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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
I Gde Ketut Sri Budarsa
Politeknik Negeri Bali
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Mathematical Modeling of an Adaptive Lighting System Based on Solar Panels and Digital Communication for Microalgae Synthesis I Gede Suputra Widharma; I Gde Nyoman Sangka; I Gde Ketut Sri Budarsa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2435

Abstract

Microalgae are promising photosynthetic microorganisms widely used in biofuel, pharmaceutical, and environmental applications. Their cultivation efficiency is highly influenced by light intensity, temperature, and pH. This study presents a mathematical model of an adaptive lighting system powered by solar energy and controlled through digital communication for sustainable microalgae synthesis. The system dynamically regulates LED illumination using real-time environmental feedback from temperature and pH sensors integrated into an IoT network. The model combines first-order ordinary differential equations (ODEs) to describe solar input, LED power consumption, environmental response, and communication delay. Numerical simulations performed in MATLAB show that the adaptive control algorithm maintains optimal illumination while minimizing unnecessary energy use. Compared to conventional static lighting, the proposed model achieves a 35% reduction in energy consumption and improved environmental stability despite communication latency. The study provides a foundational framework for developing intelligent, energy-efficient photobioreactor systems that align with the Sustainable Development Goals (SDG 7 and SDG 13). Future work may extend the model toward real-time, predictive, and machine-learning-based control for field-scale implementation.
Co-Authors I Gde Nyoman Sangka I Gede Suputra Widharma