Hybrid ann-mechanistic models for general chemical and biochemical processes

Cristiana Azevedo, Robert Lee, Rui M.C. Portela, Moritz Von Stosch, Rui Oliveira

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Developing a process model is essentially an exercise of translation of existing sources of knowledge into a compact mathematical representation. In some cases, a deep physical understanding exists that is best expressed in the form of a mechanistic model. For other cases, the absence of a comprehensive knowledge base is compensated by measured process data that can be best modeled by empirical methods such as ANNs. For the vast majority of problems both types of knowledge coexist motivating their integration into hybrid mechanistic-ANN models. Many previous studies have shown that the hybrid ANN-mechanistic approach is advantageous in relation to the one or other modeling framework alone. In this chapter, some of the key concepts that support the hybrid ANN-mechanistic modeling are overviewed. Firstly, the main hybrid structures (serial, parallel-competitive, parallel-cooperative and recurrent structures with information feedback) are reviewed. Then the underlying structure-dependent identification methods are covered, with a focus on ANNs training constrained by an existing mechanistic model. Finally, two case studies are presented. The first case study reports a static serial ANN-mechanistic model that describes the burst phenomena in controlled drug release. The second case study presents a serial ANN-mechanistic model for dynamic modeling and optimization of a bioprocess.

Original languageEnglish
Title of host publicationArtificial Neural Networks in Chemical Engineering
PublisherNova Science Publishers
Number of pages28
ISBN (Electronic)9781536118681
ISBN (Print)9781536118445
Publication statusPublished - 2017


  • ANN training
  • Hybrid mechanistic-ANN modeling
  • Hybrid structures
  • Identification methods


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