We designed a new process of hydrogen production called 'Purple Hydrogen', that utilizes bacteria to produce hydrogen from carbon dioxide and hydrogen sulfide. More specifically, this works by using a bioreactor to precisely control the environment that anaerobic purple sulfur bacteria grown in, to induce a form of anaerobic photosynthesis within the bacteria that produces hydrogen as a byproduct of the hydrogen sulfide.
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Current methods of hydrogen production are extremely detrimental to the environment, releasing amounts of carbon comparable to refining oil or burning coal. With demand for hydrogen about to grow even larger, being implemented in hydrogen fuel cells and hydrogen cars, there needs to be a clean and efficient way to produce the fuel, and additives, to get any of the benefits of using the hydrogen in the first place. ‘New solutions’ to the problem, like ‘blue hydrogen’, are either too expensive to be implemented or rely on unattainable technologies to work, but are still being trusted upon to match the growing need for hydrogen. As producing hydrogen is summarized as separating the hydrogen from a covalent bond to harvest the pure diatomic hydrogen gas resulting, we can look towards the ancient anaerobic microorganisms that are evolutionarily optimized to break hydrogen bonds for survival, purple sulfur bacteria. Purple sulfur bacteria are known to be able to revert back to breaking high-concentration hydrogen bonds, not unlike the conditions they had to survive with before modern day atmospheric oxygen, when given the right conditions. Advancements in artificial intelligence (AI) and bioreactor systems, like Pfennig’s Medium, allow for the environmental conditions of purple sulfur bacteria to be regulated accurately enough to maximize an output of pure hydrogen gas for use comparable to blue hydrogen production, as simulated by Purple Hydrogen Co.
Our Bioreactor Design
Read our whitepaper on the solution:
We built a simulator that can simulate the stoichiometry behind the bacteria's metabolic processes that produce hydrogen, to train an AI off of that can run the bioreactor at peak efficiency. Not only can this produce the most amount of hydrogen possible, from wasted hydrogen sulfide gas, but it can also do it pretty cheaply according to our AI's efficiency, lowering the cost of a kg of hydrogen to about the same price as burning oil. We used python for the computing, and AI of the solution, and then we used pygame to make an interface control board of the simulation, that could theoretically also be used to run the bioreactor:
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