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Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy.

Abstract Model-based soft sensors can enhance online monitoring in wastewater treatment processes. These soft sensor scripts are executed either locally on a programmable logic controller (PLC) or remotely on a system with data-access over the internet. This work presents a cost-effective, flexible, open source IoT solution for remote deployment of a soft sensing algorithm. The system uses low-priced hardware and open-source programming language to set up the communication and remote-access system. Advantages of the new IoT architecture are demonstrated through a case study for remote deployment of an Extended Kalman Filter (EKF) to estimate additional water quality parameters in a multistage moving bed biofilm reactor (MBBR) plant. The soft-sensor results are successfully validated against standardised laboratory measurements to prove their ability to provide real-time estimations.

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