The control of MSF desalination plants based on inverse model control by neural network

• Published in: Desalination 2014-01, Vol.333 (1), p.92-100
• Main Authors: Tayyebi, Shokoufe, Alishiri, Maryam
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Desalination; Drinking water and swimming-pool water. Desalination; Exact sciences and technology; Inverse; Inverse model control; Mathematical models; MSF desalination; Neural network; Neural networks; Nonlinearity; Pollution; Salt water; Strategy; Water treatment and pollution
• ISSN: 0011-9164; 1873-4464
• DOI: 10.1016/j.desal.2013.11.022

In this paper, a nonlinear inverse model control strategy based on neural network is proposed for MSF desalination plant. Artificial neural networks (ANNs) can handle complex and nonlinear process relationships, and are robust to noisy data. The designed neural networks consist of three layers identified from input–output data and trained with a descent gradient algorithm. The set point tracking performance of the proposed method was studied when the disturbance is present in the MSF system. Three controllers are designed for controlling the top brine temperature, the level of last stage and salinity. These results show that a neural network inverse model control strategy (NNINVMC) is robust and highly promising to be implemented in such nonlinear systems. Also the comparison between the top brine temperature of the proposed model and NN predicted data from the literature supports the accuracy of the model. •A nonlinear inverse model controller based on neural network is proposed for MSF plant.•Three controllers based on the proposed strategy are designed.•The proposed strategy is a powerful and robust promising tool for MSF plant.•The proposed model is validated with predicted data cited in the literature.