Elsevier

Bioresource Technology

Volume 183, May 2015, Pages 163-174
Bioresource Technology

Reverse engineering of biochar

https://doi.org/10.1016/j.biortech.2015.02.043Get rights and content

Highlights

  • Starting biomass and peak pyrolysis temperature jointly affect biochar properties.

  • 19 different physico-chemical properties of biochar were properly modeled by GLM.

  • Models reveal complex relationships between biochar properties and predictors.

  • Ubiquitous non-Gaussian and non-linear attributes were accounted for in GLMs.

  • Proposed correlation networks, models and web-tool can be used to engineer biochar.

Abstract

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.

Introduction

Biochar, the product of biomass thermochemical conversion in an oxygen depleted environment, has gained increasing recognition as a modernized version of an ancient Amerindian soil management practice, with at times wide-ranging agronomic and environmental gains (Lehmann et al., 2003, Atkinson et al., 2010, Novak et al., 2013). Some of the most commonly acclaimed benefits of biochar application to soils include: increased long-term C storage in soils (Atkinson et al., 2010, Joseph et al., 2010, Cross and Sohi, 2011, Ennis et al., 2011, Karhu et al., 2011, Novak et al., 2013), restored soil fertility (Glaser et al., 2002, Lehmann et al., 2003, Gaskin et al., 2008, Novak et al., 2009, Atkinson et al., 2010, Laird et al., 2010, Beesley et al., 2011, Lehmann et al., 2011, Enders et al., 2012, Spokas et al., 2012b, Novak et al., 2013), improved soil physical properties (Novak et al., 2009, Joseph et al., 2010, Ennis et al., 2011, Karhu et al., 2011, Lehmann et al., 2011, Novak et al., 2013), boosted crop yield and nutrition (Novak et al., 2009, Major et al., 2010, Lehmann et al., 2011, Rajkovich et al., 2012, Spokas et al., 2012a, Novak et al., 2013), enhanced retention of environmental contaminants (Cornelissen et al., 2005, Loganathan et al., 2009, Cao and Harris, 2010, Beesley et al., 2011), and reduced N-emission and leaching (Spokas et al., 2012b, Novak et al., 2013). Examples of the specific biochar properties responsible for these benefits are summarized in Table 1.

Biochar quality can be highly variable, and its performance as an amendment – whether beneficial or detrimental – is often found to depend heavily on its intrinsic properties and the particular soil it is added to (Lehmann et al., 2003, Novak et al., 2009, Atkinson et al., 2010, Major et al., 2010, Lehmann et al., 2011, Spokas et al., 2012a). As has been previously concluded, biochar application to soil is not a “one size fits all” paradigm (Spokas et al., 2012a, Novak et al., 2013). Consequently, detailed knowledge of the biochar properties and the specific soil deficiencies to be remediated is critical to maximize the possible benefits and minimize undesired effects of its use as a soil amendment. While soil deficiencies must be identified on a site-by-site basis, it is conceivable that biochar properties can be engineered through the manipulation of pyrolysis production parameters and proper selection of parent biomass type (Zhao et al., 2013). The capacity to produce biochars with consistent and predictable properties will, first, enable efficient matching of biochars to soils, and second, facilitate the deployment of this soil management strategy at large and commercial scales. Although the properties and effects of biochar samples produced from a variety of methods and starting biomasses have been intensively studied, as yet, the analytical techniques for characterization and effect quantification are not standardized. This creates a challenge when comparing biochar properties and effects across studies. At the same time, making such comparisons is imperative to gain a comprehensive understanding of alterable biochar properties.

