Reverse engineering of biochar. (May 2015)

• Record Type: Journal Article
• Authors: Morales, Verónica L.; Pérez-Reche, Francisco J.; Hapca, Simona M.; Hanley, Kelly L.; Lehmann, Johannes; Zhang, Wei
• Is Part Of: Bioresource technology. Volume 183(2015)
• Page Start: 163
• Page End: 174
• Publication Date: 2015-05
• Subjects: Physico-chemical properties -- Slow-pyrolysis -- Correlation networks -- Generalized Linear Models -- Web-tool; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2015.02.043
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: 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.