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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 article, 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). 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 located at Daegu, Republic of Korea. Using this strategy, an optimal and sustainable operation for the removal of biological nitrogen was achieved. [ABSTRACT FROM AUTHOR]

Parameter tuning, which includes the parameter estimation of the anaerobic ammonium oxidation (ANAMMOX) process, is challenging owing to the nonlinear and complex nature of the biological structure and microbial symmetry. This article presents an approach to overcome this challenge by identifying highly sensitive parameters by performing sensitivity analysis and parameter auto-tuning under various process conditions. To clarify this, 32% of the parameters were reduced compared to the base case by auto-tuning (the modeling approach). This advanced approach searches an optimum solution under the stringent boundary condition and auto updates the known parametric value with a new optimized one. The model is coded into MATLAB R2018a and incorporated using the extended activated sludge model (Extended ASM1) process equations that are solved using ODE45 solver tools. The obtained outcomes are validated using root mean square error method, where the error gap between the actual and predicted data is minimized. [ABSTRACT FROM AUTHOR]