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城市污水处理厂神经网络运营模型的构建与应用

发布时间:2018-10-11 20:00  文章来源:笔耕文化传播
【摘要】:随着计算机高速度计算的发展,神经网络模型在动态环境评价管理中尤其是在污水处理厂运营管理和预测评价中的应用逐渐成熟。由于其权重科学、客观;能同时对多变量进行有效地处理;具有很强的鲁棒性、记忆力及非线性拟合能力,因此使用神经网络模型对污水处理厂运营管理进行预测和评价,是对动态的多变量系统进行评价和管理过程中常用的方法。本文综合考虑水质、环境、经济效益及监督管理指标,基于深圳市污水处理厂现有评价体系进行资料调研,获取相关数据并通过回归分析、残差分析对数据进行处理,逐步建立和优化评价指标体系;基于沙井污水处理厂的历史数据和已建立的评价指标体系初步建立BP神经网络模型;采用历史数据对模型进行训练、校验及预测;最后通过逐层调整隐层数和节点数获取优化模型。对已建立的优化模型进行稳定性校验,最终获取高稳定性优化模型:[6,6,7];然后获取其权重矩阵并进行显著性分析,得出:单位污水絮凝剂消耗量、运营负荷率的权重值最大,分别为0.132和0.128,而在实际运营管理过程中对污水处理厂影响较大的出水COD、进水流量的权重值却很小;为实现实际运营管理中出水COD、进水流量的权重值最大,需要逐一增减高稳定性优化模型的指标变量、调节隐层数和节点数,然后对其进行稳定性校验,最终获取高稳定性深度优化模型:[10,5,8,7]。通过比较模型的精准度及预测能力,确定高稳定性深度优化模型为综合评价模型;通过对比深圳市污水处理厂经营评估模型和综合评价模型影响因素的指标权重,分析各个影响指标对沙井污水处理厂运营管理的具体影响,最终从运营管理、节能减排等角度给政府和污水处理厂相关管理人员提供合理的政策建议。
[Abstract]:With the development of computer high speed calculation, the application of neural network model in dynamic environmental assessment management, especially in the operation management and prediction evaluation of sewage treatment plant has gradually matured. Because its weight is scientific, objective, it can deal with multivariable effectively at the same time, it has strong robustness, memory and nonlinear fitting ability, so the neural network model is used to predict and evaluate the operation and management of sewage treatment plant. It is a common method to evaluate and manage dynamic multivariable system. In this paper, water quality, environment, economic benefit and supervision and management index are considered synthetically. Based on the existing evaluation system of wastewater treatment plant in Shenzhen, the relevant data are obtained, and the data are processed by regression analysis and residual analysis. The evaluation index system is gradually established and optimized, the BP neural network model is established based on the historical data of manhole sewage treatment plant and the established evaluation index system, and the model is trained, calibrated and predicted by historical data. Finally, the optimization model is obtained by adjusting the number of hidden layers and the number of nodes layer by layer. The stability of the established optimization model is checked, and the high stability optimization model is obtained, and then the weight matrix is obtained and the significant analysis is carried out. It is concluded that the weight value of the consumption of flocculant per unit sewage and the operating load rate is the largest. In the process of actual operation and management, the weight value of effluent COD, influent flow is very small, and in order to realize the actual operation management, the weight value of effluent COD, influent flow is the largest. It is necessary to add and decrease the index variables of the high stability optimization model one by one, adjust the number of hidden layers and the number of nodes, and then check the stability of the model, and finally obtain the high stability depth optimization model: [10]. By comparing the accuracy and prediction ability of the model, the high stability and depth optimization model is determined as the comprehensive evaluation model, and the index weights of the influencing factors of the Shenzhen sewage treatment plant management evaluation model and the comprehensive evaluation model are compared. This paper analyzes the specific impact of each impact index on the operation and management of the sewage treatment plant of manhole, and finally provides reasonable policy recommendations to the government and the relevant management personnel of the sewage treatment plant from the aspects of operation management, energy saving and emission reduction.
【学位授予单位】:哈尔滨工业大学
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:X703

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