<![CDATA[This study presents a Python-based multiphysics framework for modeling and optimizing heat transfer in insulated metallic charcoal kilns, targeting improved thermal efficiency, reduced processing time, and enhanced charcoal yield. The model couples transient heat conduction, convective–radiative losses, moisture evaporation, and biomass pyrolysis kinetics, solved using finite-difference schemes and validated against pilot-scale kiln measurements. Model predictions achieved temperature deviations below 8 °C above 200 °C and charcoal yield errors within 5% of experimental values. A multi-objective optimization using NSGA-II identified optimal kiln configurations that increased thermal efficiency from 42.5% to 67.3%, reduced carbonization time by 28–35%, and improved charcoal yield to 32–35% compared with conventional designs. Sensitivity analysis showed insulation thickness and heating rate to be the dominant design variables, while excessive insulation yielded diminishing returns. Compared with recent kiln and rotary-reactor studies, the proposed Python framework offers superior transparency, flexibility, and computational efficiency. The results demonstrate that open-source numerical tools can provide a cost-effective pathway toward cleaner, energy-efficient charcoal production, supporting sustainable biomass utilization in developing economies. Keywords: Heat-transfer; Wood; Pyrolysis; Charcoal-kiln; Modelling.]]>
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