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.　©　2024　The　Chemical　Industry　and　Engineering　Society　of　China,　and　Chemical　Industry　Press　Co.,　Ltd