README_ALGi-zen.md

🧪 ALGi-zen: Computational Modeling of Algae-Based Façade Systems

Author: Doğukan Arısan
Languages: Python, MATLAB, C
Lines of Code: ~12,000
Status: Research prototype recognized by TÜBİTAK (3rd Place, National Research Project Competition – Chemistry Category)

🌱 Overview

ALGi-zen is a computational sustainability project that models microalgae-based façade panels for carbon capture and biostimulant reuse.
The system simulates photosynthetic efficiency, CO₂ absorption, and light diffusion using coupled differential equations and real-time sensor feedback from a custom photoreactor.

The main objective is to determine optimal algae replacement intervals that maximize CO₂ sequestration while minimizing energy and nutrient costs.

⚙️ Core Features

• Differential Equation Solver: Simulates biomass growth and CO₂ uptake using Runge–Kutta and Euler integration methods.
• Photoreactor Simulation: Interfaces with sensors (light, pH, temperature) through a C-based embedded controller.
• Optimization Engine: Uses nonlinear curve fitting and gradient-based optimization to identify high-efficiency growth cycles.
• Visualization Tools: Provides time-series plots of CO₂ concentration, photosynthetic rate, and spectral absorption.
• Data Interface: Connects Python modules with embedded C control systems via serial communication.

🧮 Mathematical Model

The system models both CO₂ concentration and photosynthetic biomass over time as:

$$ \frac{dC}{dt} = -k_1 P(t) A + D(C_{\text{in}} - C) $$

$$ \frac{dP}{dt} = k_2 I(t)\left(1 - \frac{P}{P_{\text{max}}}\right) - k_3 P $$

Where:

• (C): CO₂ concentration (ppm)
• (P): Photosynthetic biomass density (g/L)
• (I(t)): Light intensity (W/m²)
• (A): Effective surface area of the façade (m²)
• (k_1, k_2, k_3): Empirical growth constants
• (D): Diffusion rate constant
• (C_{\text{in}}): Ambient CO₂ concentration

🖥 Architecture

Key Directories

• src/simulation.py – Implements the differential equation solvers
• src/optimization.py – Handles nonlinear optimization and curve fitting
• src/visualization.py – Generates dynamic plots and heatmaps
• src/photoreactor.c – Controls embedded sensors and actuators
• data/ – Stores calibration curves and experimental logs
• notebooks/algi_analysis.ipynb – Interactive data analysis

📈 Example Outputs

• Predicted vs. experimental CO₂ absorption cycles
• Photosynthetic rate curves across varying light intensities
• Efficiency heatmaps over time and nutrient levels

🧩 Impact

• Demonstrated measurable CO₂ capture efficiency in prototype façades
• Integrated environmental chemistry, differential modeling, and software automation
• Recognized nationally by TÜBİTAK for innovation in sustainable material design

📜 Citation

Arısan, D. (2024). ALGi-zen: Computational Optimization of Algae-Based Carbon Capture Systems. TÜBİTAK High School Research Project Competition.