A comprehensive collection of mathematical tools and utilities designed to support Lean Six Sigma practitioners in their process improvement journey
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Updated
Aug 24, 2023 - Python
A comprehensive collection of mathematical tools and utilities designed to support Lean Six Sigma practitioners in their process improvement journey
Data-driven computer-aided molecular and process design
A hybrid modeling framework combining neural networks with physics-based constraints for bioreactor process optimization and control.
A Python web app using Streamlit & PuLP for ILP-based production scheduling. Maximizes profits by assigning products to machines, factoring in batch sizes, setup times, rates, costs, & demand. User-friendly, flexible, & ideal for manufacturing.
The prototype, CemGenie, is a Generative AI–powered platform designed to autonomously optimize cement plant operations. It integrates real-time process data from raw material handling, grinding, clinkerization, and utilities into a unified AI control layer.
Q-Learning Implementation for Process Optimization A reinforcement learning project that calculates the shortest route between locations using the Q-Learning algorithm. This code demonstrates how AI can optimize processes in a simulated environment with predefined states and rewards. 🚀
Computer-aided molecular and process design using Bayesian optimization
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