Abstract

Aiming at reducing their emissions, wastewater treatment plants (WWTP) seek to reduce their energy consumption, where a large amount is used for the aeration. The case plant, Grindsted WWTP uses an alternating aeration strategy, with a common air supply system facilitating the process in four aeration tanks and thus making optimisation challenging. In this work, a nonlinear model of the air supply system is designed, in which multiple key parameters are estimated by data-driven optimization. Subsequently, a model-based control strategy for scheduling of compressors and desired airflow is proposed, to save energy without compromising the aeration performance. The strategy is based upon partly static- partly dynamic models of the compressors, describing their efficiency in terms of system head and volumetric airflow rate. The simulation study uses real plant data and shows great potential for improvement of energy efficiency, regardless of the aeration pattern in any of the four process tanks, and furthermore contributes to a reduction in compressor restarts per day. The proposed method is applicable to other WWTP with multiple compressors in the air supply system, as this study is conducted using first principle models validated on data from the daily operation.

Highlights

  • Since antiquity, urban populations have realized the importance of good quality drinking water [1]

  • This study aims to improve energy efficiency in activated sludge process (AASP) aeration systems, while still prioritizing nutrient removal in the wastewater, and we set up a hypothesis to investigate whether the energy efficiency of the compressed air supply in an AASP can be increased without changing the aeration pattern and dissolved oxygen (DO) set-point generation and tracking

  • In this work, Grindsted WWTP located near Billund, Denmark is used as case plant providing the data for this simulation study, where improvements for the aeration process in the activated sludge process (ASP) is proposed

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Summary

Introduction

Urban populations have realized the importance of good quality drinking water [1]. Wastewater was mostly disposed of in the streets or near population centers, resulting in serious consequences for the public health and the environment [3]. This is evident by the numerous epidemics and waterborne diseases occurring throughout Europe until the nineteenth century [1,4,5]. The most efficient way to reduce waste outputs is to reduce inputs and to make treatment processes more efficient [5,6] The latter has been a growing topic of investigation since the late twentieth century, with the introduction of detailed simulation models and control methods for various wastewater processes [7,8,9]

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