AOPC-Based Control for Efficient Uncertainty Mitigation in UASB Wastewater Treatment with Multiple Manipulated Variables and Distributed Biomass Integration

This study proposes an adaptive optimal predictive control (AOPC) for the upflow anaerobic sludge blanket (UASB) reactor with recirculation, employing a PDE-ODE system. Effectively addressing complexity and uncertainties associated with Danckwerts-boundary conditions and biomass distribution, an analytical model predictive control scheme incorporates adaptive set points and compensators. By dynamically adjusting multiple manipulated inputs, including the feed flow rate and recirculation-to-feed ratio, the controller achieves precise effluent concentration control. The robustness of the control system is investigated through fluctuations in inlet concentrations (15-30%) and variations in bacterial growth rates (10-30%). The control performance index, ISE, indicates that the AOPC-based control system outperforms the PI controller by 28-150 times for inlet concentration variations and 3-84 times for growth rate changes. With a settling time of 1-3 days, the proposed system excels over the conventional controller, which consistently struggles to maintain the target, emphasizing its inherent robustness.