Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments

Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments

Anaerobic co-digestion of food waste and agricultural residues:an overview of feedstock properties and the impact of biochar addition

Large amount of food and agricultural residues are discharged as municipal wastes. These biogenic waste possess high sugar content and are being dumped into landfills or incinerated, creating severe environmental challenges. Anaerobic co-digestion (AcD) of food waste and agricultural residues provides a sustainable valorisation route for biogas production. Biochar addition promotes the microbial activity, electrical conductivity, and molar interactions in the anaerobic digester. The present review presents an overview of the influence of biochar on the product yield during AcD. An overview of different classification of food waste and agricultural residues is presented. In addition, studies related to the application of biochar to enhance AcD were critically reviewed as well as the future outlook. The conducted studies revealed that the addition of biochar to AcD process can mitigate the buffering capacity and toxic process inhibitors faced in AcD, and ultimately enhance biogas yields, shortening the lag-phase and biodegrading running time. Biochar has a unique surface functional groups that can be modified by functionalization or by adjusting the pyrolysis temperature for optimal efficiency of specific co-substrate combinations of feedstocks. In AcD process, engineered biochar can be directed to specifically adsorb precise indirect (limonene) or direct (NH3, CO2) inhibitors for optimal process efficiency and methane production based on active surface functional groups, alkalinity of the material and hydrophobicity. Hence, biochar enhanced with the right pore sizing and pH can offset AcD limitations and improve process efficiency. The presented review provide an in- depth understand on the influence of biochar on product yield during AcD process