An optimization method of structure parameters based on the quadratic regression orthogonal combination (QROC) and Genetic Algorithm (GA) is proposed in this work. The following work has been conducted to improve the performance of the coal pyrolysis filtration system and prolong the service life of the filter tubes based on QROC-GA method. Firstly, a simulation model is established and two factors always are chosen as optimization objectives. Then one single factor regression prediction algorithm is used to optimize each factor separately while the result was not satisfactory. Secondly, QROC is introduced to achieve the optimization of two factors in the filtration system. The regression relationship is obtained proved to be effective by statistical test and back propagation neural network (BPNN). Finally, a QROC-GA method is established to find the optimization points. Then a verification calculation is done with CFD again. The optimal result has the parameters that φ=40° and ψ=25°. From the simulation results, the mean square error is 0.401. The mean square error is 0.4992 by the QROC-GA results. The errors are within 0.1 between CFD and QROC-GA. The QROC-GA model has a good effect in the prediction of models under changing parameters, and the significance is also confirmed.

提出了基于二次回归正交组合（QROC）和遗传算法（GA）的结构参数优化方法。为了提高煤热解过滤系统的性能并延长基于QROC-GA方法的滤管的使用寿命，进行了以下工作。首先，建立仿真模型，并始终选择两个因素作为优化目标。然后使用一种单因素回归预测算法分别对每个因素进行优化，但结果并不令人满意。其次，引入QROC来实现过滤系统中两个因素的优化。通过统计检验和反向传播神经网络（BPNN）证明了回归关系是有效的。最后，建立QROC-GA方法以查找优化点。然后，再次使用CFD进行验证计算。最佳结果的参数为φ= 40°和ψ= 25°。根据仿真结果，均方误差为0.401。QROC-GA结果的均方误差为0.4992。CFD和QROC-GA之间的误差在0.1以内。QROC-GA模型在参数变化的情况下对模型的预测有很好的效果，其意义也得到了证实。