Open AccessInvestigation of the Efficiency of Small-Scale NF/RO Seawater Desalination by Using Artificial Neural Network Modeling
Author(s) -
A. Adda,
Salah Bezari,
Mohamed Salmi,
Giulio Lorenzini,
Maamar Laidi,
Salah Hanini,
Rachid Maouedj,
Younes Menni,
Houari Ameur,
Hijaz Ahmad
Publication year - 2021
Publication title -
international journal of design and nature and ecodynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 13
eISSN - 1755-7445
pISSN - 1755-7437
DOI - 10.18280/ijdne.160307
Subject(s) - desalination , seawater , artificial neural network , volumetric flow rate , linear regression , permeation , environmental engineering , environmental science , mathematics , engineering , artificial intelligence , statistics , computer science , chemistry , geology , membrane , thermodynamics , biochemistry , oceanography , physics
An attempt is conducted in this paper to develop an artificial neural network (ANN) model for predicting the efficiency of small-scale NF/RO seawater desalination, then applied to the simulation of permeate flow rate and water recovery. A feed-forward back-propagation neural network with the Levenberg-Marquardt learning algorithm is considered. The performance of ANN compared to the multiple linear regression (MLR) is based on the calculated value of the coefficient of determination (R2). For ANN, R2 permeate flow rate was 0.997, and R2 permeate water recovery was 0.999, and for MLR, R2 permeate flow rate was 0.508, and R2 permeate water recovery was 0.713. It was observed that ANN performed better than the MLR.