Design and Optimization of a Smart IoT-Integrated Bioreactor for Enhanced Biogas Production Using Multi-Algorithm Modelling Approaches

Abstract

The growing global demand for clean and sustainable energy sources has intensified interest in biogas as a renewable alternative to fossil fuels. However, conventional biogas production systems often suffer from inefficiencies due to poor process monitoring and limited control mechanisms. This study focuses on the design, construction and optimization of a smart Internet of Things (IoT) system couple with anaerobic digestion (AD) bioreactor for improved biogas production and real time monitoring. Embedded sensors; temperature (DS18B20), carbon dioxide (MQ135), and methane (MQ4) were incorporated into the bioreactor connected to Thing-Speak IoT network for continuous visualization, remote system diagnostic and data acquisition. Three optimization schemes; Nelder-Mead Simplex Direct Search (NMSDS), Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) were employed for optimum biogas generation, dynamic parameter identification and predictive modelling of the bioreactor performance. Between this evaluated algorithm, the NMSDS scheme shows the best prediction accuracy with objective function (J) and mean absolute error (MAE) value of 93.577 and 0.098 respectively, illustrating its performance in capturing the non-linear behavior of the AD system, while its average standard error of prediction (SEP) and standard error of calibration (SEC) attained by the system are 0.0155 and 0.010 respectively. The operational efficiency, predictive capability and stability of AD process enhanced significantly with the integration of smart IoT monitoring system and advanced modelling.