TY - JOUR
T1 - Optimum ANN Empirical Model of Capacitive Deionization Desalination Unit
AU - El Shahat, Adel
AU - Haddad, Rami
AU - Kalaani, Youakim
AU - Haddad, Rami J.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - ANN emprical model
KW - Capacitive deionisation
KW - CDI
KW - Artificial neural network
KW - ANN
UR - https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/18
UR - https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/85
UR - https://doi.org/10.1504/IJIED.2015.069785
U2 - 10.1504/IJIED.2015.069785
DO - 10.1504/IJIED.2015.069785
M3 - Article
VL - 2
JO - International Journal of Industrial Electronics and Drives
JF - International Journal of Industrial Electronics and Drives
ER -