Evolutionary design of dynamic neural networks for evaporator control

Abstract

This paper presents a novel neural network (NN) to control an ammonia refrigerant evaporator. Inspired by the latest findings on the biological neuron, a dynamic synaptic unit (DSU) is proposed to enhance the information processing capacity of artificial neurons. Treating the dynamic synaptic activity after the nonlinear somatic activity helps to capture the dynamics demarcated by the Gaussian activation pertaining to the input space. This practice leads to a remarkable reduction in curse of dimensionality. The proposed NN architecture has been compared with two other conventional architectures; one with dynamic neural units (DNUs) and the other with nonlinear static functions as perceptrons. The objective is to control evaporator heat flow rate and secondary fluid outlet temperature while keeping the degree of refrigerant superheat in the range 4–7 K at the evaporator outlet by manipulating refrigerant and evaporator secondary fluid flow rates. The drawbacks of conventional approaches to this problem are discussed, and how the novel method can overcome them are presented. An evolutionary approach is adopted to optimize the parameters of the NN controllers. Then evaporator process model is accomplished as a combination of governing equations and a sub NN resulting in a simple and sufficiently accurate model. The effectiveness of the proposed dynamic NN controller for the evaporator system model is validated using experimental data from the ammonia refrigeration plant.