Aiming　at　the　secondary　air　flow　of　waste　incineration　process　are　usually　set　according　to　manual　experience,　which　is　subjective　and　arbitrary,　so　that　the　pollutant　emission　concentration　does　not　meet　the　standard,　an　intelligent　optimal　setting　method　is　proposed.　Firstly,　a　case-based　reasoning　pre-set　model,　and　an　evaluation　and　learning　model　of　secondary　air　flow　setpoint　are　constructed.　Secondly,　a　stochastic　configuration　network　process　index　prediction　model　is　established.　Then,　an　intelligent　compensation　model　based　on　the　RBF　neural　network　self-learning　fuzzy　inference　is　constructed.　Finally,　the　pre-set　model,　process　index　prediction　model,　intelligent　compensation　model　and　evaluation　and　learning　model　of　setpoint　are　organically　integrated,　the　structure　and　function　of　the　intelligent　optimal　setting　method　are　designed,　and　algorithm　implementation　is　given.　The　experimental　results　based　on　historical　data　of　a　waste　incineration　plant　show　that　the　fluctuation　degree　of　setpoint　obtained　by　this　method　is　less,　and　the　control　system　running　according　to　the　setpoint　can　reduce　the　pollutant　emission　concentration,　which　can　promote　the　realization　of　operation　optimal　goal　in　the　incineration　process.　©　2024　Northeast　University.　All　rights　reserved.