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Inference of chemical reaction networks using mixed integer linear programming

Lookup NU author(s): Dr Mark Willis, Dr Moritz von Stosch



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


The manual determination of chemical reaction networks (CRN) and reaction rate equations is cumber-some and becomes workload prohibitive for large systems. In this paper, a framework is developed thatallows an almost entirely automated recovery of sets of reactions comprising a CRN using experimentaldata. A global CRN structure is used describing all feasible chemical reactions between chemical species,i.e. a superstructure. Network search within this superstructure using mixed integer linear programming(MILP) is designed to promote sparse connectivity and can integrate known structural properties usinglinear constraints. The identification procedure is successfully demonstrated using simulated noisy datafor linear CRNs comprising two to seven species (modelling networks that can comprise up to forty tworeactions) and for batch operation of the nonlinear Van de Vusse reaction. A further case study using realexperimental data from a biodiesel reaction is also provided.

Publication metadata

Author(s): Willis MJ, vonStosch M

Publication type: Article

Publication status: Published

Journal: Computers and Chemical Engineering

Year: 2016

Volume: 90

Pages: 31-43

Print publication date: 12/07/2016

Online publication date: 13/04/2016

Acceptance date: 12/04/2016

Date deposited: 25/04/2016

ISSN (print): 0098-1354

ISSN (electronic): 1873-4375

Publisher: Elsevier


DOI: 10.1016/j.compchemeng.2016.04.019


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