There are 0 repository under process-optimization topic.
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
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Anki Flashcards for 3rd Year Courses
This tool models and optimizes user tasks based on real-world behaviors. It transforms individual task models into unified, constraint-driven representations, using examples like Wordle to demonstrate its effectiveness. The tool visualizes task flows for better design and efficiency.
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. 🚀
A hybrid modeling framework combining neural networks with physics-based constraints for bioreactor process optimization and control.
Advanced Chemical Process Simulation and Optimisation Framework. Overview: This enhanced project provides a sophisticated framework for simulating and optimising chemical processes.
Evidence Based Decisions Using Big Data Analytics
Multi-objective Bayesian optimization for physical experiments with Ax.
Examining a hospital's data by applying various filters to the data and interpreting the statistical results using Disco software, identifying problems in the process, designing new processes that will increase efficiency and drawing the flowchart of new process designed with Microsoft Visio.
Optimizing the sales proposal process using Six Sigma DMAIC methodology to reduce cycle time, defects, and inefficiencies.
Computer-aided molecular and process design using Bayesian optimization
Repositorio para los códigos de GAMS usados en el curso de Optimización de Procesos.
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This presents my portfolio of services and skills.
This repository contains Python codes applied to Chemical Engineering, focusing on Numerical Methods, Thermodynamics, Process Optimization, Transport Phenomena, and other areas within chemical process engineering.
A Python project implementing polynomial regression to analyse and predict manufacturing-related data. Features include data preprocessing, model training, and visualisation of results. Ideal for exploring machine learning applications in manufacturing process optimisation.