Desalination Plant Performance Prediction Model Using Grey Wolf Optimizer Based ANN Approach

Abstract

The present era of advances in desalination plants revolves around the involvement of artificial intelligence techniques in ameliorating their modeling and operational performance. Among the two objectives, an accurate modeling of the plant&#x2019;s behavior may certainly help the design engineers to operate the plant in more stable and controlled operating conditions so as to achieve higher plant efficiency. Furthermore, this helps eliminate the risk to the operator&#x2019;s life and reduces production time, energy, and money. From the literature, it is observed that Artificial Neural Network (ANN) has been the most extensively used approach for modeling and simulation of the desalination plant. However, ANN has the concept of biases and weights updation for better prediction and accuracy of the predicted model, but the conventional methods do not yield desirable results. So, the updation of biases and weights using optimization algorithms is preferred in the literature. Therefore, this paper presents the Grey Wolf Optimizer based ANN (GWO-ANN) approach for desirable prediction and accuracy of models. Further, six models (GWO-ANN Model-1 to Model-6) are proposed to more accurately predict the Reverse Osmosis (RO) desalination plant&#x2019;s performance. For this investigation, we have considered four experimental inputs (feed water salt concentration, condenser inlet temperatures, evaporator inlet temperatures, and feed flow rate) and one output (permeate flux). The simulation results predict output performance in quite proximity to the experimental datasets. The simulated hybrid GWO-ANN models (best of best results of GWO-ANN Model-2: <inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=98.9$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.007) show superior results than the reported results from the existing Response Surface Methodology (RSM) (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=98.5$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.100) and ANN models (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =98.8$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.060) and other PSO-ANN Model (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=96.3$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.025) and GA-ANN model (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =98.7$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.008). This research exemplifies the involvement of superior nature-inspired intelligent techniques in this modern era to further enhance the savings in production time, energy, and investment of desalination plants.