Achieving water quality system reliability using genetic algorithms

This paper presents an efficient approach far obtaining wasteload allocatio n solutions that provide the optimal trade-off between treatment cost and r eliability. This approach links a genetic algorithm (GA) with the first-ord er reliability method (FORM) for estimating the probability of system failu re under a given wasteload allocation. The GA-FORM optimization approach is demonstrated for the case study of managing water quality in the Willamett e River in Oregon. The objective function minimizes the sum of the treatmen t cost and the penalty associated with breaching a reliability target for m eeting a water quality standard. The random variables used to generate the reliability estimates include streamflow, temperature, and reaeration coeff icient values. The results obtained indicate that the GA-FORM approach is n early as accurate as the approach that links the GA with Monte Carlo simula tion and is far more efficient. The trade-off between total treatment cost and reliability becomes more pronounced at higher water quality standards a nd is most sensitive to the uncertainty in the reaeration coefficient. The sensitivity to the reaeration coefficient also increases at increased relia bility levels.