The elucidation of chemical reaction networks is a limiting step in the transition from chemistry research to process development, requiring considerable time, expertise and intellectual effort.

We have developed sophisticated mathematical technologies that make it possible to successfully determine chemical reaction networks, and their associated rate constants, from batch process data. Our methods involve the specification of a global ordinary differential equation (ODE) model structure capable of representing an entire set of possible chemical reactions. 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.

Chemical reaction networks allow the use of modelling and simulation software and reaction-engineering principles for the purpose of reactor design, process optimisation, prediction, scenario analysis, scale-up, thermal safety and to explore regions of parameter space not investigated experimentally.

Statistical analysis is also an extremely powerful tool in helping understand (and combat) variability in industrial processes.

Some of the advanced statistical technologies we offer include:

• Design of experiments (DoE)

• Multi-objective process optimisation

• Genetic algorithms

• Univariate and multivariate analysis

• PCA, PLS, factor analysis and on-line SPC

• Neural networks and hybrid modelling
  

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