Statistical Modeling to Analyze the Performance and Carbon Content of Agro-industrial Biomasses
Abstract
In agroindustry, a significant amount of waste is generated, which can be treated using various thermochemical technologies such as hydrothermal carbonization. Biomass yield and carbon content are two of the most common characteristics studied within the processes generated by these thermochemical technologies, and chemical analyses and statistical techniques are usually employed. These techniques include t-student tests, analysis of variance, or response surface models to optimize or estimate the effects of certain factors. Unlike research conducted in this field of chemistry, this study aimed to introduce alternative statistical techniques for modeling such data, proposing diverse analysis strategies to enhance understanding of the studied phenomena. To achieve this, the statistical modeling of two datasets derived from apple pomace and blueberries was presented, encompassing four factors (time, humidity, power, temperature) and two separate responses (carbon content and process yield). This study reveals that time, temperature, and humidity collectively affect process yield and carbon content in apple biomass. It is concluded that techniques like generalized linear models with beta response and generalized additive models for location, scale, and shape provide a deeper understanding of the phenomenon of interest and the ability to estimate the effects of studied factors on responses that do not naturally follow a normal distribution.
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