The design of dynamic experiments (DoDE) and dynamic response surface methodology (DRSM) have been recently applied to accurately model and optimize several types of industrial and pharmaceutical processes. In this work, we apply the above methodologies to the growth of a photosynthetic microorganism, a bioprocess characterized by a high degree of complexity. Compared to conventional bioprocesses involving heterotrophic bacteria, the high adaptability of photosynthetic microorganisms to environmental conditions and the complexity of understanding the effect of light intensity on biomass growth make the development of a thorough knowledge-driven model a difficult task. Based on a predefined experimental design taking into account the effect of light, temperature, and nutrient feeding profiles, we performed a set of dynamic biomass growth experiments, from which we estimated different DRSM models. The best one was then used to predict the behavior of a new set of experiments. We show that through such a model, valuable insights into the process can be gained and that the model is fairly reliable in predicting growth behavior under different experimental conditions.

Experimental Test of the Design of Dynamic Experiments and Dynamic Response Surface Methodologies: Growth of a Photosynthetic Microorganism

Trentin G.;Barbera E.;Bertucco A.;
2022

Abstract

The design of dynamic experiments (DoDE) and dynamic response surface methodology (DRSM) have been recently applied to accurately model and optimize several types of industrial and pharmaceutical processes. In this work, we apply the above methodologies to the growth of a photosynthetic microorganism, a bioprocess characterized by a high degree of complexity. Compared to conventional bioprocesses involving heterotrophic bacteria, the high adaptability of photosynthetic microorganisms to environmental conditions and the complexity of understanding the effect of light intensity on biomass growth make the development of a thorough knowledge-driven model a difficult task. Based on a predefined experimental design taking into account the effect of light, temperature, and nutrient feeding profiles, we performed a set of dynamic biomass growth experiments, from which we estimated different DRSM models. The best one was then used to predict the behavior of a new set of experiments. We show that through such a model, valuable insights into the process can be gained and that the model is fairly reliable in predicting growth behavior under different experimental conditions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3469593
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