Wastewater Treatment Control for DO and Nitrate Set-Point Optimization

The control system is segmented into multiple hierarchical levels (upper level for strategic optimization, lower level for real-time control) to handle different time-scale performance indices separately. Each level addresses specific control objectives, allowing complex multi-objective optimization to be broken down into manageable segments that can be solved independently while maintaining overall system performance.

A cooperative optimization algorithm acts as an intermediary between conflicting performance indices (effluent quality, energy consumption, operation cost). This intermediary layer processes multiple conflicting objectives and generates coordinated control signals that balance competing requirements, resolving conflicts without requiring direct confrontation between opposing control goals.

The system dynamically changes operating parameters (aeration rates, recycle flows, sludge wasting rates) based on real-time conditions and optimization results. By continuously adjusting these parameters through cooperative optimization, the system finds the optimal balance point where effluent quality requirements are met while minimizing energy consumption and operation costs, rather than maintaining fixed conservative settings.

The control system transitions from static, fixed-setpoint control to dynamic, adaptive optimization. The cooperative optimization algorithm continuously recalculates optimal operating points based on changing conditions, allowing the system to adaptively balance effluent quality and operation cost in real-time, capturing transient opportunities for cost reduction while maintaining quality standards.

The computational task is segmented across different time scales and control levels. The upper level performs less frequent, more computationally intensive optimization for strategic decision-making, while the lower level handles real-time control with simpler, faster algorithms. This segmentation allows real-time optimal control to be implemented without overwhelming computational demands at any single level.

The system performs preliminary calculations and predictions using data-driven models and historical data to anticipate optimal control actions. By pre-computing optimization results based on predicted future states and preparing control strategies in advance, the system reduces real-time computational burden while maintaining high efficiency and adaptability to changing conditions.