Using machine learning to optimize the parameters of the algae bioreactor for improving hydrogen production yield and algae growth rate

Algae bioreactors have shown great potential as a sustainable and renewable source of hydrogen, a clean energy carrier. However, the efficiency of hydrogen production in these bioreactors heavily depends on various parameters, such as light intensity, temperature, substrate, and pH level. In this study, we propose a machine learning approach to enhance the optimization process of an algae bioreactor with the goal of improving hydrogen production yield. The research methodology involves collecting data from algae bioreactor system, including measurements of hydrogen production yield and corresponding parameter values. This dataset will be used to train and validate the machine learning model. Various algorithms, such as regression and classification techniques, will be employed to analyze the data and identify the most influential parameters. Once the model is trained, it will be used to predict the optimal parameter values for maximizing hydrogen production yield. The outcomes of this research have the potential to significantly contribute to the field of renewable energy production. By optimizing the parameters of algae bioreactors using machine learning, we can enhance the efficiency and viability of hydrogen production, ultimately contributing to a more sustainable and cleaner energy future.