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    Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning
    (Elsevier Sci Ltd, 2022) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, biomass and lipid productivities of Yarrowia lipolytica were analyzed using machine learning techniques. A dataset containing 356 instances was constructed from the experimental results reported in 22 publications. The dataset was analyzed using decision trees to identify the features (descriptors) that lead to high biomass production, lipid content and lipid production. C/N ratio and fermentation time were found to be the most influential features for biomass production while the use of glucose and medium pH seemed to be more important for high lipid content. For the lipid production case, five generalizable paths leading to high values of this output were identified. One of those paths required pH to be < 6.3, high glucose and (NH4)(2)SO4 concentrations, lower concentration for yeast extract and the yeast strain not be H-222. Another one needed a pH greater than 6.3, a C/N ratio smaller than 75, a time greater than 14 h, and a strain other than W29. The same dataset was also explored deeper using association rule mining to determine the effects of individual features on output variables. It was then concluded that machine learning methods are very useful in determining the optimal conditions of biomass growth and lipid yield for Yarrowia lipolytica to produce renewable biofuels.
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    Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
    (Pergamon-Elsevier Science Ltd, 2021) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, the algal biomass productivity and its lipid content were explored using a database containing 4670 instances extracted from the experimental results reported in 102 published articles. First, the influences of critical factors such as microalgae species, cultivation conditions, light intensity, CO2 amount, nutrient concentrations, reactor type, stress conditions, cell disruption methods, and lipid extraction solvents on the biomass and lipid production were reviewed. Then, the database was analyzed using machine learning techniques; decision trees were utilized to determine the combination of variables leading to high biomass and lipid content while association rule mining was used to find the specific conditions leading to very high biomass and lipid levels. Decision tree analysis discovered 11 different combinations of variables leading to high biomass productivity and 13 combinations for high lipid content; whereas, association rule mining analysis helped to identify the levels of specific factors for very high biomass and lipid production. It was then concluded that machine learning methods can help to determine the best conditions for optimum biomass growth and lipid yield for microalgae to manufacture renewable biofuels, and this can guide the planning of new experimental works. (C) 2020 Elsevier Ltd. All rights reserved.
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    Machine learning for algal biofuels: a critical review and perspective for the future
    (Royal Soc Chemistry, 2023) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, machine learning (ML) applications in microalgal biofuel production are reviewed. First, the basic steps of algal biofuel production are summarized followed by a bibliometric analysis to demonstrate the major research trends in the field. Also, the major challenges related to the commercialization of technology are identified. Then, ML applications for various steps in the value chain are reviewed and analyzed systematically. Finally, a future perspective on the contribution of ML in the field is provided. Our analysis indicates that ML applications should focus on screening and selecting suitable strains, preferably together with some other value-added products, requiring close collaborations among the researchers in the field to construct an extensive microalgal strain database. Optimization of cultivation conditions appears to be another area where ML can be helpful. Although most published ML works on cultivation are not usually suitable to extract generalizable knowledge (due to the nonstandard nature of strains, wastewater, and irradiation), standard testing and methodologies related to reporting protocols should also be built through collaboration to build comparable and generalizable ML models.