Gaurav-Van/Optimizing-Rate-of-Penetration-in-Geothermal-Drilling-A-Digital-Twin-Approach
Let’s explore something interesting together. In this project, we developed a machine learning digital twin using Intel-optimized XGBoost and daal4py to simulate and optimize the Rate of Penetration (ROP) in geothermal drilling. We leveraged SHAP for Explainable AI (XAI) to interpret model predictions.
No commits in the last 6 months.
Stars
4
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Nov 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Gaurav-Van/Optimizing-Rate-of-Penetration-in-Geothermal-Drilling-A-Digital-Twin-Approach"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
petrobras/BibMon
Python package that provides predictive models for fault detection, soft sensing, and process...
hustcxl/Deep-learning-in-PHM
Deep learning in PHM,Deep learning in fault diagnosis,Deep learning in remaining useful life prediction
kokikwbt/predictive-maintenance
Datasets for Predictive Maintenance
biswajitsahoo1111/rul_codes_open
This repository contains code that implement common machine learning algorithms for remaining...
tvhahn/weibull-knowledge-informed-ml
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets....