Acid　production　with　flue　gas　is　a　complex　nonlinear　process　with　multiple　variables　and　strong　coupling.　The　operation　data　is　an　important　basis　for　state　monitoring,　optimal　control,　and　fault　diagnosis.　However,　the　operating　environment　of　acid　production　with　flue　gas　is　complex　and　there　is　much　equipment.　The　data　obtained　by　the　detection　equipment　is　seriously　polluted　and　prone　to　abnormal　phenomena　such　as　data　loss　and　outliers.　Therefore,　to　solve　the　problem　of　abnormal　data　in　the　process　of　acid　production　with　flue　gas,　a　data　cleaning　method　based　on　improved　random　forest　is　proposed.　Firstly,　an　outlier　data　recognition　model　based　on　isolation　forest　is　designed　to　identify　and　eliminate　the　outliers　in　the　dataset.　Secondly,　an　improved　random　forest　regression　model　is　established.　Genetic　algorithm　is　used　to　optimize　the　hyperparameters　of　the　random　forest　regression　model.　Then　the　optimal　parameter　combination　is　found　in　the　search　space　and　the　trend　of　data　is　predicted.　Finally,　the　improved　random　forest　data　cleaning　method　is　used　to　compensate　for　the　missing　data　after　eliminating　abnormal　data　and　the　data　cleaning　is　realized.　Results　show　that　the　proposed　method　can　accurately　eliminate　and　compensate　for　the　abnormal　data　in　the　process　of　acid　production　with　flue　gas.　The　method　improves　the　accuracy　of　compensation　for　missing　data.　With　the　data　after　cleaning,　a　more　accurate　model　can　be　established,　which　is　significant　to　the　subsequent　temperature　control.　The　conversion　rate　of　SO2　can　be　further　improved,　thereby　improving　the　yield　of　sulfuric　acid　and　economic　benefits.&　COPY;　2023　The　Chemical　Industry　and　Engineering　Society　of　China,　and　Chemical　Industry　Press　Co.,　Ltd.　All　rights　reserved.