(758c) Kinetic Modelling of Microalgae Growth for the Optimization of Starch and Lipid Production

Gonzalo M. Figueroa-Torres ^a,b, Jon Pittman^c and Constantinos Theodoropoulos^a,b

^aSchool of Chemical Engineering and Analytical Science, University of Manchester, M13 9PL, UK

^bCentre for Process Integration, University of Manchester, M13 9PL, UK

^cFaculty of Life Sciences, University of Manchester, M13 9PL, UK

Microalgal biomass has been regarded as a sustainable and renewable feedstock for biofuel production due to its ability to naturally accumulate carbohydrates - mainly in the form of starch â?? and lipids. These carbon-based compounds can potentially be used as raw substrates for biofuel production (Brennan & Owende, 2010). Studies have shown that carbohydrate and lipid content in microalgae cells is positively influenced under conditions of nutrient limitation. However, this increase in starch and lipid content is coupled with a decrease in biomass production (Markou, Angelidaki, & Georgakakis, 2012), an adverse effect on the overall cultivation process. An efficient approach towards improving starch and lipid production by means of a cultivation strategy involves the use of kinetic models capable of predicting the main compositional elements of microalgae growth (Bernard, 2011). However, there have been very limited modelling efforts in the literature regarding the simultaneous production of starch and lipids by microalgal biomass. Thus, the aim of this work is to develop a multi-parameter kinetic model for the optimization of starch and lipid production. The proposed model considers a set of intracellular carbon flows between two main cellular compartments: a pool of active biomass and a storage pool comprising of starch and lipids. Model parameters were fitted against experimental datasets generated from lab-scale cultivation of Chlamydomonas reinhardtii CCAP 11/32C in standard Tris-Acetate-Phosphate (TAP) media under different concentration regimes. Fitting was carried out through an in-house developed optimization algorithm. Validation of the model was then carried out against different experimental datasets. The model can be used to predict three carbon-based cellular pools â?? starch, lipids, and active biomass â?? as well as nutrient consumption and pH evolution, useful factors for the establishment of optimal cultivation conditions.

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