Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks

A growing world population, unrelenting urbanization, increasing scarcity of good quality water resources, and rising fertilizer applications are the driving forces behind the accelerating upward trend in the use of efficient methods of water and wastewater treatment such as biological processes. Due to the complexity of the reactions in biological processes, a few studies have been performed involving the modeling of biological removal of water pollutants. Thus, the application of the artificial neural networks (ANNs) to predict the performance of the biological systems has been attempted. ANNs are computer‐based systems that are designed to simulate the learning process of neurons in the human brain. One of the characteristics of modeling based on ANNs is that it does not require the mathematical description of the phenomena involved in the process. This review article describes the application of ANNs for modeling of biological water and wastewater treatment processes. Examples of early applications of ANNs in modeling and simulation of biological water and wastewater treatment processes in the presence of various microalgae, macroalgae, bacteria, microbes, yeasts, anaerobic sludge, aerated submerged biofilms, and submerged membrane bioreactors are reviewed. This review reveals that ANN modeling requires no additional information on the mechanism and the kinetics of biodegradation of treated contaminants and time‐consuming kinetic investigations can therefore be avoided. ANNs might be useful in process modeling, optimization, as well as in the design, scale‐up, and industrial application of water and wastewater biological treatment processes.