Dynamic optimal ground water remediation including fixed and operation costs

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Optimization

Background:

• Groundwater remediation faces challenges in optimizing control due to the complexity of simultaneous fixed and time-varying costs.
• Existing methods struggle to efficiently balance installation expenses with operational expenditures in dynamic remediation scenarios.

Purpose of the Study:

• To develop a novel algorithm for time-varying groundwater remediation that integrates fixed and operating costs.
• To address the computational limitations of solely using genetic algorithms for dynamic policy optimization.

Main Methods:

• Integration of a genetic algorithm (GA) to handle fixed costs, such as well installation.
• Application of constrained differential dynamic programming (CDDP) to manage time-varying operating costs efficiently.
• Validation through a hypothetical case study with both cost types.

Main Results:

• The proposed GA-CDDP algorithm effectively solves the complex time-varying groundwater remediation problem.
• Fixed costs significantly impact the optimal number and placement of remediation wells.
• The integrated algorithm demonstrates potential for substantial total cost savings.

Conclusions:

• The novel GA-CDDP algorithm provides an effective solution for optimizing groundwater remediation under mixed cost structures.
• Strategic consideration of fixed costs is crucial for efficient well placement and overall remediation strategy.
• This approach offers a computationally feasible method for achieving significant cost reductions in dynamic remediation projects.