Qiao Xiaolong Gao Wei Xiong 

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China [2]Beijing Laboratory of Smart Environmental Protection,Beijing 100124,China [3]Engineering Research Center of Intelligent Perception and Autonomous Control,Ministry of Education,Beijing 100124,China [4]Baotou Reclaimed Water Resources and Sewage Treatment Co.,Ltd.,Inner Mongolia 014000,China [5]Baotou Water Group,Inner Mongolia 014000,China

出　　处：《Chinese Journal of Chemical Engineering》2024年第12期318-326,共9页中国化学工程学报（英文版）

基　　金：gratefully acknowledge the financial support from the Scientific and Technological Innovation 2030-“New Generation Artificial Intelligence”Major Project(2021ZD0112301);National Natural Science Foundation of China(62273011,62076013,62303027).

摘　　要：Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares.Due to the complex and variable composition of the waste gas they handle,the combustion environment is severely affected by meteorological conditions.Key process parameters such as intake composition,flow rate,and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability.As a result,the control methods for these flares are limited,leading to poor control effectiveness.To address this issue,this paper proposes an adaptive sliding mode control method based on the radial basis function(RBF)network.Firstly,the operational characteristics of the petrochemical flare combustion process are analyzed,and a control model for the combustion process is established based on carbon dioxide detection.Secondly,an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system.Finally,by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law,fast and stable control of the flare combustion state is achieved.Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state,and the adaptive law also accomplishes system identification.

关 键 词：Petrochemical torch combustion process Sliding mode control RBF neural network System identification Fast response 

分 类 号：TQ038[化学工程] TP273[自动化与计算机技术—检测技术与自动化装置]