Separation of succinic acid from fermentation broths. modelling and optimization

Elena-Niculina Dragoi1, Silvia Curteanu1, Dan Cascaval1, Anca Irina Galaction2

1 Gheorghe Asachi Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, 73 Prof. dr. docent Dimitrie Mangeron Str., 700050 Iasi, Romania
2 Gr. T. Popa University of Medicine and Pharmacy, Faculty of Bioengineering, 9-13 Mihail Kogalniceanu Str., 700454 Iasi, Romania

Abstract


Succinic acid is widely used in different industries, its demand increasing each year. Therefore, efficiently producing it (especially from bio-regenerable sources) is an aspect that researchers try to solve thorough different methods, one of the approaches consisting in using process models for generating predictions and improving production by process optimization. In this work, a combination of two bio-inspired algorithms represented by Artificial Neural Networks and Clonal Selection was employed for determining optimal models for the separation of succinic acid from fermentation broths. Since these two algorithms cannot be naturally combined, a direct, real-value encoding for the most important model parameters was employed. In order to improve the performance of the general algorithm, a local hybrid search method based on Random Search and Back-Propagation was introduced into the optimization procedure. The results obtained showed that the algorithm improvements are translated into performance improvements.

Keywords


back-propagation; clonal selection; neural network; random search; succinic acid

Full Text:

 Subscribers Only