Development and performance evaluation of an electronic multifunctional intelligent cooking stove

Wood is a significant global energy source, accounting for about 6% of primary energy supply. However, efficient and controlled utilization for domestic heating poses challenges. This study introduces a multi-functional wood-fired cooking stove designed for simultaneous cooking and liquid heating, featuring an IoT-enabled electronic control system. The stove utilizes an ATmega328-based Arduino Uno microcontroller along with digital temperature sensors to regulate heat transfer through a copper-pipe heat exchanger, allowing for closed-loop temperature control. Performance evaluation involved controlled cooking and boiling experiments, with thermal efficiency assessed by comparing the useful heat absorbed by the cooked food and heated water to the chemical energy input from the firewood. Experimental results show that the stove cooked 2 kg of rice in approximately 10 min while simultaneously boiling 5 L of water, achieving a combined useful energy output of 836 kJ under regulated temperature conditions. From experiment, the fuel consumed of improved biomass stoves was 140.53 g of fuel per liter, while traditional stoves were 72.3–214.8 g of fuel per liter of water. From this, the proposed stove could save 0.127 kg of fuel wood per liter of water and food than traditional and biomass stoves. In addition, this paper achieved a thermal efficiency of 36.28% better than conventional stoves (13%) and improved biomass stoves (31%). The high heat utilization effectiveness of 97.75% was observed in this paper. The IoT-based control algorithm maintained water temperature within a ± 2 °C tolerance, reducing overheating losses and improving heat utilization compared to conventional wood stoves. The novelty of the proposed system lies in the integration of real-time electronic temperature regulation and multi-output thermal utilization in a single wood-fired stove, demonstrating improved energy efficiency, operational safety, and adaptability for both residential and small-scale commercial applications. The proposed stove confirm that can significantly enhance sustainable and efficient use of wood energy. Future research should integrate active airflow control and AI/ML-based predictive thermal optimization to further enhance combustion efficiency and reduce emissions.