Municipal　solid　waste　incineration　(MSWI)　technology　has　developed　rapidly　worldwide.　Carbon　monoxide　(CO)　is　one　of　the　to　be　controlled　key　operating　index　of　such　processes.　CO　emission　concentration　prediction　is　a　challenge　problem　duo　to　its　large　fluctuation　range.　A　new　CO　emission　concentration　prediction　method　based　on　concept　drift　detection　using　kernel　principal　component　analysis　(KPCA)　is　proposed.　The　proposed　approach　includes　off-line　model　construction　module,　on-line　concept　drift　detection　prediction　and　updating　module.　First,　we　construct　the　LSTM-based　CO　prediction　model　using　historical　data　and　KPCA-based　concept　drift　detection　model　for　calculating　the　evaluation　index.　Then,　recursive　KPCA　is　used　to　adaptive　monitor　the　concept　drift　of　the　time-varying　process.　Finally,　based　on　continuous　updating　of　the　historical　LSTM　mode　with　the　concept　drift　samples,　we　achieve　higher　prediction　accuracy.　The　rationality　and　validity　are　verified　with　the　actual　data　of　MSWI　processes.　©　2023　IEEE.