Development of a knowledge based hybrid neural network (KBHNN) for studying the effect of diafiltration during ultrafiltration of whey

Abstract The membrane surface dynamics is very difficult to predict and can be roughly estimated by the available models but a true depiction is always difficult since the magnitude and direction of driving forces change as a function of time. The present study is an effort to address the issue, so that the combinatorial approach of deterministic and stochastic modelling might present a better understanding of membrane dynamics. The effect of diafiltration has also been incorporated to investigate the effects it has on the membrane. A stochastic model developed by a knowledge based hybrid neural network (KBHNN) was trained using the Levenberg–Marqurt algorithm where the film layer model was used as the deterministic layer, called the first principle model (FPM). Present work employs two different types of KBHNN architecture with an effort to understand the suitability and applicability of the hybrid network in case of predictions for an ultrafiltration (UF) process. In one sort of architecture neural part was in series with the FPM and in the other one it was in parallel with the FPM. The high correlation coefficient (R2) value portrays the correctness and preciseness of the underlining assumptions and establishes the validity of the developed network.