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).
In this work, a hybrid semi-parametric modelling framework implemented using mixed integer linear programming (MILP) is used to extract (coupled) nonlinear ordinary differential equations (ODEs) from process data. Applied to fed-batch (bio) chemical reaction systems, unknown (or partially known) system connectivity and/or reaction kinetics are represented using a multivariate rational function (MRF) superstructure. The MRF’s are embedded within an ODE framework which is used to incorporate known system model characteristics. Using derivative estimation, the ODEs are decoupled and a MILP algorithm is then used to identify appropriate constitutive model terms using sparse regression. Superstructure sparsity is promoted using a L0 – pseudo norm penalty, i.e. the cardinality of the model parameter vector, enabling the simultaneous yet decoupled identification of the parameters and model structure discrimination. Using simulated data, two case studies demonstrate a principled approach to hybrid model development, distilling unknown elements of (bio) chemical model structures from process data.
Author(s): Willis MJ, von Stosch M
Publication type: Article
Publication status: Published
Journal: Computers and Chemical Engineering
Print publication date: 02/09/2017
Online publication date: 17/05/2017
Acceptance date: 10/05/2017
Date deposited: 07/06/2017
ISSN (print): 0098-1354
ISSN (electronic): 1873-4375
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