Ceramic filters have been widely used in industrial engineering, such as the Shell coal gasification process (SCGP) and Integrated gasification combined cycle (IGCC), where the performance of the ceramic filter is achieved by the pulse jet. Residual pressure drop and gas consumption are directly related to reverse-flow pulse (RFP) pressure. However, in the process of operation, the RFP pressure is too large and gas consumption is too high. In this study, the effects of RFP pressures on filter’s cleaning efficiency, residual pressure drop, and gas consumption were investigated on a ceramic filter. Within a certain range, the cleaning efficiency gradually increased with increased RFP pressure. When the RFP pressure reached a certain value, the cleaning efficiency did not increase with increased pressure, showing a quadratic relationship between cleaning efficiency and RFP pressure. The residual pressure drop and RFP pressure were also in a quadratic relationship. Besides, the gas consumption increased linearly as increased RFP pressure according to the theoretical model. Based on the research results, a multi-objective optimization model of a ceramic filter was established with the cleaning efficiency as a constraint condition, gas consumption and residual pressure drop as the optimization objectives. A fuzzy decision-making method was used to solve the optimization model and calculate the residual pressure drop and gas consumption, from which the optimal RFP pressure was obtained.

陶瓷过滤器已广泛用于工业工程中，例如壳牌煤气化工艺（SCGP）和集成气化联合循环（IGCC），其中陶瓷过滤器的性能是通过脉冲喷射实现的。残余压降和气体消耗与回流脉冲（RFP）压力直接相关。但是，在操作过程中，RFP压力太大，气体消耗太高。在这项研究中，研究了RFP压力对陶瓷过滤器清洁效率，残余压降和气体消耗的影响。在一定范围内，清洁效率随着RFP压力的增加而逐渐提高。当RFP压力达到一定值时，清洁效率不会随压力的增加而增加，显示清洗效率和RFP压力之间的二次关系。残余压降和RFP压力也呈二次关系。此外，根据理论模型，随着RFP压力的增加，气体消耗呈线性增加。基于研究结果，建立了以清洗效率为约束条件，耗气量和残余压降为优化目标的陶瓷过滤器多目标优化模型。采用模糊决策方法对优化模型进行求解，计算出残余压降和耗气量，从而得到最优的RFP压力。根据理论模型，随着RFP压力的增加，气体消耗呈线性增加。基于研究结果，建立了以清洗效率为约束条件，耗气量和残余压降为优化目标的陶瓷过滤器多目标优化模型。采用模糊决策方法对优化模型进行求解，计算出残余压降和耗气量，从而得到最优的RFP压力。根据理论模型，随着RFP压力的增加，气体消耗呈线性增加。基于研究结果，建立了以清洗效率为约束条件，耗气量和残余压降为优化目标的陶瓷过滤器多目标优化模型。采用模糊决策方法对优化模型进行求解，计算出残余压降和耗气量，从而得到最优的RFP压力。