AOPC-based control for efficient uncertainty mitigation in UASB wastewater treatment with multiple manipulated variables and distributed biomass integration

•Optimization-based control strategy for UASB reactor with Danckwerts-boundary conditions and biomass distribution.•Applied an AOPC-based controller with multiple manipulated inputs for a UASB process described by a PDE-ODE model.•Control performance was investigated by introducing variations in inlet concentration (15–30 %) and bacterial growth rates (10–30 %).•Superior performance was demonstrated by the developed control system compared to a PI controller, emphasizing its inherent robustness. 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. [Display omitted]