Modeling chemical process systems via neural computation

Abstract

The use of neural nets for modeling nonlinear chemical systems is discussed. Three cases are considered: a steady-state reactor, a dynamic pH stirred tank system, and interpretation of biosensor data. In all cases, a back-propagation net is used successfully to model the system. One advantage of neural nets is that they are inherently parallel and, as a result, can solve problems much faster than a serial digit computer. Furthermore, neural nets have the ability to learn. Rather than programming neural computers, one presents them with a series of examples, and from these examples the nets learn the governing relationships involved in the training database.