Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

• Published in: Computers & chemical engineering 2012-02, Vol.37, p.125-135
• Main Authors: Brunet, Robert, Guillén-Gosálbez, Gonzalo, Pérez-Correa, J. Ricardo, Caballero, José Antonio, Jiménez, Laureano
• Format: Article
• Language: English
• Subjects: Applied sciences; Bioconversions. Hemisynthesis; Biological and medical sciences; Bioreactors; Biotechnological processes; Biotechnology; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Hybrid simulation-optimization; L-Lysine; Mechanical engineering. Machine design; Methods. Procedures. Technologies; Mixed-integer dynamic optimization; Various methods and equipments
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2011.07.013

► Superstructure optimization of bio-processes considering all their individual steps. ► Development of a combined simulation optimization approach for the design of bioprocesses. ► Application of a reduced-space mixed-integer dynamic optimization (MIDO) algorithm to the design of biotechnological plants. ► Optimization of the production of L-lysine using systematic mathematical programming tools. In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer ® with an external NLP solver implemented in Matlab ®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.