The determination of chemical reaction networks is generally a limiting step in the transition from chemistry research to process development, requiring considerable time, expertise and intellectual effort. Quantitative information, such as chemical reaction networks, allow the use of modelling and simulation software for the purpose of reactor design, process optimisation, prediction, scenario analysis, scale-up, thermal safety etc. Thus, a systematic procedure to determine chemical reaction networks would reduce the time to market of new products, which is a critical success factor for the fine chemical and pharmaceutical industries.

Thus, the overarching aim of this work is a move towards the automated determination of chemical reaction networks, from batch process data. In this paper it is demonstrated how, in principal, this may be achieved using simple systematic mathematical and statistical analyses of the process data. Initially a global ordinary differential equation (ODE) model structure, capable of representing an entire set of possible chemical reactions, is specified. Mathematical and statistical tests are then used to reduce the ODE model structure to a subset of reactions. Rationalisation procedures that exploit the basic rules of reaction chemistry and a number of mathematical principals, often employed in biological systems theory (BST), are incorporated to ensure consistent chemical reaction networks are obtained. The methods are developed using simulated case studies and, finally, a real experimental case study is used to demonstrate the techniques.

It is concluded that it is possible to successfully determine chemical reaction networks, and their associated rate constants, from batch process data.