Control Optimization Through Prediction-Based Wastewater Management

David Konstantin Tilcher, F. Popescu, H. Sommer, L. Thamsen, P. Thamsen
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Abstract

As part of a collaborative research project (OPTIMA) by Fraunhofer FOKUS, Engineering Company Prof. Dr. Sieker mbH, Department of Distributed and Operating Systems and Department of Fluid System Dynamics, TU Berlin, an „Intelligent Pumping Station” is being developed. In this research project, the operation of wastewater pumping stations is to be optimized by integrating precipitation forecasts and recording operating conditions on one hand, and by integrating historical data on use and operation on the other. The individual strategies for optimizing the operation of pumping stations and the possibilities of data integration will be systematically investigated. The focus of this paper is on the method for developing an optimized pump control. It examines how knowledge of predicted inflow can be used to achieve energy savings and a reduction in wastewater overflows. This method is based on the development of an algorithm in which detailed consideration of pump specifics and future pumping station inflow can be used to predict all possible suction head level curves for the considered period of time. Depending on the target criterion — minimum energy consumption per transported cubic meter or minimum overflow volume — the algorithm calculates the optimum path from all possible suction chamber level curves.
基于预测的污水管理控制优化
作为Fraunhofer FOKUS、工程公司分布式与操作系统系和柏林工业大学流体系统动力学系的教授、博士、博士合作研究项目(OPTIMA)的一部分,“智能泵站”正在开发中。在本研究项目中,通过结合降水预报和记录运行情况,结合历史使用和运行数据,对污水泵站的运行进行优化。将系统地研究优化泵站运作的个别战略和数据整合的可能性。本文的重点是开发优化泵控制的方法。它研究了如何利用预测流入的知识来实现节能和减少废水溢出。该方法基于一种算法的发展,该算法详细考虑了泵的特性和未来泵站的流入,可以用来预测所考虑的时间段内所有可能的吸头液位曲线。根据目标标准——每输送立方米的最小能耗或最小溢出体积——该算法从所有可能的吸入室水平曲线中计算出最佳路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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