The prevailing hypothesis in the literature is that the selection of peak pyrolysis temperature and parent biomass – as two key production variables – fundamentally affects resulting biochar properties. Identification of relationships between production variables and biochar properties has been pursued by many investigators, but has been limited to the small number of samples produced and analyzed for each study (e.g., Karaosmanoğlu et al., 2000, Zhu et al., 2005, Gaskin et al., 2008, Nguyen and Lehmann, 2009, Cao and Harris, 2010, Joseph et al., 2010, Keiluweit et al., 2010, Cao et al., 2011, Cross and Sohi, 2011, Hossain et al., 2011, Mukherjee et al., 2011, Enders et al., 2012, Rajkovich et al., 2012, Zhao et al., 2013), with few reports combining measurements from more than one source (Cordero et al., 2001, Glaser et al., 2002, Atkinson et al., 2010, Ennis et al., 2011, Spokas et al., 2012a). The knowledge gained from the above studies does not provide a quantitative understanding of the relationships between production variables and biochar properties. The shortcomings responsible for such lack of systematic insight include: (i) reported trends that are primarily qualitative with respect to the independent effect of parent biomass or temperature (e.g., decrease in labile carbon with increasing pyrolysis temperature for selected samples (Cross and Sohi, 2011)), (ii) trends that are often in conflict with similar samples of other studies (e.g., positive effect (Rajkovich et al., 2012) vs. negligible effect (Nguyen and Lehmann, 2009) of temperature on pH for oak biochar), and (iii) correlations that are not convincing (e.g., correlation r = 0.5 between volatile matter content and microporous surface area (Mukherjee et al., 2011)). A recent study by Zhao et al., 2013 reports, for the first time, a quantitative evaluation of the individual influence of feedstock source and production temperature on various biochar properties. The authors classified a variety of physical and chemical biochar properties as predominantly controlled by either feedstock or temperature. While this initial knowledge is critical to guide the production of designed biochar, it falls short when the influence of both parameters is significant, as is the case with most properties of interest.

The present study advances the quantitative approach one step further by constructing relationships that capture the combined influence that starting biomass and temperature has on various biochar physico-chemical properties of agronomic and environmental interest. The first objective was to gather comparable data from various sources to create an unbiased meta-data set on which to perform statistical analyses. The second objective was to identify groups of inter-correlated properties to gain an insight into how individual properties may be affected when others are manipulated. The third objective was to underpin quantitative relationships between production variables and the measured properties of biochar in the meta-data, as listed in Table 1. The fourth objective was to implement the identified relationships in a simple-to-use web application, which provides an estimate of the expected properties of biochar when produced under a user-defined set of production variables. The overarching goal is to improve the efficiency in production of biochar with engineered properties so that it can best match the needs of a particular soil or crop system.

Section snippets

Assembly of meta-data library

A library of meta-data (summarized in Table A.1) was created using information from 102 different biochar samples measured for 22 unique physical and chemical characteristics. To build the library, data were gathered from published studies that: (i) used slow-pyrolysis biochar, (ii) reported the production details, and (iii) extensively characterized the physical and chemical properties of biochar materials (Karaosmanoğlu et al., 2000, Cordero et al., 2001, Gaskin et al., 2008, Keiluweit et

Correlation matrix and networks

Related biochar properties identified from the correlation matrix (Fig. 1) were used to build a network representation of the 22 responses included in this study (Fig. 2). From the generated networks, three groups of interdependent biochar properties were distinguished and five individual properties found to be independent (i.e., the correlation coefficient between any pair of properties was |r|<0.75). As illustrated in Fig. 2, the first correlated group includes Fe, Yield, Ash, Ca, C, FixedC,

Conclusion

Statistical results demonstrate that arbitrary choices of starting biomass or peak pyrolysis temperature are unlikely to produce biochar with prescribed physico-chemical properties. Generalized Linear Models were used to quantify the combined effect that starting biomass and peak temperature has on different biochar properties. These properties are typically non-Gaussian and exhibit non-linear dependence on the two predictor variables. Proper description of most biochar properties by GLMs

Acknowledgements

The authors thank Dr. D.R. Fuka and Dr. M.P. Allan for advise on programing and statistical methods, and Dr. S. Sohi and Dr. O. Mašek for valuable discussions. This study was financed in part by the Teresa Heinz Foundation for Environmental Research and Project Unicorn. V.L. Morales acknowledges support from Marie Curie International Incoming Fellowships (FP7-PEOPLE-2012-SoilArchnAg).

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