In　the　production　process　of　the　process　industry,　precise　adjustment　of　working　conditions　presents　a　challenge　due　to　the　complexity　of　processes　and　unknown　disturbances.　Central　control　operators　need　to　adjust　setpoints　based　on　deviations　in　process　parameters　and　monitor　target　values　to　maintain　system　stability.　However,　many　operating　procedures　excessively　rely　on　human　experience,　increasing　the　uncertainty　of　the　production　process.　In　addition,　the　expert　knowledge　is　not　fully　embedded　in　accumulated　operations,　limiting　its　potential　in　decision　support.　Therefore,　data-driven　modeling　of　production　processes　is　essential　for　developing　industrial　expert　systems　to　realize　intelligent　manufacturing.　This　work　proposes　a　work　condition　prediction　framework　based　on　an　Operation　Mode　Library　(OML)　to　realize　Working　Condition　Prediction　(WCP),　called　for　OML-WCP　short.　Taking　the　cement　rotary　kiln　adjustment　process　as　an　example,　a　stable　OML　is　constructed　using　Gaussian　mixture　clustering　technology.　Experimental　results　with　real-life　operation　data　of　a　cement　plant　reveal　that　the　prediction　accuracy　of　OML-WCP　outperforms　the　existing　methods.　Moreover,　the　continuous　accumulation　of　operating　mode　libraries　can　improve　prediction　accuracy　in　practical　annlications.　©　2024　IEEE.