Optimization of operating parameters and heating modes for small biomass boilers oriented toward minimizing operational costs

With the ongoing transition of the global energy structure and increasingly stringent environmental regulations, biomass boilers, as a form of renewable energy utilization technology, are being widely adopted in the heating sector. However, the operation of traditional biomass boilers still relies heavily on manual experience, resulting in low energy efficiency and high operating costs. To address this issue, this study proposes a novel dynamic intermittent heating mode for small biomass boilers, developed based on heating theory and real-world operational data characteristics. Through orthogonal experimental design, the relationships between key operating parameters—including blower frequency, feed rate, and grate frequency—and the overall operating cost were quantitatively analyzed. A regression-based cost model was established and further optimized using an Adaptive Genetic Algorithm (AGA) to determine the minimum-cost parameter combination. The optimal operating conditions were obtained as follows: blower frequency 23.4 Hz, feed rate 7.0 Hz, and grate frequency 13.5 Hz. Experimental validation on a 2-ton CDZL1.4–80/60-S biomass boiler (fueled with wood pellets) demonstrated that, compared with conventional experience-based operation, the proposed heating strategy ensures stable indoor temperatures while reducing the average operating cost by 29.5 %, corresponding to seasonal savings of USD 13,345.2.