Optimal groundwater remediation design by chance-constrained programming

Groundwater remediation optimization models for pump-and-treat technology were developed using a statistical methodology, Chance-Constrained Programming (CCP). The method accounted for uncertainty in the coefficients of the models. Several models were formulated that depended on which set of coefficients were considered uncertain. The CCP method transformed the probabilistic constraints to their deterministic equivalents which used less computer memory and storage locations. The resulting models were mixed-integer programming models. Results are presented that demonstrated the application of the models. The results showed that incorporating uncertainty into the groundwater optimization model using CCP could be a practical method for making decisions on well locations, pumping rates, and number of wells in groundwater remediation.