A diagnostic procedure for improving the structure of approximated kinetic models. (2nd February 2020)

• Record Type: Journal Article
• Authors: Quaglio, Marco; Fraga, Eric S.; Galvanin, Federico
• Is Part Of: Computers & chemical engineering. Volume 133(2020)
• Publication Date: 2020-02-02
• Subjects: Process-model mismatch -- Diagnosis -- Information -- Lagrange multipliers; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.106659
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: Kinetic models of chemical and biochemical phenomena are frequently built from simplifying assumptions. Whenever a model is falsified by data, its mathematical structure should be modified embracing the available experimental evidence. A framework based on maximum likelihood inference is illustrated in this work for diagnosing model misspecification and improving the structure of approximated models. In the proposed framework, statistical evidence provides a measure to justify a modification of the model structure, namely a reduction of complexity through the removal of irrelevant parameters and/or an increase of complexity through the replacement of relevant parameters with more complex state-dependent expressions. A tailored Lagrange multipliers test is proposed to support the scientist in the improvement of parametric models when an increase in model complexity is required.