Data and Code for: Hybrid Modelling of Chemical Processes - A Unified Framework
Description
This dataset contains the Python source code and synthetic data required to reproduce the results in the paper, "Hybrid Modelling of Chemical Processes: A Unified Framework Based on Deductive, Inductive, and Abductive Inference." The project implements a layered hybrid modeling framework for a batch polymerization reactor, combining physics-based models with data-driven methods. The framework is composed of three distinct layers: - Deductive Layer (Tp): Enforces first-principles mass and energy balances to simulate the physical dynamics of the reactor. - Inductive Layer (Tm): An LSTM-based neural network that learns the unknown reaction kinetics from process data. - Abductive Layer (Ta): A feedforward neural network that functions as a soft sensor to infer latent (unmeasured) variables such as molecular weight, viscosity, and branching index. The dataset includes all necessary Python scripts to generate synthetic data, define and train the neural network models, run the integrated hybrid simulation, and visualize the results. The framework is built using Python with libraries including PyTorch, SciPy, and Scikit-learn.
Files
Steps to reproduce
To reproduce the simulation results presented in the paper, please follow these steps: 1. Download and Unzip: Download the provided Hwang_Hybrid_Modeling_2025.zip file and extract its contents to a local directory. 2. Set up the Python Environment: It is recommended to use a virtual environment. Install all required packages using the requirements.txt file. "pip install -r requirements.txt" 3.Run the Main Simulation Script: Navigate to the project directory in your terminal and execute the main script. This single command will automate the entire workflow: synthetic data generation, model training for both inductive and abductive layers, execution of the hybrid simulation, and generation of all result plots discussed in the paper. "python hybrid_model.py" Upon completion, the script will display the result figures and save them to the project directory.