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http://hdl.handle.net/10609/147942
Title: | Applying Design of Experiments and Machine Learning algorithms to define the consumption envelope of lactic acid |
Author: | Garcia Lopez, Marc |
Tutor: | Fernández Martínez, Daniel |
Abstract: | Because of the climate crisis, the scientific community has been developing and exploring carbon neutral alternatives to fossil fuels. Although it is not as well-known as wind or solar energy, the use of microbial cell factories to produce biofuels and other bioproducts is gaining prominence lately. Nonetheless, on the long term, those processes require an important optimization to make them feasible and economically profitable. Historically, this optimization has been carried out by using suboptimal methods (e.g. COST: change one single variable at a time). Nevertheless, by applying the proper experimental design and by using the adequate computational tools, we can obtain solid results without investing too much time or effort. In this project, we aim to use design of experiments (DoE) and some Machine Learning algorithms to optimize a bioindustrial process for the company Photanol, whose goal is to turn CO2 and sunlight into organic acids by cultivating genetically engineered strains of the photosynthetic cyanobacteria Synechocystis PCC 6803. Without going yet into further detail, our goal is to - through the use of classifiers- identify those combinations of pH, temperature and osmolarity (π) that result in a safe lactate consumption rate of the biological pollutants that inhabit our photobiorreactors (β < 0.1 mM/day). |
Keywords: | design of experiments machine Learning microbial factories biological pollutants artificial neural networks consumption envelope naive bayes random forests support vector machines |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 25-Jan-2023 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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mgarcialopez012345678FMDP0123report.pdf | Report of TFM | 2,46 MB | Adobe PDF | View/Open |
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