Reverse engineering of biochar

Veronica L. Morales; Francisco J. Perez-Reche; Simona M. Hapca; Kelly L. Hanley; Johannes Lehmann; Wei Zhang

doi:10.1016/j.biortech.2015.02.043

Reverse engineering of biochar

Veronica L. Morales^*, Francisco J. Perez-Reche, Simona M. Hapca, Kelly L. Hanley, Johannes Lehmann, Wei Zhang

^*Corresponding author for this work

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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. (C) 2015 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	163-174
Number of pages	12
Journal	Bioresource Technology
Volume	183
Early online date	18 Feb 2015
DOIs	https://doi.org/10.1016/j.biortech.2015.02.043
Publication status	Published - May 2015

Bibliographical note

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)

Keywords

physico-chemical properties
slow-pyrolysis
correlation networks
generalized linear models
web-tool
contaminated soils
organic-compounds
black carbon
charcoal
manure
temperature
remediation
mechanisms
adsorption
fertilizer

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 3.9 MB

Francisco Perez-Reche
- School of Natural & Computing Sciences, Physics - Reader
- Institute for Complex Systems and Mathematical Biology (ICSMB)
Person: Academic

Cite this

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title = "Reverse engineering of 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. (C) 2015 Elsevier Ltd. All rights reserved.",

keywords = "physico-chemical properties, slow-pyrolysis, correlation networks, generalized linear models, web-tool, contaminated soils, organic-compounds, black carbon, charcoal, manure, temperature, remediation, mechanisms, adsorption, fertilizer",

author = "Morales, {Veronica L.} and Perez-Reche, {Francisco J.} and Hapca, {Simona M.} and Hanley, {Kelly L.} and Johannes Lehmann and Wei Zhang",

note = "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. Ma{\v s}ek 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)",

year = "2015",

month = may,

doi = "10.1016/j.biortech.2015.02.043",

language = "English",

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AU - Morales, Veronica L.

AU - Perez-Reche, Francisco J.

AU - Hapca, Simona M.

AU - Hanley, Kelly L.

AU - Lehmann, Johannes

AU - Zhang, Wei

N1 - 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)

PY - 2015/5

Y1 - 2015/5

N2 - 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. (C) 2015 Elsevier Ltd. All rights reserved.

AB - 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. (C) 2015 Elsevier Ltd. All rights reserved.

KW - physico-chemical properties

KW - slow-pyrolysis

KW - correlation networks

KW - generalized linear models

KW - web-tool

KW - contaminated soils

KW - organic-compounds

KW - black carbon

KW - charcoal

KW - manure

KW - temperature

KW - remediation

KW - mechanisms

KW - adsorption

KW - fertilizer

U2 - 10.1016/j.biortech.2015.02.043

DO - 10.1016/j.biortech.2015.02.043

M3 - Article

SN - 0960-8524

VL - 183

SP - 163

EP - 174

JO - Bioresource Technology

JF - Bioresource Technology

ER -

Reverse engineering of biochar

Abstract

Bibliographical note

Keywords

UN SDGs

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Francisco Perez-Reche

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