A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling

Area of Science:

• Environmental Engineering
• Computational Intelligence

Background:

• Wastewater treatment processes (WWTP) are crucial for mitigating environmental pollution.
• Effluent scheduling significantly impacts WWTP efficiency and effluent quality.
• Existing scheduling methods struggle with the complex, constrained nature of WWTP effluent scheduling.

Purpose of the Study:

• To develop an optimized scheduling method for wastewater treatment plants.
• To address the challenges of complex constraints in effluent scheduling.
• To simultaneously improve maximum completion time and effluent quality.

Main Methods:

• Proposing a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm.
• Designing an effective encoding and decoding method for mapping solutions to schedules.
• Implementing a constraint handling strategy based on violation degree ranking.
• Incorporating a Sobel filter-based local search for enhanced efficiency.

Main Results:

• The RS-DPSO algorithm effectively handles the complex discrete solution space of WWTP effluent scheduling problems.
• Novel designs within the RS-DPSO algorithm demonstrate effectiveness.
• Comparative experiments show superior performance of RS-DPSO over existing algorithms.

Conclusions:

• RS-DPSO offers a novel and effective approach for solving wastewater treatment plant effluent scheduling problems.
• The algorithm enhances search ability and efficiency in discrete solution spaces.
• The proposed method provides a significant advancement in optimizing WWTP operations.