March 3, 2026

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

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.

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