Intelligent modelling of a batch reactor with partially unmeasurable states based upon a structure approaching hybrid neural networks

Abstract

The success of model-based control of chemical processes is dependent on good process models. For processes that are poorly known, the generic modelling capability of neural networks offers an attractive alternative. However, for satisfactory performance, the conventional implementations of neural networks require large sets of offline data in addition to online measurement of key variables, such as concentrations. Meeting each of these requirements is often infeasible in chemical processes. By combining the structural information from a first-principles model and the virtual supervisor-artificial immune algorithm, a novel hybrid neural network, called a structure approaching hybrid neural networks (SAHNN), is proposed. The proposed approach solves the structural problem of neural models and requires a more manageable number of offline data and online key variables. The accurate prediction of online partially unmeasurable concentrations in a batch reactor demonstrated that SAHNN is a promising tool to model complicated batch processes and can be utilized as a vehicle for the control and optimization of other similar chemical reactors.