The high fuel requirements for downdraft gasifiers prevent the technology's breakthrough. To identify the limitations for the woodchips fines content (), bark content () and water content (), this work developed novel and easy-to-use functions to predict the gas composition based on these fuel properties. While previous prediction tools require the elemental fuel composition, the presented tool uses only typical commercial fuel properties. Multivariate polynomial regression is performed using 24 different operating points of an 85-kW open-top gasifier. The evaluations revealed three novel findings: (1) The polynomial functions of first order were the most suitable to describe the effects of the fuel properties on the gas composition. (2) According to the developed approach, the optimum fuel leading to the highest product gas lower heating value and a total hydrocarbon content below 8000 ppm contains 0 m% , 6.1 m% and 7.2 m% . (3) The achieved average deviation of the predicted gas composition compared to the measured one was in the same range as the typical variations between experiments. Therefore, a satisfying accuracy was achieved. The developed functions can be applied in academia and industry to identify suitable woodchips and to predict the gas quality when buying woodchips.

中文翻译：

---

基于软木片商业使用特性的商用开顶式气化炉的基于实验数据、易于使用的产品气体成分预测

下吸式气化炉对燃料的高要求阻碍了该技术的突破。为了确定木片细粉含量 ()、树皮含量 () 和水含量 () 的限制，这项工作开发了新颖且易于使用的函数，以根据这些燃料特性预测气体成分。虽然以前的预测工具需要元素燃料成分，但所提出的工具仅使用典型的商业燃料特性。使用 85 kW 开顶式气化炉的 24 个不同操作点执行多元多项式回归。评估揭示了三个新的发现：（1）一阶多项式函数最适合描述燃料性质对气体成分的影响。 (2)根据所开发的方法，导致产物气体较低热值最高且总烃含量低于8000 ppm的最佳燃料包含0 m%、6.1 m%和7.2 m%。 (3) 与测量的气体成分相比，预测的气体成分所实现的平均偏差与实验之间的典型变化处于相同的范围内。因此，获得了令人满意的精度。所开发的功能可应用于学术界和工业界，以识别合适的木片并在购买木片时预测气体质量。