PANACHE - Physics-based artificial neural network framework for adsorption and chromatography emulation

The python code TrainPANACHE.py trains the physics-based neural network model for constituent steps in cyclic adsorption processes. The code follows the methodology proposed in Physics-based neural networks for simulation and synthesis of cyclic adsorption processes (https://doi.org/10.1021/acs.iecr.1c04731). As an example, relevant training data is provided here to train blowdown step neural network using this code.

FILE LIST

1. TrainPANACHE.py: Trains physics-based neural networks for constituent steps.
2. trainfcn.m: Parser function loads train_data.mat and generates train_ads.mat file required for running TrainPANACHE.py.
3. train_data.mat: .mat data file containing blowdown step spatiotemporal solutions of all state variables.
4. train_ads.mat: .mat file containing training data for neural network training.

SOFTWARE REQUIREMENTS AND INSTALLATION

Dependencies

The following dependencies are required for the proper execution of this program.

1. MATLAB version 2019b onwards [required]
2. Python 3 [required]
3. Tensorflow v1.15 (GPU) [required]

Installation

1. Clone the full software package from the GitHub server into the preferred installation directory using:

git clone https://github.com/ArvindRajendran/PANACHE.git

INSTRUCTIONS

1. Run trainfcn.m (with train_data.mat in the same directory) in MATLAB to generate train_ads.mat.
2. Run TrainPANACHE.ipyb (with train_ads.mat in the same directory) in Python notebook 3.
3. Save the weights and biases of the trained model for subsequent use in model predictions.

CITATION

@article{Subraveti2022,
title = {Physics-based neural networks for simulation and synthesis of cyclic adsorption processes},
author = {Sai Gokul Subraveti and Zukui Li and Vinay Prasad and Arvind Rajendran},
journal = {Industrial & Engineering Chemistry Research},
year = {2022},
doi = {10.1021/acs.iecr.1c04731}
}

AUTHORS

Maintainers of the repository

• Sai Gokul Subraveti ([email protected])

Project Contributors

• Prof. Dr. Arvind Rajendran ([email protected])
• Prof. Dr. Vinay Prasad ([email protected])
• Prof. Dr. Zukui Li ([email protected])

LICENSE

Copyright (C) 2022 Arvind Rajendran

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.