Abstract: The process parameter optimization of selective non-catalytic reduction (SNCR) denitration process can effectively reduce NO_x emission and denitration cost of cement calciner. Taking a cement calciner as the research object, a NO_x concentration prediction model based on LightGBM is established. Taking the minimization of denitration cost and NO_x concentration as the optimization objective, the deep deterministic policy gradient (DDPG) algorithm is used to optimize and control the relevant process parameters of SNCR denitration process of cement calciner mixed with sludge. The results show that the RMSE of NO_x concentration prediction model is 6.8 and MAPE is 3.48%. The DDPG algorithm can effectively optimize the relevant process parameters: when the ammonia injection amount and sludge combustion amount are 427.87 L/h and 9.78 t/h, respectively, the NO_x emission concentration is 225.99 mg/(Nm³) and the denitration operation cost is 1 747.8 yuan/h. Compared with the results of other optimization algorithms and conventional working conditions, the NO_x emission concentration and denitration cost of DDPG optimal control model show different degrees of decline. The simulation and effect verification of the model show that the model can output a reasonable combination of ammonia injection and sludge combustion, reduce the fluctuation of NO_x concentration at the outlet of SNCR, effectively reduce NO_x emission concentration and denitration cost, and realize the multi-objective optimal control of SNCR denitration system. This study can provide a reference for the multi-objective optimal control design of SNCR denitration of cement calciner based on intelligent algorithm.