Performance optimization of the humidification–dehumidification desalination process using mathematical programming

In this paper the humidification–dehumidification desalination process is studied and its performance optimized using mathematical programming. An advantage of this method is consideration of the simultaneous effect of various parameters on process performance. An NLP system model is solved for three objective functions: minimization of specific thermal energy consumption, maximization of productivity and maximization of condenser heat recovery. The solutions have been improved especially from a productivity point of view in comparison with previous studies. The productivity objective function leads to the best solution if there is no limitation for the humidifier inlet water temperature. Otherwise, the specific energy objective function seems to be better than others. In the next step the effect of important parameters on optimum operation point of the HD process is analyzed and related variation curves are presented. Results reveal that the water to air mass flow rate ratio (L/G) is the most effective parameter and has an optimum value. Also the humidifier inlet water temperature will be an important parameter. The upper temperature results in more productivity and a smaller heat transfer area. But minimum specific thermal energy consumption of the system occurs at an optimum temperature. Finally, the analysis of the dehumidifier inlet water temperature shows that if the humidifier inlet water temperature is high, the specific thermal energy consumption can be decreased more through recycling of the outlet humidifier water into the dehumidifier.