Abstract
Several technologies have been developed for nutrient removal based on the popular environment-friendly anaerobic ammonium oxidization (AnAmmOx) process. However, despite decades of research, it is significantly difficult to perform nutrient removal using AnAmmOx under strict regulations due to process complexity, nonlinearity, anomalies involving sensor failure, online monitoring, time consumption, increased consumable costs, lack of decision support, and sludge pumping for robust operation. This chapter presents the development and findings of a smart AnAmmOx system (SmartASys) that comprises a stand-alone package to address the aforementioned complex challenges. The package is equipped with supervisory control for different types of optimized data-driven functionalities. These functionalities (i.e., soft sensing, decision-making support for engineers, and a sequencing batch reactor mathematical model simulator) were developed using advanced algorithms (i.e., artificial neural network, Kalman filter, principal component analysis, and least-squares technique) and software (i.e., MATLAB® R2018a, Microsoft Visual Studio 2016 IDE; Integrated Development Environment, Microsoft SQL Server 2014, and object linking and embedding for process control Automation for connection with XGT series programing logic controller product group that combines the technology of LSIS). This study aims to enhance the energy consumption of a pilot-scale wastewater treatment plant located in Daegu (Republic of Korea).
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