Carbon　monoxide　(CO)　is　a　hazardous　gas　discharged　during　municipal　solid　waste　incineration　(MSWI).　Its　emission　concentration　serves　as　a　vital　indicator　for　assessing　the　stability　of　the　MSWI　process.　Therefore,　accurate　prediction　of　CO　emissions　is　crucial.　While　existing　research　predominantly　relies　on　historical　real　data-driven　models,　it　often　overlooks　the　effective　utilization　of　the　combustion　mechanism.　This　article　introduced　a　novel　approach:　a　heterogeneous　ensemble　prediction　model　that　integrates　virtual　and　real　data.　Firstly,　virtual　mechanism　data　was　obtained　through　a　multi-condition　mechanism　model　constructed　using　coupled　numerical　simulation　software　of　FLIC　and　Aspen　Plus.　Secondly,　based　on　this　virtual　mechanism　data,　a　linear　regression　decision　tree　(LRDT)　algorithm　was　employed　to　establish　the　mechanism　mapping　model.　Simultaneously,　a　real　historical　data-driven　model　based　on　a　long　short-term　memory　(LSTM)　neural　network　algorithm　was　developed.　In　the　offline　training　verification　phase,　the　heterogeneous　models　were　combined　using　an　inequality-constrained　random　weighted　neural　network　(CIRWNN)　after　aligning　virtual　and　real　samples　representing　operating　conditions　based　on　the　k-nearest　neighbor　(KNN)　approach.　Subsequently,　in　the　online　testing　verification　stage,　CO　online　prediction　was　achieved　by　ensemble　the　LRDT-based　mechanism　mapping　model　and.　the　LSTM-based　historical　data-driven　model.　The　proposed　method＇s　effectiveness　and　rationality　were　validated　through　an　industrial　case　study　of　MSWI　process　in　Beijing.　©　2024