Generalizability of empirical correlations for predicting higher heating values of biomass

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%).

KEYWORDS:

• Biomass
• higher heating value
• ultimate analysis
• proximate analysis
• HHV prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

CRediT authorship contribution statement

Mahmut Daskin: Formal analysis, Validation, Funding, Project administration, Visualization, Investigation, Writing – original draft, Writing – review & editing. Ahmet Erdoğan: Project administration, Formal analysis, Visualization, Investigation, Writing – original draft, Writing – review & editing. Fatih Güleç: Data collection, Project administration, Validation, Funding, Formal analysis, Visualization, Investigation, Writing – original draft, Writing – review & editing. Jude A. Okolie: Validation, Formal analysis, Visualization, Writing – review & editing.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567036.2024.2332472

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Mahmut Daskin

Dr Mahmut Daskin is a visiting academic at Cranfield University and an Assistant Professor in the Department of Mechanical Engineering at İnönü University. He obtained his Ph.D. in Mechanical Engineering from İnönü University in 2019. His areas of interest include Control Theory, Fuels and Combustion, System Modelling, CO2 Capture, Hydrogen Detection.

Ahmet Erdoğan

Dr Ahmet Erdoğan is a visiting academic at the University of Nottingham and an Assistant Professor in the Department of Mechanical Engineering at İnönü University. He obtained his Ph.D. in Mechanical Engineering from İnönü University in 2017. His areas of interest include Computational Fluid Dynamics, Refrigeration and Air Conditioning, and CO2 capture.

Fatih Güleç

Dr Fatih Gulec is an Assistant Professor in the Department of Chemical and Environmental Engineering at the University of Nottingham with expertise in energy and industrial decarbonisation. Dr gulec’s research encompasses (i) the synthesis and characterisation of advanced nanocomposites and their applications in chemical/calcium looping technologies, CO2 capture, negative emissions, energy storage and catalytic reactions and (ii) process integration/ intensification based on waste/biomass-to-energy via thermal conversion.

Jude A. Okolie

Dr. Jude A. Okolie is currently an Assistant Professor at the College of Engineering, University of Oklahoma and the co-founder of VComics. Dr. Okolie's research focuses on the thermochemical conversion of waste materials to green fuels and the subsequent utilization of hydrochar/biochars for environmental remediation. In addition, his research includes the application of process simulation and artificial intelligence/machine learning to address climate change, environmental pollution, and sustainable agriculture challenges. Dr. Okolie has been recognized for making significant contributions to the field of biomass to energy, reflected by his outstanding publication track record and peer recognition. He has been granted several prestigious local and international awards, including the George Ira Hanson Energy Award for his work on thermochemical hydrogen production and the University of Oklahoma, Alternative textbook grant. Dr. Okolie is the author of Biofuels book and also an editorial board member of the societal impact journal. He is a strong believer that anyone could be an engineer and thrive with the right support system.