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Forests
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Mathematical Modeling in Wood Processing
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Mathematical Modeling in Wood Processing

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Wood Science and Forest Products".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 708

Special Issue Editor

Dr. Chandan Kumar

E-Mail Website
Guest Editor
Agri-Science Queensland, Department of Agriculture and Fisheries, Salisbury Research Facility, 50 Evans Road (cnr Nettleton Cres), Salisbury, QLD 4107, Australia
Interests: mathematical model; optimisation; simulation; prediction; efficiency; improvement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wood is gaining recognition as a sustainable building material because it is a natural and renewable resource and has low carbon impact and low embodied energy. There have been significant advancements made in recent years regarding the application of mathematical models in wood processing, such as for the allocation of resources, wood drying, process optimization, and the design of engineered wood products. Mathematical models are helping to optimize wood processes, reducing the need for costly experiments. This Special Issue explores mathematical modeling in wood processing, and addresses the following topics:

  • Drying processes;
  • Heat and mass transfer;
  • Treatment;
  • Assessing forest value;
  • Enhancing sawing process;
  • Reducing energy use;
  • Improving product design and testing;
  • Maintaining quality and reducing waste;
  • Streamlining wood grading.

Dr. Chandan Kumar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical model
  • optimisation
  • simulation
  • prediction
  • efficiency
  • improvement

Published Papers (1 paper)

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Research

15 pages, 2183 KiB  
Article
Prediction of Dielectric Loss Factor of Wood in Radio Frequency Heating and Drying Based on IPOA-BP Modeling
by Jingying Gao, Wei Wang and Zening Qu
Forests 2024, 15(7), 1187; https://doi.org/10.3390/f15071187 - 9 Jul 2024
Viewed by 366
Abstract
In this paper, an Improved Pelican Optimization Algorithm (IPOA) was proposed to optimize a BP neural network model to predict the dielectric loss factor of wood in the RF heating and drying process. The neural network model was trained and optimized using MATLAB [...] Read more.
In this paper, an Improved Pelican Optimization Algorithm (IPOA) was proposed to optimize a BP neural network model to predict the dielectric loss factor of wood in the RF heating and drying process. The neural network model was trained and optimized using MATLAB 2022b software, and the prediction results of the BP neural network with POA-BP and IPOA-BP models were compared. The results show that the IPOA-optimized BP neural network model is more accurate than the traditional BP neural network model. After the BP neural network model with IPOA optimization was used to predict the dielectric loss factor of wood, the value increased by 4.3%, the MAE decreased by 68%, and the RMSE decreased by 67%. The results provided by the study using the IPOA-BP model show that the prediction of the dielectric loss factor of wood under different macroscopic conditions in radio frequency heating and drying of wood can be realized without the need for highly costly and prolonged experimental studies. Full article
(This article belongs to the Special Issue Mathematical Modeling in Wood Processing)
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