Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms

In the electrodialysis process, separation percent (SP) had nonlinear relationships with a number of influencing factors (feed concentration (C), flow rate of dilute compartment (Q), reaction temperature (T) and applied voltage (V)), and the relationships were hard to express by a simple formula. And four influencing factors had remarkable effects on SP. In this paper, the four factors were studied in the electrodialysis experiments. Back propagation (BP) neural networks and improved BP algorithms were applied on the prediction of SP, and their prediction capabilities could reflect generalization and adaptive abilities on complex data which had nonlinear relationships with each other. And with different structures of neural networks, transfer functions of neurons and learning rates, the optimum training parameters were obtained. Comparing BP neural networks with improved BP algorithms, improved BP algorithms were better than BP algorithm, due to changing with increasing ratios of learning rates and weights properly. And in the condition of high temperatures and voltages, the improved BP algorithms were predicted to have better performance, this was because improved BP algorithms had the generalization ability for high values.

► The manuscript is to study BP neural networks and improved BP algorithm.
► The manuscript is a good reference for membrane and desalination technologies.
► The manuscript is a detailed description of the methods and their applications.
► It is meaningful to deal with nonlinear relationships between inputs and outputs.