Estimation of Several Wood Biomass Calorific Values from Their Proximate Analysis Based on Artificial Neural Networks.

I Ketut Gary Devara, Windy Ayu Lestari, Uma Maheshwera Reddy Paturi, Jun Hong Park, Nagireddy Gari Subba Reddy

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Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

I Ketut Gary DevaraDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0002-0672-2843

Windy Ayu LestariDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea.

Uma Maheshwera Reddy PaturiDepartment of Mechanical Engineering, CVR College of Engineering, Hyderabad 501510, Telangana, India.ORCID 0000-0001-5843-466X

Jun Hong ParkDepartment of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0001-5138-1622

Nagireddy Gari Subba ReddySchool of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0003-4206-4515

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate estimation of the higher heating value (HHV) of wood biomass is essential to evaluating the latter's energy potential as a renewable energy material. This study proposes an Artificial Neural Network (ANN) model to predict the HHV by using proximate analysis parameters-moisture, volatile matter, ash, and fixed carbon. A dataset of 252 samples (177 for training and 75 for testing), sourced from the Phyllis database, which compiles the physicochemical properties of lignocellulosic biomass and related feedstocks, was used for model development. Various ANN architectures were explored, including one to three hidden layers with 1 to 20 neurons per layer. The best performance was achieved with the 4-11-11-11-1 architecture trained using the backpropagation algorithm, yielding an adjusted R

Indexed as

Artificial Neural Networkshigher heating valuepredictive modelproximate analysiswood biomass

Identifiers

PMID40731474

DOI10.3390/ma18143264

PMCPMC12298734

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.