Syngas production from biomass gasification is a potentially sustainable and alternative means of conventional fuels. The current challenges for biomass gasification process are biomass storage and tar contamination in syngas. Co-gasification of two biomass and use of mineral catalysts as tar reformer in downdraft gasifier is addressed the issues. The optimized and parametric study of key parameters such as temperature, biomass blending ratio, and catalyst loading were made using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) on tar reduction and syngas. The maximum H₂ was produced when Portland cement used as catalyst at optimum conditions, temperature of 900 °C, catalyst-loading of 30%, and biomass blending-ratio of W52:OPF48. Higher CO was yielded from dolomite catalyst and lowest tar content obtained from limestone catalyst. Both RSM and ANN are satisfactory to validate and predict the response for each type of catalytic co-gasification of two biomass for clean syngas production.

通过生物质气化生产合成气是一种潜在的可持续和替代传统燃料的方法。当前生物质气化过程面临的挑战是生物质储存和合成气中的焦油污染。解决了两种生物质的共气化和在下吸式气化炉中使用矿物催化剂作为焦油重整器的问题。使用响应面方法 (RSM) 和人工神经网络 (ANN) 对焦油还原和合成气等关键参数进行优化和参数化研究，如温度、生物质混合比和催化剂负载。最大 H ₂以硅酸盐水泥为催化剂，在最佳条件下生产，温度为900°C，催化剂负载量为30%，生物质混合比为W52:OPF48。白云石催化剂产生较高的 CO，石灰石催化剂产生的焦油含量最低。RSM 和 ANN 都令人满意地验证和预测两种生物质的每种类型的催化共气化以生产清洁合成气的响应。