Water Cycle Algorithm for Modelling of Fermentation Processes

Olympia Roeva,Maria Angelova,Dafina Zoteva,Tania Pencheva

doi:10.3390/pr8080920

Abstract

The water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly applied WCA outperforms GA with regard to the model accuracy.

Highlights

When studying complex systems such as biological ones, the interactions and behaviour of their components, i.e., molecules, cells, organs, and organisms, are explored in order to achieve their better understanding
Case study 1: Parameters identification of an E. coli fed-batch fermentation processes (FP) model, presented by Equations (1)–(3) with an optimisation criterion expressed by Equation (8); and
Case study 2: Parameters identification of a S. cerevisiae fed-batch FP model, presented by Equations (4)–(7) with an optimisation criterion expressed by Equation (8)

Summary

Introduction

When studying complex systems such as biological ones, the interactions and behaviour of their components, i.e., molecules, cells, organs, and organisms, are explored in order to achieve their better understanding. Examples of biological systems on a macro scale are populations of organisms. Among the most widely used microorganisms are bacteria and yeast due to their significant industrial and economic value. They both are model organisms in genetics, used as synthetic pathways for antibiotics and other biomolecules of interest. As the most extensively studied model organisms, Escherichia coli is chosen here as a representative of the bacteria and Saccharomyces cerevisiae—of the yeast. The scientific efforts are directed to a deeper understanding of cellular growth control mechanisms in order to overcome several technological barriers and to achieve efficient and cost-effective bioprocesses [2]

Methods

Results

Conclusion