To　satisfy　incineration　efficiency　and　nitrogen　oxides　ultra-low　emission　requirements,　it　is　of　great　importance　to　improve　the　operational　performance　of　the　municipal　solid　waste　incineration　(MSWI)　process.　However,　due　to　the　complex　and　fluctuating　composition　of　municipal　solid　waste,　it　is　challenging　to　guarantee　optimal　operation　under　changing　conditions.　To　address　this　issue,　a　multi-condition　operational　optimization　method　based　on　adaptive　knowledge　transfer　(MCOO-TR)　is　proposed　for　the　MSWI　process.　First,　an　integrated　optimization　scheme,　where　different　operation　conditions　are　identified　based　on　cluster　analysis　and　expert　knowledge,　is　developed.　Second,　a　surrogate-assisted　multi-objective　particle　swarm　optimization　algorithm　is　designed　to　solve　the　data-driven　optimization　problems　for　optimal　operation,　where　the　surrogate　model　selection　mechanism　is　proposed　to　cope　with　multi-condition　characteristics.　Third,　an　adaptive　knowledge　transfer　strategy,　corresponding　to　condition　identification,　is　designed　aiming　to　improve　optimization　efficiency.　Finally,　the　proposed　MCOO-TR　method　is　evaluated　based　on　actual　industrial　data.　The　experimental　results　demonstrate　that　the　MCOO-TR　method　can　achieve　satisfactory　operational　performance　under　changing　operation　conditions.