Nonylphenol biodegradation kinetics estimation using neural networks

Many man made chemical substances are coming under the focus for environmental abuse and their impact on wild life and humans. A widely used alkylphenolethoxylates (APEs) surfactant was recently banned in Europe because scientists discovered that APE breakdown products are estrogenic and highly toxic to aquatic organisms. Nonylphenol is one such substance that has come under the focus as an environmental pollutant. However, sufficient information is not there to study the kinetic behavior of this toxic surfactant. The biodegradation process of nonylphenol is best described by Monod´s model which is based on a coupled system of nonlinear differential equations. This model is based on set of kinetic parameters. It is very difficult to measure the actual biodegradation process of nonylphenol because of the unknown nature of the parameters involved and expense in measuring the states. The estimation of kinetic parameters of nonylphenol biodegradation is done by using a gradient optimization neural network estimator