ARTIFICIAL NEURAL NETWORK BASED MODELING OF THE FOULING PHENOMENA IN MEMBRANE BASED BIOREACTORS

Abstract

Membrane-based bioreactors represent a pivotal technology in various biological processes, offering unique advantages such as selective product removal and continuous nutrient replenishment, which are conducive to optimizing reaction efficiency and sustainability. However, the pervasive issue of membrane fouling presents a significant obstacle to the efficacy of these systems. Fouling, characterized by the accumulation of particulates and microorganisms that obstruct membrane pores, leads to decreased flux rates and increased operational costs. Traditional approaches to modeling membrane fouling in bioreactors often fall short due to the complex and nonlinear nature of the phenomenon. In response to this challenge, this study employs an Artificial Neural Network (ANN) approach, leveraging its ability to capture intricate relationships and nonlinearities within the fouling process. ANNs offer a data driven framework that can learn and adapt from experimental data, making them well suited for modeling the dynamic behavior of fouling in membrane-based bioreactors. The ANN model developed in this study is trained and validated using experimental data sourced from literature, ensuring its accuracy and reliability in capturing the underlying fouling mechanisms. Through meticulous optimization of the ANN architecture, including the determination of an optimal number of neurons in the hidden layer, the model achieves minimal error and demonstrates robust performance in predicting experimental outcomes. Optimization reveals that an ANN with seven neurons in the hidden layer yields the minimum error, with validation demonstrating relative errors of less than 10% between theoretical and experimental results for all data points. Subsequently, the trained ANN serves as a powerful tool for exploring the effects of various operational parameters, such as flux, backwashing duration, and interval of relaxation, on membrane fouling dynamics. These findings offer valuable insights into optimizing membrane-based bioreactor performance and suggest avenues for future research in this field. Specifically, there is a need for the development of more sophisticated modeling techniques and the exploration of novel membrane recovery strategies to further enhance the efficacy and sustainability of membrane based bioreactors across diverse biological applications.