The　sulfur　dioxide　blower　refers　to　a　centrifugal　blower　that　transports　various　gases　in　the　sulfuric　acid　production　process　from　flue　gases.　Accurately　predicting　the　outlet　pressure　of　the　sulfur　dioxide　blower　is　significant　for　the　sulfuric　acid　production　process　from　flue　gases.　Due　to　the　complex　internal　structure　of　the　sulfur　dioxide　blower,　it　is　difficult　to　establish　a　precise　mechanism　model.　In　this　paper,　a　novel　hybrid　algorithm　combining　Autoregressive　exogenous　(ARX)　model　and　Sage-Husa　adaptive　Kalman　filter　is　used　to　establish　the　sulfur　dioxide　blower　model　and　predict　its　outlet　pressure.　Where　the　Akaike　Information　Criterion　(AIC)　is　used　to　determine　the　order　of　the　ARX　model,　and　the　least　square　method　is　used　to　determine　the　ARX　model　parameters.　Considering　the　high-order　ARX　model　parameter　estimation　is　difficult　to　calculate,　the　optimal　ARX　model　is　determined　in　the　low-order　range,　and　the　Kalman　equation　of　state　and　observation　equation　are　constructed　using　this　model.　By　combining　the　ARX　model　and　the　Sage-Husa　adaptive　Kalman　filter,　experiment　shows　that　the　proposed　algorithm　obtains　better　prediction　effect　than　the　traditional　time　series　model　combined　with　Kalman　filter.　©　2022　IEEE.