Novel data-driven optimal control methods for cost-effective brine treatment

Abstract

This study presents a novel data-driven optimal control method to minimize the cost of Convection-Enhanced Evaporation (CEE) systems under time-varying weather conditions. CEE is the approach of evaporating water from saline films (brine) on packed evaporation surfaces by air convection. Here, operating variables (brine injection rate, brine temperature, and air speed) are actively controlled as a function of current ambient conditions and daily weather forecast. The controller optimizes the process operation variables based on a dataset consisting of Pareto fronts, obtained in advance by solving a set of optimization problems. Three optimal operation strategies are presented: (1) real-time selection of operating variables, (2) predictive scheduled operation, and (3) hybrid wind-fan operation. The effectiveness of the proposed strategies was assessed through two case studies with distinct geographical locations and weather conditions: Alamogordo, New Mexico, and Minneapolis, Minnesota. The results show significant cost-saving potential relative to static operation. Predictive scheduled operation resulted in an annual average operating costs of $0.91/m3 and $6.63/m3 in Alamogordo and Minneapolis, respectively, with the higher costs in Minneapolis a result of the added thermal energy required to prevent freezing in winter.