Predicting H2 reservoirs for sustainable energy

Inspiration

Fossil fuel combustion for energy has been found to be 73% of total U.S. greenhouse gases emissions, as well as for 92% of total U.S. anthropogenic CO2 emissions. This large degree of fossil fuel emissions means that climate change, global warming, reduced air quality, and – as an all-encompassing term – increased air pollution, are largely due to fossil fuels.

Very recently, there has been a large increase in scientific interest in natural hydrogen in the Earth. Similar to how natural gas or methane are found in reservoirs inside the Earth, H2 can surprisingly also form pockets underground due to geochemical processes, which resulted in H2 being found at Mali and Athabasca.

Hydrogen fuel cells are one of the most promising sources of energy in the future. They can be extremely efficient energy sources, and upon combustion their only product is water, thus making them extremely clean fuels with no pollution. Currently, however, hydrogen fuel cells are only run by using man-made hydrogen, either through electrolysis, or some other chemical process, which are usually energy inefficient and do not break even in terms of the energy put in compared to the energy released.

Our project aims to utilize the untapped potential of natural H2 by predicting where its reservoirs could be found inside the Earth. The hydrogen gas found as a result of our program can then be used in hydrogen fuel cells to revolutionize the extent of use of sustainable and clean energy.

What it does

Based on our analysis of the geochemical processes leading to the formation of H2 reservoirs underground, the main minerals required are clay-based kaolinite, illite, sudolite, and carbon monoxide concentration in the soil. Carbon monoxide net flux is quite difficult to compute due to factors that are constantly changing, like rainfall and root permeation, therefore we chose to ignore this factor for now. Our project thus compares the mineral distributions specifically for kaolinite, illite and sudolite to find the areas of highest concentration of all three minerals, as well as regions that neighbor areas of high concentration. Based on the relative concentration of the required minerals, the algorithm assigns each region a relative probability of finding an H2 reservoir.

The final product of the project is an interactive map that depicts each 1ºx1º area of the world with a relative probability (shown by specific colors) of finding natural H2 reservoirs.

How we built it

First we performed web scraping (from the website MinDat) to find the regions where the minerals sudolite, kaolinite and illite could be found across the globe. We specifically used the coordinates of each region (latitude and longitude). We then used the Leaflet JavaScript library to plot the data of regions with mineral content on a map. We then found the number of regions in each singular 1ºx1º area of the world map, and assigned weights for each mineral based on how important research suggested it was for H2 production in the Earth. Then, based on the combined weighted concentration of the minerals in each area, we assigned it a relative probability of containing H2 reservoirs. Then, we displayed the final world map indicating the relative probabilities with a gradient of colors.

Challenges we ran into

1. The data for the mineral distributions was very hard to find, as there was no direct data source that we could use. We had to instead scrape the data from a very vast website.
2. Once we scraped the data, we had to make sure it agreed with the data in the published papers of where the H2 reservoirs were actually found. While we found that the data was mostly reflected, we believe the data may have been biased towards North American sources, as the correspondence with Mali was weaker than expected. Thus, it was a challenge to make sure our data was accurate (within our limits) and was reflecting the empirical research that had already been published.
3. It was also a challenge to determine the weights to be assigned to each mineral when calculating the probability distributions. Since our time was limited, our scope for research was also limited, and therefore we chose our weights somewhat arbitrarily.
4. It was a challenge to find which specific minerals could contribute to H2 production, since there are so many environmental conditions that can affect the process globally. We therefore had to make some approximations in our model and adjust for parameters that we could not predict, such as the CO levels.

Accomplishments that we're proud of

1. We were able to understand the rudimentary processes and parameters that affected natural H2 production despite our very limited knowledge in geology and geochemistry, and were able to use these parameters to approximately predict results that corresponded to empirical research.
   
2. We made use of effective resources in the time given to process data and present it clearly to the audience.

What we learned

Firstly, we learned a lot about the actual science behind hydrogen fuel cells and natural H2 reservoirs. This was in conjunction with learning about geological processes in the Earth, and the chemistry that goes on with the minerals. Secondly, we learned how to use the Leaflet webmap library to create interactive webmaps displaying our data. Thirdly, we learned how to create algorithms that assign weights based on prior information, which can sometimes be arbitrary.

What's next for Predicting H2 reservoirs for sustainable energy

With more time and access to research, we could come up with more accurate weightings for each mineral, and also come up with more parameters that would help determine potential H2 reservoirs more accurately. Since mining for H2 can be an expensive process, having greater accuracy for a prediction would help in efficiency in terms of time and economy. As an extension, we could also consider improvements to hydrogen fuel cells to make them more efficient and thus extract more energy from its combustion.

Built With

• css
• html
• javascript
• python