Abstract
Eco-efficient anaerobic ammonium oxidation (ANAMMOX) can eliminate toxic nutrients from wastewater, and has been used in several nutrient removal technologies. However, its implementation for robust operation remains challenging because of process nonlinearity and time variant characteristic, higher energy consumption, excess sludge produced and biomass loss during sludge pumping. Also, sensor failure, process startup and shut down present additional difficulties. In this study, an intelligent human machine interface using an advanced numerical solution for a knowledge-based system (called ANKSys) was developed by integrating the fully optimized-functionality (soft sensing, decision making, and simulating model) data driven by supervisory control. This control consists of advanced algorithms (artificial neural network, KALMAN filter, principal component analysis, least-square technique/renowned root mean squared error) using commercial software (MATLAB R2018a, Microsoft Visual Studio IDE 2016, Microsoft SQL Server 2014, OPC Automation with XGT series programmable logic controller (PLC)). The developed ANKSys can help in online monitoring and optimal process operation by assessing risk and failure occurrences, acquiring data for data analysis, and managing operating expenditure. In real time implementation, ANKSys enhanced the energy efficiency i.e., 16% of a pilot-scale "LEAOX" wastewater treatment plant (WWTP) located at Daegu, Republic of Korea. Using this strategy, an optimal and sustainable operation for the removal of biological nitrogen was achieved.