Abstract
The research addresses the growing global demand for sustainable energy by developing a smart biodiesel reactor that integrates intelligent monitoring, automated control, and AI optimization. Traditional biodiesel production faces challenges such as inefficient management and inconsistent quality due to manual monitoring. The proposed system features a microcontroller-based control platform to manage critical variables through Pulse Width Modulation (PWM) signals. It regulates components like heating elements and pumps while collecting real-time data on temperature, flow rate, and other parameters, creating a flexible and cost-effective solution for small-scale biodiesel production and research.
A Machine learning model was developed to predict biodiesel yield based on key operating parameters, including reaction temperature, mixing speed, catalyst concentration, and reaction time. The model was trained and evaluated using experimental datasets in a Python-based computational environment using scientific libraries and predictive analytics techniques. Performance evaluation using statistical metrics demonstrated satisfactory predictive capability, indicating that the model can effectively estimate biodiesel yield under varying operating conditions.
A digital twin simulation of the biodiesel reactor was also developed to validate the AI model and test optimization strategies prior to physical implementation. The digital twin replicates the reactor behavior using a simplified process model and allows real-time comparison between predicted and simulated yields. Furthermore, a closed-loop optimization framework was implemented in which the AI model continuously evaluates different operating conditions and automatically identifies optimal parameter combinations that maximize biodiesel yield.
The integration of AI-based optimization, digital twin simulation, and microcontroller-driven automation demonstrates the potential for developing intelligent biodiesel production systems that can improve yield, enhance process efficiency, and reduce operational complexity. The proposed system provides a scalable foundation for future implementation in fully automated smart biofuel production platforms.
