Application of connectionist model to controlling a MSF desalination plant

This paper presents a novel neural network (NN) approach to the problem of control and modelling of nonlinear dynamic process through the manipulation of collected input-output data from multi-flash desalination (MSFD) process, using four multilayers feedforward NN´s with the backpropagation learning rule to learn and code the nonlinear and complex mapping. This is implemented by allowing the mapping of the NN to generate on intermediate function V(t) from both the collected inputs and outputs of the plant. V(t) is then used to generate the control input signals of the plant by another mapping process. V(t) is a function representing the time state space. Different cases for V(t) are considered and the results are reported