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.

End-to-end ML pipeline for the esterification process yield prediction and optimization using neural networks.

Strategic framework for digital transformation in multi-national manufacturing. designed for conservative organizations

This repository contains machine learning models for predicting the degree of SiO2 extraction from iron ore tailings using aqueous solution of ammonium bifluoride (NH4HF2). The prediction models take into account three key parameters: temperature, reaction time, and NH4HF2 concentration.

Python automation tool for bulk YouTube transcript extraction | Built for CCNA study workflow

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. 🚀

Autonomous AI system to optimize manufacturing process parameters — injection molding, extrusion, CNC — using Bayesian optimization and reinforcement learning

First-principles simulation of batch reactor kinetics (A→B→C) using Python & Scipy to optimize yield under thermal constraints.

Computer-aided molecular and process design using Bayesian optimization