AOPC-Based Control for Efficient Uncertainty Mitigation in UASB Wastewater Treatment with Multiple Manipulated Variables and Distributed Biomass Integration
Computers & Chemical Engineering(2024)
摘要
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.
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关键词
Coupled PDE-ODE models,Nonlinear optimization-based control,Multiple manipulated variables,Adaptive optimal predictive control,UASB reactor system
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