Pinned Repositories
-Machine-Learning-Slides
Slides for CMU 10601, 10605
06-623-Mathematical-Modelling-of-Chemical-Engineering-Processes
Work done for the course 'Mathematical Modelling of Chemical Engineering Processes', all of the work is in MATLAB
10601-18Fall-Homework
My homework solutions for CMU Machine Learning Course (10-601 2018Fall)
bfb-pyrolyzer
Bubbling fluidized bed (BFB) reactor model for biomass fast pyrolysis
ChemEng_Gym
RL for chemical engineering using COCO simulator
Computational_Methods
This repository contains essential python functions for numerical techniques such as root finding, solving ODEs and optimization that are the most commonly used functions by undergraduate students in CHBE.
DrillingProject
Repo to track machine learning work on Oil and Gas drilling data
Heat-Pipe-Reactor
Neutronics, Thermal-hydraulics and Structure Multi-physics Coupled Simulation of Heat Pipe Reactor
Modeling-Simulation
Fixed bed reactor
SCRReactor
Chemical Engineering Design: Projects 4 - Reactor Design Task
puttak's Repositories
puttak/Computational_Methods
This repository contains essential python functions for numerical techniques such as root finding, solving ODEs and optimization that are the most commonly used functions by undergraduate students in CHBE.
puttak/pydens
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
puttak/2D-Navier-Stokes-Finite-Difference-Solver
This project used the forward and backward difference schemes for coding the solution of partial differential equations like linear and non linear convection, diffusion, burger's equation, laplace and navier-stokes equation to simulate the physical phenomena depicted by them.
puttak/bdproject
2020 Course Project in Big Data and Public Policy @ ETH Zurich
puttak/biosteam
The Biorefinery Simulation and Techno-Economic Analysis Modules; Chemical Process Simulation Under Uncertainty
puttak/CHBE220-OER-Dev
Development of Jypiter notebooks for CHBE220
puttak/ChemE-Thermodinamics
functions and some Analysis on Chemical Engineering Thermodinamics.
puttak/Chemical-Engineering-100-UCLA
puttak/Chemical_Engineering
puttak/cheminformatics
Facilitates searching, screening, and organizing large chemical databases
puttak/CVW_PyDataSci2
Supplementary material for Cornell CVW on "Python for Data Science - Part 2: Data Modeling and Machine Learning"
puttak/data-analytics-se
Handout repository for the course "Data Analytics for Scientists and Engineers" at Purdue University.
puttak/Data-Science-Skills-Practice
puttak/DWSIM-Flowsheets
puttak/Energy-Efficiency-Optimization-Neural-network
Data science project
puttak/f17BEM
For f17 12-752 data-driven building energy management
puttak/FluidLearn
Software to solve PDEs and estimate physical parameters governing fluid flow using Deep learning techniques.
puttak/Gasflow_prediction_biogasplant
puttak/Introduction_to_Machine_Learning
Repository of course ressources for Introduction to Machine Learning @ ETH Zurich
puttak/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
puttak/MachineLearningProjects
My projects for the course "Introduction to Machine Learning" (MSc level - 2020) at ETH Zürich. I worked with @ruslenghi in the group "Graphkrone".
puttak/master-thesis
Condition Monitoring of Heat Exchangers using Machine Learning
puttak/Neural-Network-for-solving-PDE
Different methods of solving partial differential equations with neural networks
puttak/NNs-for-Differential-Equations
Implementation and example problems from the method proposed in the "Artificial Neural Networks for Solving Ordinary and Partial Differential Equations" paper.
puttak/Optimisation-of-a-Catalytic-Process-Using-Neural-Network
In recent year, there are increasing concerns over the greenhouse gases in that many environmental issues are associated with the emission of the greenhouse gases. Methane (CH4) and carbon dioxide (CO2) are two major greenhouse gases. Many techniques have been applied to mitigate these greenhouse gases in the environment, among which the dry reforming of methane [1] is particularly used for the production of syngas, an intermediate for producing fuels. 〖CH〗_4 + 〖CO〗_2 ⇌ 2H_2 + 2CO This method provides a possible solution for both global warming and energy shortages. Therefore, many studies have focused on the evaluation of the catalysts in order to promote this reaction as much as possible. This project aims to predict and optimise the catalytic dry reforming of methane using artificial neural network (ANN) algorithms. Based on the experimental data from previous studies, an ANN model will be constructed and developed to investigate the efficiency of different catalyst compositions, which will assist in analysing experimental data and reduce experimental costs in further studies. This report intends to give a general introduction of this project, and the objectives and expected contributions of the project will be introduced in the following parts. A preliminary introduction to the artificial neural network (ANN) algorithm used in the experiment will also be given in this report.
puttak/practical-machine-learning-with-python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
puttak/process-design
puttak/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
puttak/triReforming
Modeling of tri-reforming reactions
puttak/URAP_2020
Storage for code files during Building Decarbonization URAP project Fall 2019