This repository contains MATLAB code dedicated to predict dual-functional gas adsorption processes. The codebase leverages Bayesian analysis and various isotherm models to provide a comprehensive tool for understanding mechanisms within dual-functional gas purfication. Key functionalities include the setup and execution of Bayesian analysis, forward modeling based on inferred parameters, and essential functions for solving linear systems and calculating isotherm properties.
1. Comprehensive Bayesian Analysis and Results Visualization for Dual-Functional Gas Purification Modeling (BPM_DualFunc_GasPurification_Main.m)
The BPM_GasAdsorption_Main.m module is central to the Bayesian analysis of gas adsorption processes. It is designed to process experimental data meticulously, laying the groundwork for sophisticated analysis. Key steps within this module include:
- Data Loading and Processing: Efficiently manages the ingestion and preliminary treatment of experimental data, ensuring readiness for analysis.
- Bayesian Analysis Framework: Establishes a robust Bayesian inference framework. This involves setting up algorithmic structures for 2-step Bayesian parameter estimation, including defining prior distributions and configuring options for included solvers within entire procedure.
- Inversion Solver Configuration: Carefully configures the Bayesian inversion solver and sampler, optimizing them for the specific needs of gas adsorption modeling.
- Results Reporting and Visualization:Finalizes the analysis process by reporting and visually representing results, focusing on the interpretation and understanding of the Bayesian analysis outcomes.
2. IUQ Procedure Module (uq_DualFunc_GasPurification_Step1.m and uq_DualFunc_GasPurification_Step2.m)
This function embodies the essence of the Inverse Uncertainty Quantification method, a critical aspect of Bayesian parameter estimation techniques in modeling of dual-functional gas purification process. It is designed to:
- Handle Multiple Data Sets: Processes various datasets simultaneously, ensuring a comprehensive approach to parameter estimation.
- Estimate Model Parameters: Utilizes Bayesian analysis to precisely estimate parameters within the gas adsorption model, enhancing both accuracy and reliability.
The Model_DualFunc_GasAdsorption function is a testament to the detailed simulation capabilities of the tool. It undertakes forward modeling for dual-functional gas purification by:
- Employing Various Isotherm Models: Adapts to different isotherm models, providing versatility in modeling the adsorption process.
- Resolving concurrent mechanisms within dual-functional gas purification process: Accounts for both adsorption and catalytic decomposition occurring simultaneously in dual-functional gas purification process, enabling mechanism-specific parameter estimation, predictive breakthrough analysis, and quantitative evaluation of gas purification performance under varied operating conditions.
- Solving with Implicit Solver: Utilizes an advanced implicit solver with iterative linearization using the Newton method, ensuring rapid and precise simulation results even in complex scenarios.
These functions form the backbone of the computational processes within the tool:
- Thomas Algorithm (
thomas): A robust solution for tridiagonal linear systems, pivotal in numerical methods and simulations. - Langmuir Isotherm (
Isotherm_Langmuir): Calculates adsorption quantities, implementing the renowned Langmuir isotherm model for single-layer adsorption. - Derivative of Langmuir Isotherm (
Deriv_Langmuir): Provides the derivative calculations of the Langmuir isotherm model, essential for understanding the dynamics of adsorption processes.
- Yesol Hyun - School of Mathematics and Computing (Computational Science and Engineering), Yonsei University - yesol2@yonsei.ac.kr
- Jung-Il Choi - School of Mathematics and Computing (Computational Science and Engineering), Yonsei University - jic@yonsei.ac.kr
Clone the repository to your local machine using:
git clone https://github.com/MPMC-Lab/BPM_DualFunc_GasPurification.gitAlternatively, the source files can be downloaded through github menu 'Download ZIP'.
Contributions to this project are welcome. Please fork the repository and submit a pull request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
Should you require assistance or have any inquiries, kindly contact Dr. Jung-Il Choi via email at jic@yonsei.ac.kr.
For detailed information and further reading related to BPM_DualFunc_GasPurification, you are encouraged to consult the reference paper. Additional insights and resources are available at School of Mathematics and Computing, within the domain of Computational Science and Engineering at Yonsei University. For more details, please visit our website: mpmc.yonsei.ac.kr.