The　nonlinear,　time-varying,　and　lagging　nature　of　the　municipal　solid　waste　incineration　(MSWI)　process　presents　challenges　for　ensuring　controller　safety.　While　offline　reinforcement　learning　(RL)　can　ensure　safety　in　furnace　temperature　(FT)　control,　its　performance　is　hindered　by　extrapolation　errors,　making　it　unsuitable　for　direct　application　in　the　incineration　environment.　To　address　this,　we　propose　a　conservative　Q-learning-based　furnace　temperature　control　strategy　(CQL-FTC).　This　strategy　involves　two　stages:　online　sampling　and　offline　training.　During　the　online　sampling　stage,　the　agent　interacts　with　the　environment　to　collect　samples,　building　an　experience　replay　buffer　(ERB)　and　performing　pretraining.　In　the　offline　training　stage,　we　introduce　the　CQL　method,　adding　constraint　terms　to　the　traditional　Bellman　equation　to　minimize　extrapolation　errors.　After　offline　training,　the　agent　is　directly　applied　to　the　FT　setpoint　control　in　the　incineration　process.　Simulation　results　using　the　actual　MSWI　process　dataset　demonstrate　the　effectiveness　of　the　proposed　method　in　complex　industrial　environments.　©　2024　IEEE.