Activated sludge model no. 1 calibration for a paper mill wastewater treatment plant in Finland

Abstract

The Wastewater Treatment Plant (WWTP) in the paper industry faces challenges in controlling and estimating Chemical Oxygen Demand (COD) to improve monitoring and optimize the process under varying operational and environmental conditions. However, maintaining the COD content at the desired level becomes difficult due to constantly changing environmental conditions and stricter regulations. A common method in WWTP to remove organic COD is aeration treatment. For simulating the aeration process, Activated Sludge Model No.1 (ASM1) is a widely used tool, but it requires an abundance of influent COD concentration samples to estimate the effluent COD. Collecting these samples is costly both in terms of time and human resources. This study aims to use limited wastewater samples to generate artificial data for the ASM1 model using linear regression techniques. The objective is to reduce costs associated with COD concentration sample collections for a WWTP processing wastewater of a Finnish paper mill, while still providing reliable estimations for effluent COD over an extended period by identifying the optimal tuning parameters for the ASM1.