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Energy consumption model for wastewater treatment process control

机译:废水处理过程控制的能耗模型

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Wastewater treatment must satisfy discharge requirements under specified constraints and have minimal operating costs (OC). The operating results of wastewater treatment processes (WWTPs) have significantly focused on both the energy consumption (EC) and effluent quality (EQ). To reflect the relationship between the EC and EQ of WWTPs directly, an extended Elman neural network-based energy consumption model (EENN-ECM) was studied for WWTP control in this paper. The proposed EENN-ECM was capable of predicting EC values in the treatment process. Moreover, the self-adaptive characteristic of the EENN ensured the modeling accuracy. A performance demonstration was carried out through a comparison of the EC between the benchmark simulation model No. 1 (BSM1) and the EENN-ECM. The experimental results demonstrate that this EENN-ECM is more effective to model the EC of WWTPs.
机译:废水处理必须在指定的限制条件下满足排放要求,并具有最小的运营成本(OC)。废水处理过程(WWTP)的运营结果主要集中在能耗(EC)和废水质量(EQ)上。为了直接反映污水处理厂的EC和EQ之间的关系,本文研究了一种扩展的基于Elman神经网络的能源消耗模型(EENN-ECM),用于污水处理厂的控制。拟议的EENN-ECM能够预测治疗过程中的EC值。而且,EENN的自适应特性确保了建模的准确性。通过比较基准1号仿真模型(BSM1)和EENN-ECM之间的EC进行了性能演示。实验结果表明,该EENN-ECM更有效地模拟了污水处理厂的EC。

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