Data-driven Model Construction

Author:

Zhang Zhaoyan1,Zhang Dongda2,del Rio Chanona Ehecatl Antonio3

Affiliation:

1. aDepartment of Chemistry, Imperial College London, UK

2. bDepartment of Chemical Engineering, The University of Manchester, UK

3. cDepartment of Chemical Engineering, Imperial College London, UK

Abstract

The amount of data generated by modern reaction engineering systems has increased exponentially. A natural progression is to apply the data to gain information about the process, be it for scale-up, control, or optimisation. Data-driven models enable this acquisition of knowledge by transforming data into predictions. These predictions are increasingly important as systems become complex beyond human comprehension and logical reasoning fails. Data-driven models come in two main forms: parametric models and non-parametric models. Parametric models contain parameters similar to kinetic models. While a modeller carefully considers the placement of each parameter and its meaning in their kinetic model, a parametric data-driven model often bears no relation to the underlying system. In deliberately defining such a general parameterisation, the model gives data a chance to ‘speak for itself’ without the inductive bias of a human modeller. Non-parametric models directly apply the data without using parameters to make predictions. In this chapter, key parametric and non-parametric data-driven models for reaction engineering will be introduced. Examples will be given of many popular use cases, and the benefits of each method will be described.

Publisher

Royal Society of Chemistry

Reference46 articles.

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