Artificial neural networks based models approach, applications

Another fitting procedure, based on the application of neural networks has also been developed [235-238]. The artificial neural-network approach has been shown to produce algebraic models that are good approximations of kinetic mechanisms. Simulations based on this model are much faster than the solution of differential equations but slower than the use of an algebraic model obtained from the polynomial approximation. 

The application of hybrid modelling to chemical and biochemical reactors has been exemplified in several works. The most widely adopted hybrid structure is based on the mass balance equations, like in the traditional first principles approach, but the reaction kinetics are modelled with artificial neural networks (ANNs) (Psichogios and Ungar (1992), Schubert et al. (1994), Montague and Morris (1994), Feyo de Azevedo et al. 

Electronic tongue systems for remote environmental monitoring applications have been presented in several applications. A new approach in the chemical sensor field consists in the use of an array of nonspecific sensors coupled with a multivariate calibration tool which may form a node of a sensor network. The proposed arrays were made up of potentiometric sensors based on polymeric membranes, and the subsequent cross-response processing was based on a multilayer artificial neural network model as proposed by Mimendia et al. who described environmental monitoring of ammonium as a pollutant plus alkaline ions at different measuring sites in the states of Mexico and Hidalgo (Mexico), and monitoring of heavy metals (Cu ", Pb ", Zn ", and Cd " ) in open-air waste streams and rivers. 

---