Abstract
Abstract: In this study, several linear regression models were tested to predict the cumulative 30-day methane yield produced in mesophilic solid-state anaerobic digestion, employing diverse lignocellulosic biomass sources. Data collected from 13 studies were utilized, totalizing 86 experimental points, divided into regression and validation. Models containing higher order terms, the inverse of variables and interactions among all eleven input variables were tested. Simple linear models utilizing a single variable were unable to describe the methane production, giving an R² lower than 0.37. However, combinations of multiple variables and its inverses as only independent variable permitted an increase in simple linear models predictive capacity up to 63% of experimental variability. Higher order models presented an improvement in predictive quality: for a fourth-order multiple linear model, a validation R² of 0.8329 was achieved. In view of the obtained results, the proposed linear regression models consist in an attractive tool to propose experimental routines and to investigate new biomass sources for methane production using solid-state anaerobic digestion, significantly reducing time and cost requirements to experiments’ execution.
Highlights
Anaerobic digestion (AD) is a natural process, in which organic matter is converted by microbes into gases which present energetic potential, such as methane (CH4) and hydrogen (H2), under oxygen-free conditions (Baêta et al 2016)
Linear regression models are relevant tools to propose experimental routines of little understood systems, since these models do not require a deep knowledge of the involved phenomena in the processes, which applies for solid-state anaerobic digestion employing lignocellulosic biomass
In studies performed by Gunaseelan (2007), methane yield produced by liquid anaerobic digestion (L-AD) from different biomass sources is predicted using linear regression models with basis on feedstock chemical composition
Summary
Biogas is produced by means of liquid anaerobic digestion (L-AD). recent studies have highlighted the alternative use of solidstate anaerobic digestion (SS-AD), which occurs for solid concentration above 15% (Ge et al 2016, Xu et al 2016, Pezzolla et al 2017). Linear regression models are relevant tools to propose experimental routines of little understood systems, since these models do not require a deep knowledge of the involved phenomena in the processes, which applies for solid-state anaerobic digestion employing lignocellulosic biomass. In studies performed by Gunaseelan (2007), methane yield produced by L-AD from different biomass sources (fruit and vegetable solid wastes, sorghum and napiergrass) is predicted using linear regression models with basis on feedstock chemical composition. The influence of terms containing interactions between the explanatory variables, as well as quadratic and cubic terms, was investigated only for terms containing feedstock-to-effluent ratio (F/E) This approach might have disconsidered relevant information, having in sight the probable non-linear relation between the input variables and methane yield, in addition to interactions among variables other than F/E ratio. A data set containing more experimental points, comparing to previous publications, was used for models’ creation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
