Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Predicting Efficiency blends exergy thermodynamics with modern machine‑learning techniques to forecast the performance of energy systems. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Predicting Efficiency blends exergy thermodynamics with modern machine‑learning techniques to forecast the performance of energy systems.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A demonstrable AI Enablement project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – The Physics: Sensors, Steam, and Exergy
Explore Energy vs. Exergy using the First & Second Laws • Map Power Cycle components – boilers, turbines, condensers • Implement CoolProp in Python to compute enthalpy & entropy • Ingest turbine sensor data and calculate Exergy Destruction
Day 2 – The Algorithm: Intro to Machine Learning
Define features and targets for efficiency prediction • Prepare data splits for training and testing • Build a RandomForestRegressor model and train it • Evaluate predictions using Mean Absolute Error
Day 3 – The Insight: Advanced AI & Predictive Maintenance
Deploy XGBoost for high‑accuracy forecasting • Generate feature‑importance charts to explain model decisions • Visualize actual vs. predicted exergy destruction • Save the trained model for real‑time deployment
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | CoolProp |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | RandomForestRegressor |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | scikit-learn |
Frequently Asked Questions
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