Generalizability of empirical correlations for predicting higher heating values of biomass

Daskin, Mahmut; Erdoğan, Ahmet; Güleç, Fatih; Okolie, Jude A.

Abstract

Designing efficient biomass energy systems requires a thorough understanding of the physicochemical, thermodynamic, and physical properties of biomass. One crucial parameter in assessing biomass energy potential is the higher heating value (HHV), which quantifies its energy content. Conventionally, HHV is determined through bomb calorimetry, but this method is limited by factors such as time, accessibility, and cost. To overcome these limitations, researchers have proposed a diverse range of empirical correlations and machine-learning approaches to predict the HHV of biomass based on proximate and ultimate analysis results. The novelty of this research is to explore the universal applicability of the developed empirical correlations for predicting the Higher Heating Value (HHV) of biomass. To identify the best empirical correlations, nearly 400 different biomass feedstocks were comprehensively tested with 45 different empirical correlations developed to use ultimate analysis (21 different empirical correlations), proximate analysis (16 different empirical correlations) and combined ultimate-proximate analysis (8 different empirical correlations) data of these biomass feedstocks. A quantitative and statistical analysis was conducted to assess the performance of these empirical correlations and their applicability to diverse biomass types. The results demonstrated that the empirical correlations utilizing ultimate analysis data provided more accurate predictions of HHV compared to those based on proximate analysis or combined data. Two specific empirical correlations including coefficients for each element (C, H, N) and their interactions (C*H) demonstrate the best HHV prediction with the lowest MAE (~0.49), RMSE (~0.64), and MAPE (~2.70%). Furthermore, some other empirical correlations with carbon content being the major determinant also provide good HHV prediction from a statistical point of view; MAE (~0.5–0.8), RMSE (~0.6–0.9), and MAPE (~2.8–3.8%).

• Journal Article Type: Article
• Acceptance Date: Mar 11, 2024
• Online Publication Date: Apr 11, 2024
• Publication Date: 2024
• Deposit Date: May 2, 2024
• Publicly Available Date: May 2, 2024
• Journal: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
• Print ISSN: 1556-7036
• Electronic ISSN: 1556-7230
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Issue: 1
• Pages: 5434-5450
• DOI: https://doi.org/10.1080/15567036.2024.2332472
• Keywords: Biomass; higher heating value; ultimate analysis; proximate analysis; HHV prediction
• Public URL: https://nottingham-repository.worktribe.com/output/33567225
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/15567036.2024.2332472