Ling Guobi, Wang Zhiwen, Shi Yaoke, Wang Jieying, Lu Yanrong, Li Long
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China.
Membranes (Basel). 2022 Jul 4;12(7):691. doi: 10.3390/membranes12070691.
In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent-PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.
鉴于实时获取膜生物反应器（MBR）膜通量存在困难，考虑到反向传播（BP）网络在预测MBR膜通量方面的缺点，如模型存在局部最小值和泛化能力差等问题，本文在标准麻雀搜索算法（SSA）中引入帐篷混沌映射，提高了种群分布的均匀性和算法的搜索能力（用于优化BP网络的关键参数）。建立了帐篷麻雀搜索算法反向传播网络（Tent-SSA-BP）膜污染预测模型，以实现对膜通量的准确预测；与BP、遗传算法反向传播网络（GA-BP）、粒子群优化反向传播网络（PSO-BP）、麻雀搜索算法极限学习机（SSA-ELM）、麻雀搜索算法反向传播网络（SSA-BP）和帐篷粒子群优化反向传播网络（Tent-PSO-BP）模型相比，具有独特优势。与改进前的BP模型相比，改进后的软测量模型的平均绝对百分比误差（MAPE）降低了96.76%，均方根误差（RMSE）降低了99.78%，平均绝对误差（MAE）降低了95.61%。本文提出的算法预测准确率达到97.4%，远高于BP的48.52%。也高于其他预测模型，预测准确率有了很大提高，具有一定的工程参考价值。
