SIMBIOS Centre, Abertay University, Dundee DD1 1HG, UK.
Institute for Complex Systems and Mathematical Biology, SUPA, Department of Physics, University of Aberdeen, Old Aberdeen AB24 3UE, UK.
Bioresour Technol. 2015 May;183:163-74. doi: 10.1016/j.biortech.2015.02.043. Epub 2015 Feb 18.
This study underpins quantitative relationships that account for the combined effects that starting biomass and peak pyrolysis temperature have on physico-chemical properties of biochar. Meta-data was assembled from published data of diverse biochar samples (n=102) to (i) obtain networks of intercorrelated properties and (ii) derive models that predict biochar properties. Assembled correlation networks provide a qualitative overview of the combinations of biochar properties likely to occur in a sample. Generalized Linear Models are constructed to account for situations of varying complexity, including: dependence of biochar properties on single or multiple predictor variables, where dependence on multiple variables can have additive and/or interactive effects; non-linear relation between the response and predictors; and non-Gaussian data distributions. The web-tool Biochar Engineering implements the derived models to maximize their utility and distribution. Provided examples illustrate the practical use of the networks, models and web-tool to engineer biochars with prescribed properties desirable for hypothetical scenarios.
本研究支持定量关系，这些关系考虑了起始生物量和峰值热解温度对生物炭物理化学性质的综合影响。元数据是从不同生物炭样品的已发表数据中收集的（n=102），以（i）获得相互关联性质的网络，和（ii）得出预测生物炭性质的模型。组装的相关网络提供了样本中可能出现的生物炭性质组合的定性概述。广义线性模型用于说明各种复杂情况，包括：生物炭性质取决于单个或多个预测变量，其中对多个变量的依赖可以具有加性和/或交互作用；响应和预测变量之间的非线性关系；以及非正态数据分布。网络、模型和 web 工具 Biochar Engineering 实现了推导模型，以最大限度地提高它们的实用性和分布。提供的示例说明了如何使用网络、模型和 web 工具来设计具有指定性质的生物炭，这些性质是假设情景所需的。
