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.