Data-driven soft constrained model predictive control with disturbance rejection for wastewater treatment processes

Key Points

• Improved disturbance rejection is observed, enhancing system stability and performance in wastewater treatment processes.
• Data-driven modeling shows a reduction in error by up to 25%, significantly improving control accuracy.
• Soft constrained model predictive control was implemented, allowing for flexible adjustments in operational parameters.
• Overall findings suggest that integrating data-driven techniques can optimize wastewater treatment efficiency.