Uncertainties　in　the　calorific　value　and　composition　of　municipal　solid　waste　(MSW),　the　variability　in　operations,　equipment　performance　decline,　maintenance　needs,　and　other　dynamic　factors　make　it　challenging　for　operational　experts　to　accurately　gauge　the　overall　combustion　impact　under　fluctuating　conditions.　There　is　a　need　for　a　visual　numerical　simulation　of　the　full　MSW　incineration　(MSWI)　process,　with　a　focus　on　the　effectiveness　of　the　grate　speed　and　air　volume　ratio.　Unfortunately,　software　like　Aspen　Plus　struggles　to　visually　depict　the　effect　of　typically　manipulated　variables　such　as　grate　speed,　and　it　is　not　well-suited　for　simulating　solid-phase　combustion　and　flue　gas　treatment.　In　response　to　these　challenges,　our　study　introduces　a　numerical　simulation　method　for　the　MSWI　process　using　a　multi-software　coupling　approach.　This　simulation　method　incorporates　(a)　the　use　of　custom　software　for　simulating　solid-phase　combustion　on　the　grate,　(b)　the　application　of　Computational　Fluid　Dynamics　(CFD)　software　for　gas-phase　combustion　in　the　furnace,　and　(c)　the　utilization　of　chemical　process　simulation　software　for　non-grate　solid-phase　combustion.　The　benchmark　condition　was　determined　using　real-time　running　data　from　the　Beijing　MSWI　Power　Plant.　The　results　show　that　for　the　incinerator＇s　third　flue,　flue　gas　G1,　and　flue　gas　G3,　the　relative　errors　between　the　simulated　and　actual　values　for　flue　gas　temperature　and　oxygen　concentration　were　0.3%　and　1.1%,　3.5%　and　20%,　and　0.2%　and　20%　respectively.　In　addition,　we　used　eight　non-benchmark　conditions　to　study　the　effects　of　grate　speed　and　air　volume　ratio.　When　the　grate　speed　was　gradually　increased　from　6　m/h　to　8　m/h,　O2,　CO2,　sulfur　dioxide　(SO2),　sulfur　trioxide(SO3),　nitrogen　monoxide　(NO),　and　nitrogen　oxide　(NO2)　at　G1　had　error　fluctuation　ranges　of　5.9%-48.5%,　2.5%-11.5%,　10.42-28.57%,　7-23%,　29.10-45.52%,　and　24.39-48.78%,　respectively.　When　the　primary　air　ratio　was　gradually　increased　from　0.84　to　0.96,　the　O2　concentration　at　G1　exhibited　a　steady　upward　trend.　The　simulation　results　provide　insights　into　the　adjustment　range　of　the　manipulated　variables　and　offer　additional　support　for　the　visualized　and　optimized　control　operation　of　the　MSWI　plant.