Double and integer multi-objective optimization of solar HDH desalination system with bubble column dehumidifier: An artificial neural network approach

Humidification-dehumidification (HDH) systems are thermal desalination methods well-suited for small-to-medium capacity applications and particularly beneficial for remote areas. In this method, the presence of a significant amount of non-condensable gases circulating in the cycle creates substantial heat and mass transfer resistance, posing a challenge for the system. Bubble column dehumidifiers have been introduced to address this issue. This paper presents a numerical study on the multi-objective optimization of a solar-powered HDH system featuring a multi-stage bubble column dehumidifier. The objective functions are selected from a range of performance parameters to demonstrate the thermal and economic efficiency of the system. The gain output ratio (GOR) is selected to indicate the thermal performance, while the unit cost of water produced (UCWP) is chosen to reflect the economic performance. A neural network model is utilized to predict how the objective functions vary with six decision variables. The chosen optimization approach utilizes the genetic algorithm (GA) method. From a single-objective optimization perspective, it is possible to achieve a GOR of 4.56 and a UCWP of 5.99 $ m 3 . However, maximizing the GOR and minimizing the UCWP may not positively impact the mutual parameter. The optimal solution yields a GOR of 1.77 with a UCWP of 7.7 $ m 3 .