Sequencing batch-reactor control using Gaussian-process models.

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Data Science

Background:

• Sequencing Batch Reactors (SBRs) are widely used for wastewater treatment.
• Effective control of SBRs is crucial for optimizing treatment efficiency and effluent quality.
• Traditional SBR control methods often rely on fixed timers or simple threshold values, which can be suboptimal.

Purpose of the Study:

• To develop and evaluate a Gaussian-process (GP) based control model for SBRs.
• To enable on-line optimization of batch-phase durations in SBRs.
• To improve the accuracy of predicting biodegradation process termination times.

Main Methods:

• Utilized a probabilistic, nonparametric Gaussian-process (GP) model for SBR control.
• Employed GP-based regression for signal smoothing of indirect process variables (pH, redox, dissolved oxygen).
• Applied GP-based classification for recognizing characteristic patterns in time-profiled process variables.

Main Results:

• The GP control algorithm demonstrated satisfactory agreement between predicted and actual biodegradation process termination times.
• Achieved final effluent concentrations below 1 mg L⁻¹ for ammonia and 0.5 mg L⁻¹ for nitrate in tested batches.
• Significantly shortened the aeration time required for effective treatment.

Conclusions:

• Gaussian-process modeling provides an effective framework for advanced SBR control.
• The proposed method enhances the precision of SBR operation by optimizing batch-phase durations.
• This approach leads to improved effluent quality and operational efficiency in wastewater treatment.