The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

Abstract

Biomass is a carbon-neutral renewable energy resource. Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution. For time-and cost-saving, it is vital to establish predictive models to predict biochar properties. However, limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar. Therefore, the multi-linear regression (MLR) and the machine learning (ML) models were developed to predict the measured HHV of biochar from the experiment data of this study. In detail, 52 types of biochars were produced by pyrolysis from rice straw, pig manure, soybean straw, wood sawdust, sewage sludge, Chlorella Vulgaris, and their mixtures at the temperature ranging from 300 to 800°C. The results showed that the co-pyrolysis of the mixed biomass provided an alternative method to increase the yield of biochar production. The contents of ash, fixed carbon (FC), and C increased as the incremental pyrolysis temperature for most biochars. The Pearson correlation (r) and relative importance analysis between HHV values and the indicators derived from the proximate and ultimate analysis were carried out, and the measured HHV was used to train and test the MLR and the ML models. Besides, ML algorithms, including gradient boosted regression, random forest, and support vector machine, were also employed to develop more widely applicable models for predicting HHV of biochar from an expanded dataset (total 149 data points, including 97 data collected from the published literature). Results showed HHV had strong correlations (|r| > 0.9, p < 0.05) with ash, FC, and C. The MLR correlations based on either proximate or ultimate analysis showed acceptable prediction performance with test R 2 > 0.90. The ML models showed better performance with test R 2 around 0.95 (random forest) and 0.97-0.98 before and after adding extra data for model construction, respectively. Feature importance analysis of the ML models showed that ash and C were the most important inputs to predict biochar HHV.

Machine-learning-aided thermochemical treatment of biomass: a review

“…Biochar can also be used as a fuel for boilers; thus, calorific values such as HHV and LHV are also generally reported, and many equations are available for their calculation (Chen et al, 2022c). However, using biochar as a carbon material, replacing activated carbon and other materials, is more promising than using it as fuel.…”

“…Biomass is a kind of renewable energy with huge reserves, possessing environment-friendly and carbon-neutral properties. Using biomass is one of the effective ways to alleviate future energy needs while mitigating the greenhouse effect and environmental pollution crises (Leng et al, 2019b;Chen et al, 2022c). Thermochemical processes, such as torrefaction, pyrolysis, gasification, and hydrothermal carbonization/liquefaction/gasification, are important methods to valorize biomass or biowastes (carbon-neutral materials), such as forestry waste (wood, woody biomass, etc.…”

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

“…), pyrolysis parameters (temperature, residence time, etc. ), and additional treatments (Leng et al 2020b(Leng et al , 2021aChen et al 2022), that have considerable effects on these two properties as well as the yield of biochar (Yield-char). Therefore, engineering biochar with either high SSA or high N content with experimental screening method is difficult, not to mention mediating these two biochar characteristics with Yield-char together.…”

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

“…It is now feasible to create accurate prognostic models that take into consideration a variety of factors that affect the production of biochar by exploiting the power of ML. The selection of appropriate feedstocks, optimization of pyrolysis conditions, and promotion of sustainable biochar production techniques can all be facilitated by the capacity to estimate the properties and yield accurately from various biomass sources 45–47 . There is a need to compile and assess the ongoing research into using ML to forecast biochar yield, and this forms the motivation for the present review study.…”