Internet of Things and Artificial Neural Network Application for Optimizing Spirulina Cultivation with Palm Oil Mill Effluent - Ula, Munirul; Fajriana, Fajriana; Ulfah, Julia (Universitas Muhammadiyah Sumatera Utara, Indonesia, 2025)
This study aims to optimize algae biomass production by utilizing Palm Oil Mill Effluent (POME) as a nutrient source, employing Internet of Things (IoT) technology and Artificial Neural Networks (ANN) for predictive modeling and system control. POME, an organic waste from the palm oil industry, was used as an organic liquid fertilizer to enhance the efficiency and sustainability of algae cultivation. The system was designed to monitor and control key environmental parameters such as pH, temperature, salinity, and dissolved oxygen in real-time during a one-month trial in July 2024. ANN-based models were used to predict and adjust environmental conditions, leading to significant improvements in algae growth and resource efficiency. The results indicate that POME can serve as an effective and eco-friendly nutrient source, contributing to both reduced industrial waste and sustainable biomass production. This integrated approach supports circular economy principles and sustainability goals, with potential applications in bioresource production and waste management. Future research will focus on large-scale system testing, optimization for various algae species, and long-term sustainability assessment.