Summer Machine Learning Prediction of Gasification Gas Yield and Compositions: Student or Bachelor's Grad
Through the Oak Ridge Institute for Science and Education (ORISE), this posting seeks a student researcher to engage in projects with the Research Innovation Center (RIC) at the National Energy Technology Laboratory (NETL), under the mentorship of Ping Wang.
Gasification technology is effective for conversion of carbonaceous fuels (like coal, biomass, and plastic waste) into syngas, which can be used for various applications like heat, power, liquid fuel, and chemicals. Gasification is a complex thermochemical process with multi-phase reactions. It is crucial to control the properties of syngas accurately for different applications. This project focuses on machine learning (ML) algorithms for predicting gasification gas yields and compositions based on gasification conditions and the fuels' characteristics. The selected applicant will search and collect published literature on gasification. They will extract gasification data from the literatures and use it for ML development. Data will include feedstock thermochemical property, operation conditions (like temperature, steam to feedstock ratio, equivalence ratio), product yields, and gas compositions. Under the guidance of their mentor, the selected participant will learn to create a database from the complied dataset. They will learn to develop and train ML models to predict the gasification yields and gas compositions.
During this project, the selected participant will learn how to search literature on gasification and collect data from literature, create a database with complied dataset, pretreat data using Z-Score normalization method, and apply data-driven machine learning (ML) methods for gasification product prediction. By reporting research progress bi-weekly in group meetings, the participant will gain skills in technical communication and team works.
This is an educational opportunity offered by NETL and administered by the Oak Ridge Institute for Science and Education. Participants in the program are not considered employees of DOE, NETL, ORISE, or any other office or agency.
All applications must be accepted through the external application site (Zintellect). Completed applications will be reviewed on a rolling basis. Selection decisions are made directly by the hosting mentor, and may be made at any point throughout the year.
- Appointment dates: Appointment start and end date are flexible, based on the needs of the research project and the availability of the selected student or recent graduate. New applications may be considered for an appointment starting 3+ months in the future. Students interested in a potential spring internship are encouraged to apply by October 1st. Students interested in a potential summer internship are encouraged to apply by February 15th.
- Duration: appointment duration varies based on project needs and the selected applicant's availability. Summer appointments are typically 10-12 weeks, longer appointments and extensions are possible.
- Location: This project is hosted by NETL's Pittsburgh, Pennsylvania site.
- Stipend: The selected participant will receive a biweekly stipend commensurate with educational level and experience. Stipends start at $440 per week - $600 per week for undergraduate students; $675 to $756 per week for recent Bachelor's graduates; or $675 to $1,025 per week for graduate students.
- On-site participants who must travel over 50 miles to the hosting NETL facility to begin their appointment may be eligible for inbound/outbound travel reimbursement. Details to be included in an official appointment offer letter.
No previous experience with research or technical projects is required to apply for this opportunity. Do describe any relevant experience you have in your application. Relevant skills that you may have from non-research experiences could include problem-solving, communication, teamwork, organizational skills, and more.
To be eligible for this opportunity, you must be a current undergraduate student, recent Bachelor's graduate (having received a Bachelor's degree within the last 2 years at time of application), or a current graduate student.
The ideal candidate would have experience with some, but not necessarily all, of the following:
- Java and Python
- Creating a database and performing data analysis on the database
- Developing a user-interface for the database
- Thermochemical processes
This opportunity does have citizenship restrictions. Students and recent Bachelor's grads who are not U.S. citizens are invited to apply to the general Professional Internship Program for consideration for other projects: https://zintellect.com/Opportunity/Details/NETL-PIP.