污水处理过程的QSOM出水水质预报

针对活性污泥污水处理过程中微生物活动的不确定性、生化反应的复杂性及工艺参数的强耦合和大滞后等特性，提出一种量子自组织特征映射神经网络（QSOM）方法来进行出水水质预报。该方法将出水水质在异常情况下所对应的进水数据样本转换成量子态形式提交给网络输入层，通过计算量子输入与相应权值的相关系数作为网络的最佳输入匹配，学习规则中采用量子门更新网络权值。最后通过某污水处理厂生化处理过程中的实际运行数据的实验表明所提预报方法是有效的。

Prognoses of inlet water quality via QSOM in wastewater treatment processes

A quantum self organizing feature map neural network (QSOM) method is introduced for water quality prediction in activated sludge wastewater treatment processes which includes uncertainty of microbial activity and complexity of biochemical reactions and strong lagging of parameters. This approach quantizes the inlet water quality data corresponding outlet water in abnormal state and makes the quantized data sample as the input of QSOM. The correlation coefficient of the quantum inputs and its weights are calculated as the best inputs matching of network by using quantum gates to update the weights in learning the rules. The experiments illustrate the efficiency of this prediction approach by using operational data of Chongqing Jiguanshi wastewater treatment plant.