BioReact: Dynamic Simulation of a Continuous Stirred Tank Bioreactor (CSTR)

Overview

BioReact is a Python-based simulation tool designed to model the dynamic behavior of a Continuous Stirred Tank Bioreactor (CSTR). It enables users to analyze the evolution of biomass, substrate, and growth rates over time using the fourth-order Runge-Kutta method. The tool is particularly suited for researchers, students, and engineers in the fields of biotechnology and bioengineering.

Features

1. Dynamic Simulation:

   • Models the concentrations of biomass and substrate over time.
   • Tracks specific growth rate (( \mu )) dynamically using the Monod model.

2. Visualization:

   • Automatically generates plots for:
     • Biomass vs. Substrate (Phase Diagram).
     • Biomass and Substrate vs. Time.
     • Growth Rate vs. Time.
   • Saves all plots in a dedicated figures directory with consistent dimensions and transparent backgrounds.

3. Customizable Parameters:

   • Allows users to specify reactor conditions, microbial kinetics, and simulation parameters to suit different scenarios.

4. Examples of Generated Visualizations:

   • Biomass vs Substrate (Phase Diagram):
   • Biomass and Substrate vs Time:
   • Growth Rate vs Time:

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How It Works

The simulation is based on the following equations:

1. Biomass Balance: [ \frac{dX}{dt} = X \cdot (\mu - D) ]

2. Substrate Balance: [ \frac{dS}{dt} = D \cdot (S_f - S) - \frac{\mu \cdot X}{Y} ]

3. Growth Rate (Monod Model): [ \mu = \frac{\mu_{\text{max}} \cdot S}{K_s + S} ]

Parameters

• ( X ): Biomass concentration (g/L).
• ( S ): Substrate concentration (g/L).
• ( D ): Dilution rate (( F / V ), 1/h).
• ( S_f ): Feed substrate concentration (g/L).
• ( \mu ): Specific growth rate (1/h).
• ( Y ): Biomass yield coefficient (g biomass/g substrate).
• ( \mu_{\text{max}} ): Maximum specific growth rate (1/h).
• ( K_s ): Substrate affinity constant (g/L).

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Getting Started

Prerequisites

Ensure you have Python 3.8+ installed along with the following libraries:

• numpy
• pandas
• matplotlib

Install the required libraries using pip:

pip install numpy pandas matplotlib

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Usage

1. Clone the repository:

   git clone https://github.com/yourusername/BioReact.git
   cd BioReact

2. Run the simulation: Define your reactor parameters and execute the simulation. Example script:

   from cstr_simulation import runge_kutta_4, plot_biomass_vs_substrate, plot_biomass_and_substrate_vs_time, plot_growth_rate_vs_time
   
   # Simulation parameters
   initial_time = 0.0
   initial_biomass = 0.1
   initial_substrate = 20.0
   yield_coefficient = 0.5
   step_size = 0.1
   mu_max = 0.4
   Ks = 1.0
   reactor_volume = 1.0
   flow_rate = 0.1
   substrate_feed = 50.0
   simulation_steps = 100
   
   # Run simulation
   results = runge_kutta_4(
       time=initial_time,
       biomass=initial_biomass,
       substrate=initial_substrate,
       yield_factor=yield_coefficient,
       step_size=step_size,
       mu_max=mu_max,
       Ks=Ks,
       volume=reactor_volume,
       flow_rate=flow_rate,
       substrate_feed=substrate_feed,
       steps=simulation_steps,
   )
   
   # Generate plots
   plot_biomass_vs_substrate(results)
   plot_biomass_and_substrate_vs_time(results)
   plot_growth_rate_vs_time(results)

3. Review the results:

   • The simulation outputs a DataFrame with the following columns:
     • Time (h)
     • Biomass (g/L)
     • Substrate (g/L)
     • Growth Rate (1/h)
   • The plots are saved in the figures directory.

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Repository Structure

The repository is organized as follows:

BioReact/
│
├── cstr_simulation.py       # Core simulation and plotting functions
├── examples/
│   ├── example_run.py       # Example script demonstrating usage
│   └── example_notebook.ipynb # Example Jupyter Notebook
├── figures/                 # Contains generated plots
│   ├── biomass_vs_substrate.png
│   ├── biomass_and_substrate_vs_time.png
│   └── growth_rate_vs_time.png
├── README.md                # Documentation
└── LICENSE                  # License for the repository

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Future Development

Planned features include:

• Advanced visualizations for parameter sensitivity analysis.
• Enhanced support for batch and fed-batch reactor models.
• Integration with experimental data for validation.

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Authors

Emiliano Balderas Ramírez
PhD Student at the Instituto de Biotecnología, UNAM
Email: ebalderas@live.com.mx
Phone: +52 2221075693

Dr. Octavio Tonatiuh Ramírez Reivich
Principal Investigator, Instituto de Biotecnología, UNAM
Email: tonatiuh.ramirez@ibt.unam.mx

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For questions or suggestions, feel free to contact:
Emiliano Balderas Ramírez
PhD Student at the Instituto de Biotecnología, UNAM
Email: ebalderas@live.com.mx
Phone: +52 2221075693