Optimal design of water treatment plant under uncertainty using genetic algorithm

Abstract

An approach that links genetic algorithm (GA) as an optimization tool with Monte Carlo simulation (MCS)-based reliability program is presented for reliability-constrained optimal design of water treatment plant (WTP). The reliability of a WTP is defined as its ability to produce water of desired effluent water quality standard. The objective function minimizes the treatment cost subjected to reliability constraint, design constraints, and performance constraints. The random variables used to generate the reliability estimates are suspended solid concentration, viscosity of water, specific gravity of floc particle, sedimentation basin performance index, velocity gradient, and flow rate. The application of GA-MCS approach for design of a WTP is illustrated with a hypothetical case study. The application shows that the reliability of achieving desired water quality standard is affected by the uncertainty of system parameters. The suggested GA-MCS approach is efficient to evaluate treatment cost-reliability tradeoff in design of WTP.

Optimal Design of Regional Wastewater Pipelines and Treatment Plant Systems

“…Pollution loads and energy consumptions were minimized, showing a substantial improvement over previous applied control methodologies. Gupta and Shrivastava (2008) developed a treatment plant reliability-constrained optimal design model by linking a genetic algorithm with Monte Carlo simulations. The objective function minimized the cost subject to design-and reliability-based performance constraints.…”