Abstract
In recent years, there has been increasing concern about the treatment of chlorinated compounds released by chemical industries. These pollutants are challenging to handle and therefore special interest has been aroused in the treatment of dichloromethane (DCM). DCM is the most salient chlorinated hydrocarbon sparingly soluble in water and used as an essential industrial solvent that emits high levels of contaminants in to the environment. Therefore a strategy is needed to address this issue by introducing an intelligent processing system that has been developed for the prediction of bioreactor performance, such as an artificial neural network (ANN). In this chapter, the application of an ANN is presented to model the bioreactor’s (biotrickling filter and modified rotating biological contactor) performance during the treatment of DCM in terms of the removal efficiency. The results retrieved from this ANN model revealed that it is an efficient data-driven model that can be useful for estimating the safe operating conditions.
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