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This dissertation proposes mathematical modeling and novel cost-optimal control methods for Convection-Enhanced Evaporation (CEE) systems. CEE is the approach of evaporating water from saline films (brine) on packed evaporation surfaces by air convection, and actively controlling the operation variables to minimize the process cost. The developed approach represents a modular, cost-effective solution for brine management in decentralized and/or small scale desalination plants and industrial processes which currently lack safe and effective brine management options. Forced convection across packed, wetted evaporation surfaces is induced either by means of a fan, natural wind, or a combination of both (hybrid approach). As air flows over the liquid films, the difference in vapor pressure between the air and liquid surfaces induces evaporation. The work contains three major parts. The first part develops a generalized mathematical model of CEE systems to simulate the heat and mass transfer associated with convection-driven evaporation of saline films. The model is derived from the fundamental conservation equations of mass and energy, solved numerically using the finite difference method to predict the evaporation rate and the spatial distribution of humidity, temperature and salinity along the evaporation surfaces based on ambient condition, liquid (brine) inlet condition, and design configuration. The model-predicted performance is in good agreement with experimental pilot CEE system performance and with values published in the literature. The developed model is used to explore and compare the performance of three design aspects: (1) the liquid-air flow configuration (cross-flow vs parallel-flow), (2) the alignment and wetting of the surfaces (vertically aligned with double-sided wet surfaces vs horizontally aligned with single-sided wet surfaces), and (3) hybrid wind-fan operation, a novel operation model aimed at reducing the electrical energy demand of the fan by harnessing the natural drying power of the wind. The second part of this dissertation focuses on cost optimization. It proposes a method for formulating objective functions using cost ratios to generalize the optimization results to applications with varying material and energy prices and scenarios. The problem of identifying the cost-optimal operating settings was then solved as a multi-objective optimization using the genetic algorithm. The optimization revealed and characterized two distinct operation modes: "all-electric" mode, and "heating" mode. Finally, the last part of this dissertation proposes a data-driven optimal control method. The controller is based on a large dataset consisting of Pareto fronts, obtained in advance by solving a set of optimization problems. The method allows three optimal operation strategies: (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 weathers. The results showed significant costsaving potential relative to static operation. The presented control strategies enable CEE to adjust its operation under various weather conditions. The models and methods developed in this dissertation are conducive to study and control of other configurations of CEE systems. They have the potential to be applied to other desalination and renewable energy systems, particularly those involving a trade-off between thermal and electric energy demand.