基于可控参数的前馈神经网络出水总氮预测模型研究

• 作 者: 赵子豪 王子昊 袁家洛 马骏 何哲灵 徐一兰 沈晓佳 朱亮
• 机 构: Institution of Environment Pollution Control and Treatment,Department of Environmental Engineering,Z
• 出 处: 工程（英文）
• 发表日期: 2021年2期
• 关键词: Feedforward neural network (FFNN) Algorithms Controllable operation parameters e

当前,出水总氮(TN)稳定达标是我国城镇污水处理厂面临的关键问题.本文基于我国城镇污水处理厂主流工艺——序批式活性污泥工艺(SBR),构建了一种基于可控参数的前馈神经网络(FFNN)出水总氮预测模型.与已有预测模型相比,本模型具备以下两个特点:①采用可控参数(表面气速与缺氧段时长)代替溶解氧作为模型主要输入参数,明显提高模型可用可控性;②采用算法优化的前馈神经网络构建模型,显著提高模型预测精准度.研究结果表明:量化共轭梯度法优化的FFNN模型预测精准,其拟合相关系数(R)明显高于其他相关模型;优化后的FFNN模型可根据进水与关键控制参数实现出水TN精准预测,有望实现城镇污水处理厂总氮稳定去除、系统节能降耗,助力国家“3060”碳目标实现.“,”The problem of effluent total nitrogen (TN) at most of the wastewater treatment plants (WWTPs) in China is important for meeting the related water quality standards, even under the condition of high energy consumption. To achieve better prediction and control of effluent TN concentration, an efficient predic-tion model, based on controllable operation parameters, was constructed in a sequencing batch reactor process. Compared with previous models, this model has two main characteristics: ① Superficial gas velocity and anoxic time are controllable operation parameters and are selected as the main input parame-ters instead of dissolved oxygen to improve the model controllability, and②the model prediction accu-racy is improved on the basis of a feedforward neural network (FFNN) with algorithm optimization. The results demonstrated that the FFNN model was efficiently optimized by scaled conjugate gradient, and the performance was excellent compared with other models in terms of the correlation coefficient (R). The optimized FFNN model could provide an accurate prediction of effluent TN based on influent water parameters and key control parameters. This study revealed the possible application of the optimized FFNN model for the efficient removal of pollutants and lower energy consumption at most of the WWTPs.