Detecting Deficiencies

Detecting deficiencies efficiently

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Inspiration

The first thing we did at HackUTD was talk to the employees and sponsors at EOG since they were closest to the entrance of the event. We absolutely loved their charisma and energy. We chose to focus our efforts towards EOG's challenge statement because of how much we enjoyed them personally and the feedback that they were able to provide both in regard to personal advice and our experience at their workshop.

The solution itself is not inspired by any particular existing solution, or people, but rather it is the product of many hours of ideation among our team members.

What it does

This solution aims to improve how efficient it is for a lease operator to detect hydrate formation in gas valves by converting large quantities of data into scatterplots within a graphic web app. This app also functions as an alert system to quickly notify the lease operator to address the issue of severe hydrate formation.

How we built it

Python (pandas, plotly), Figma, React/Tailwind CSS

Firstly, we used the Python library pandas to read all the given .csv data as well as fill in any null (empty) columns or gaps in the data with a linear-regression-predictive function. Then, the library plotly was used to create scatterplots of the data, as well as a line of best fit on each graph. We took screenshots of this data to include in our web app, built with React (structure web)/Tailwind CSS (style web).

Figma was used to continue the prototyping/design of our prospective app, beyond what were able to actually code with React/Tailwind CSS within the timeframe of the hackathon.

Challenges we ran into

We could not figure out how to implement Python into our React code; we resorted to using snapshots of our Python plots instead of any seamless implementation of the plots themselves within our React web app.

Accomplishments that we're proud of

We are proud of being able to draw a significant connection and come to a conclusion about what all the data provided to us meant considering that we initially had minimal understanding of how to process such data.

We are also proud of prioritizing the delivery of a respectable idea, at the very least, if not a full solution, over getting sleep. As first-time hackers, we are glad to have had this experience, problem-solving as a team.

What we learned

Throughout this experience, we learned how to use Figma to prototype, design, and bring our UI ideas to life without having to fully understand the technicalities of coding the UI.

As well as the fact that it is most crucial to our success how we "sell ourselves" when pitching an idea--not fixating on the completion and perfect functionality of a tangible product, such as an app or website.

What's next for HackUTD

We should come next year prepared with some projects under our belts, as well as resumes ready to hand out to the sponsors who are recruiting for internships.

As we leave, we consider studying successful projects and learning from other hackers' projects to see what qualities we need to work on for the future.

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