用层次贝叶斯模型估计原木风干时间及评价影响原木干燥特性的原木性质

Ken Watanabe, H. Korai, Isao Kobayashi, T. Yanagida, Keisuke Toba, Kosei Mitsui
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引用次数: 3

摘要

空气干燥是最常用的操作之一,以脱水伐木残留物生产木材燃料片。本研究建立了原木风干的干燥曲线模型,以估计达到给定湿度水平所需的干燥时间,并评估原木性质对原木干燥特性的影响。2014年11月至2015年8月,在日本筑波市风干了杉木((cid:9479)(cid:9526)(cid:9533)(cid:9524)(cid:9528)(cid:9523)(cid:9521)(cid:9513)(cid:9526)(cid:9517)(cid:9509)(cid: 9544)(cid:9518)(cid: 9524)(cid: 9522)(cid:9517)(cid:9511)(cid:9509))的原木堆。采用层次贝叶斯模型估计单个原木的含水率作为初始含水率、直径、长度、心材比和干燥时间的函数。实测的平均含水率与估计的干燥曲线拟合得很好,表明所建立的模型是在有限的气候条件范围内估计任何含水率的干燥时间的潜在有用方法。综合评价了影响原木干燥特性的原木性质,发现初始含水率、直径和长度对原木干燥速率有影响。时间,层次贝叶斯模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Drying Time for Air-drying of Logs and Evaluation of Log Properties Affecting Drying Characteristics of Logs Using a Hierarchical Bayesian Model
Air-drying is one of the most frequently used operations to dehydrate logging residues for produc-tion of wood fuel chips. This study developed a drying curve model for the air-drying of logs in order to estimate drying time to reach a given moisture level, and to evaluate the influence of log properties on drying characteristics of logs. Log piles of sugi ((cid:9479)(cid:9526)(cid:9533)(cid:9524)(cid:9528)(cid:9523)(cid:9521)(cid:9513)(cid:9526)(cid:9517)(cid:9509)(cid:9444)(cid:9518)(cid:9509)(cid:9524)(cid:9523)(cid:9522)(cid:9517)(cid:9511)(cid:9509)) were air-dried in Tsukuba city, Japan, from November 2014 to August 2015. A hierarchical Bayesian model was used to estimate the moisture content of individual logs as a function of initial moisture content, diameter, length, heartwood ratio, and drying time. The measured mean moisture content fit well to the estimated drying curve, indicating that the developed model is a potentially useful method to estimate the drying time for any moisture content within the limited range of climate conditions studied here. A comprehensive evaluation of log properties affecting the drying characteristics of logs showed that initial moisture content, diameter, and length have an influence on the drying rate of logs. time, hierarchical Bayesian model.
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