Iterative modeling and optimization of biomass production using experimental feedback. (2nd September 2017)

• Record Type: Journal Article
• Authors: Luna, Martin F.; Martínez, Ernesto C.
• Is Part Of: Computers & chemical engineering. Volume 104(2017)
• Page Start: 151
• Page End: 163
• Publication Date: 2017-09-02
• Subjects: Model-based optimization -- Experimental design -- Biomass production -- Fed-batch reactor -- Baker's yeast; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.04.020
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Advantageous integration of first-principles models with data from designed experiments. A novel objective function describing an economic tradeoff between high biomass production and low glucose consumption. Experimental feedback is key to iteratively correct modeling errors in an iterative scheme. Iterative modeling and optimization is experimentally tested in a bench scale fed-batch reactor. Experimental results demonstrate the efficacy of iterative optimization with simple models. Abstract: Models of cultures of microorganisms are widely used for analysis, control and optimization of bioreactors in order to enhance productivity and performance. Typically, model-based optimization approaches may have acceptable convergence rates to a local optimum, but they are negatively affected by modeling errors when extrapolating to unknown operating conditions. In this work, a model-based optimization methodology that uses experimental feedback is applied to a fed-batch bioreactor. Experimental feedback is used to solve the extrapolation problem. After the model has been (re)parameterized, an optimized experiment is designed to maximize the performance of the bioprocess. Data gathered in this experiment is used to correct the model, and the cycle continues until no further improvement is found. The method is tested in the production of baker's yeast biomass. Results obtained demonstrate the capability of the proposed approach to find an improved feeding profile that leads to better performance with minimum experimental effort.