Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract

The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method.

Predictive Modelling of Students’ University English Language Performance by Classification with Gaussian Process Models

“…Gaussian process models offer a non-parametric method of modelling complex interactions without predetermined hypotheses, in contrast to typical regression or classification models that make strict assumptions about data distributions and functional forms. Gaussian process model categorization allows for the uncertainty and variability that are inherent in predicting student performance, allowing for more accurate and detailed forecasts [13,14]. This is achieved by treating predictions as distributions across probable outcomes.…”