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Energies
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Application of New Technologies in Bioenergy and Biofuel Conversion
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Application of New Technologies in Bioenergy and Biofuel Conversion

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A4: Bio-Energy".

Deadline for manuscript submissions: closed (29 August 2024) | Viewed by 1228

Special Issue Editors

Dr. Yunye Shi

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
Interests: biomass gasification; conventional and alternative energy systems; Zero+ energy buildings
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Albert Ratner

E-Mail Website
Guest Editor
Mechanical Engineering, University of Iowa, Iowa City, IA 52242, USA
Interests: combustion instability; laser diagnostics; fuel sprays; real fuel and crude oil behavior; biomass gasification and combustion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of bioenergy and biofuel conversion technology will play a critical role in the production and utilization of renewable and sustainable energy sources in the future. However, the complex and heterogeneous nature of bioenergy and biofuel conversion systems makes it difficult to build models based on experience or theory for accurate predictions. Recent advancements in data science and machine learning (ML) can provide new opportunities. ML is among the fastest growing applications, with practical applications in the health industry, social media, retail sector, financial sector, travel industry, etc. More research is needed to utilize ML in bioenergy and biofuel conversion research, especially in the areas of prediction accuracy, the validity domain and model reliability. This Special Issue will provide an overview of the most recent advancements in applying ML tools to bioenergy and biofuel conversion research in diverse areas.

Potential topics include, but are not limited to:

  • Machine learning in biomass characterization;
  • Machine learning in biomass pretreatment;
  • Machine learning in biomass thermochemical conversion;
  • Machine learning in biomass biochemical conversion;
  • Machine learning in bioenergy and biofuel conversion supply chain management.

Dr. Yunye Shi
Prof. Dr. Albert Ratner
Guest Editors

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. Energies is an international peer-reviewed open access semimonthly 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

  • biomass
  • biofuels conversion
  • machine learning

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Published Papers (1 paper)

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Research

16 pages, 10398 KiB  
Article
U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes
by Lianming Li, Zhiwei Wang and Defeng He
Energies 2024, 17(20), 5143; https://doi.org/10.3390/en17205143 - 16 Oct 2024
Viewed by 832
Abstract
This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and [...] Read more.
This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%. Full article
(This article belongs to the Special Issue Application of New Technologies in Bioenergy and Biofuel Conversion)
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