Optimum ANN Empirical Model of Capacitive Deionization Desalination Unit

Adel El Shahat, Georgia Southern University
Rami Haddad, Georgia Southern University
Youakim Kalaani, Georgia Southern University


Capacitive deionisation (CDI) has emerged as a robust energy efficient for water desalination. In this paper, a novel CDI electrosorption process is proposed to increase the efficiency based on real experimental data. It is achieved by artificial neural network (ANN) to develop four models. For problem formulation, closed forms mathematical equations were derived, thus, resulting in a very efficient programming algorithm. Optimum patterns ANN models were validated by implementing two ANN units to drive the CDI electrosorption process. This proposed method was tested and verified using actual and predicted ANN values which yielded excellent results with regression factors between 0.99983 to 1. Optimum patterns are validated in the form characteristics comparisons between genetic and original one. The ANN models their algebraic equations are adopted for various characteristics estimation process. They created with suitable numbers of layers and neurons that provided fast and accurate network training.