Improvised grey wolf optimizer assisted artificial neural network (IGWO-ANN) predictive models to accurately predict the permeate flux of desalination plants

Effective planning, management, and control of industrial plants and processes have exploded in popularity to enhance global sustainability in recent decades. In this arena, computational predictive models have significantly contributed to plant performance optimization. In this regard, this research proposes an Improvised Grey Wolf Optimizer (IGWO) aided Artificial Neural Network (ANN) predictive model (IGWO-ANN Model-1 to 4) to predict the performance (permeate flux) of desalination plants accurately. For this, the proposed models investigated experimental inputs four: salt concentration & feed flow rate, condenser & evaporator inlet temperatures of the plant. Besides, mean squared error (MSE) and the regression coefficients (R²) have been used to assess the models' accuracy. The proposed IGWO-ANN Model-4 shows strong optimization abilities and provides better R² = 99.3 % with minimum errors (0.004) compared to existing Response Surface Methodology (RSM) (R² = 98.5 %, error = 0.100), ANN (R² = 98.8 %, error = 0.060), GWO-ANN (R² = 98.8 % error = 0.008), models. The proposed models are multitasking, multilayers, and multivariable, capable of accurately analyzing the desalination plant's performance, and suitable for other industrial applications. This study yielded a promising outcome and revealed the significant pathways for the researchers to analyze the desalination plant's performance to save time, money, and energy.