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Energy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations

机译：污水处理厂的节能：在不确定性和数据可用性限制下的每日基准测试

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摘要

Efficient management of Waste Water Treatment Plants (WWTPs) can produce significant environmental and economic benefits. Energy benchmarking can be used to compare WWTPs, identify targets and use these to improve their performance. Different authors have performed benchmark analysis on monthly or yearly basis but their approaches suffer from a time lag between an event, its detection, interpretation and potential actions. The availability of on-line measurement data on many WWTPs should theoretically enable the decrease of the management response time by daily benchmarking. Unfortunately this approach is often impossible because of limited data availability. This paper proposes a methodology to perform a daily benchmark analysis under database limitations. The methodology has been applied to the Energy Online System (EOS) developed in the framework of the project "INNERS" (INNovative Energy Recovery Strategies in the urban water cycle). EOS calculates a set of Key Performance Indicators (KPIs) for the evaluation of energy and process performances. In EOS, the energy KPIs take in consideration the pollutant load in order to enable the comparison between different plants. For example, EOS does not analyse the energy consumption but the energy consumption on pollutant load. This approach enables the comparison of performances for plants with different loads or for a single plant under different load conditions. The energy consumption is measured by on-line sensors, while the pollutant load is measured in the laboratory approximately every 14 days. Consequently, the unavailability of the water quality parameters is the limiting factor in calculating energy KPIs. In this paper, in order to overcome this limitation, the authors have developed a methodology to estimate the required parameters and manage the uncertainty in the estimation. By coupling the parameter estimation with an interval based benchmark approach, the authors propose an effective, fast and reproducible way to manage infrequent inlet measurements. Its use enables benchmarking on a daily basis and prepares the ground for further investigation.
机译：废水处理厂（WWTP）的有效管理可以产生巨大的环境和经济效益。能源基准测试可用于比较污水处理厂，确定目标并利用它们来改善其绩效。不同的作者每月或每年进行基准分析，但是他们的方法受到事件，事件的检测，解释和可能采取行动之间的时间间隔的困扰。从理论上讲，许多污水处理厂的在线测量数据的可用性应该可以减少管理响应时间。不幸的是，由于数据可用性有限，这种方法通常是不可能的。本文提出了一种在数据库限制下执行每日基准分析的方法。该方法已应用于在项目“ INNERS”（城市水循环中的INNovative能源回收策略）框架下开发的在线能源系统（EOS）。 EOS计算一组关键绩效指标（KPI），用于评估能源和过程绩效。在EOS中，能源KPI考虑到污染物负荷，以便能够在不同工厂之间进行比较。例如，EOS不会分析能耗，而是分析污染物负荷下的能耗。这种方法可以比较具有不同负载的工厂或在不同负载条件下的单个工厂的性能。能耗通过在线传感器测量，而污染物负荷大约在实验室中每14天测量一次。因此，水质参数的不可用是计算能量KPI的限制因素。在本文中，为了克服此限制，作者开发了一种方法来估计所需的参数并管理估计中的不确定性。通过将参数估计与基于间隔的基准方法相结合，作者提出了一种有效，快速且可重现的方法来管理不频繁的入口测量。它的使用可以每天进行基准测试，并为进一步的调查做准备。

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