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Applied Sciences
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Application of Biomass Energy Technology
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Application of Biomass Energy Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2356

Special Issue Editors

Dr. Georgiana Moiceanu

E-Mail Website
Guest Editor
Department of Entrepreneurship and Management, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
Interests: renewable energy; biogas; energy engineering; renewable energy technologies; biomass conversion; gasification; energy modeling; pyrolysis; energy; biomass
Dr. Irina Aura Istrate

E-Mail Website
Guest Editor
Department of Biotechnical Systems, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
Interests: renewable energy; biogas; energy engineering; renewable energy technologies; biomass conversion; gasification; energy modeling; pyrolysis; energy; biomass; machine learning

Special Issue Information

Dear Colleagues,

Biomass energy technology plays an increasingly pivotal role in the realm of sustainable energy solutions. This Special Issue aims to deliver a comprehensive examination of biomass energy technology, covering its fundamental aspects, applications, advantages, integrated strategies, and future research avenues. Biomass energy technology is primarily concerned with the conversion of organic materials, such as plant matter and waste, into valuable energy resources. This multifaceted field encompasses diverse technologies, including biomass combustion, gasification, anaerobic digestion, and biofuel production. The adaptability of biomass as an energy source underscores its essential role in the transition towards cleaner and renewable energy solutions.

Our scientific journal is devoted to promoting a profound understanding of and progress in biomass energy technology. Our scope includes, but is not confined to, investigations into biomass feedstock selection, conversion processes, efficiency enhancements, environmental implications, economic assessments, policy considerations, and emerging trends. Additionally, the carbon cycle, the net energy efficiency of bioenergy systems, sustainability evaluation, and biodiversity concerns are all related to biomass and the environment. In addressing the escalating global energy demand and environmental challenges, forthcoming research in biomass energy technology should concentrate on the following focal points:

  • Innovations in conversion processes to maximize energy output and diminish emissions.
  • Sustainable management of feedstock resources and their availability.
  • Integration with other renewable energy sources to fortify a resilient energy grid.
  • Adoption of circular economy principles for waste-to-energy solutions.
  • Technological advancements catering to small-scale, decentralized biomass systems.
  • Holistic evaluations of socio-economic and environmental impacts to uphold sustainability.
  • The production of feedstock in connection to forestry, silviculture, agronomy, and agriculture.

Biomass energy technology is amenable to integration within the broader energy landscape, synergizing with other renewable sources like wind and solar. It is our hope that through the dissemination of knowledge and research in this field, we can collectively accelerate the adoption and optimization of biomass energy technologies for a cleaner and more sustainable future.

Dr. Georgiana Moiceanu
Dr. Irina Aura Istrate
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. Applied Sciences 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 2400 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

  • biomaterials
  • bioenergy
  • energy efficiency
  • bioeconomy
  • green fuels
  • feed production
  • ecosystem
  • renewables
  • sustainable fuels
  • plant biotechnology for fuel crops
  • biorefining
  • machine learning for energy estimation
  • biofuel process
  • bioethanol
  • biodiesel
  • biopolymers
  • chemical engineering in biofuels
  • hydrolysis of biomass to sugars
  • gasification
  • pyrolysis
  • properties of biofuels
  • energy content of fuel drops
  • energy content of biofuels
  • bioproducts
  • green chemistry
  • environment/environmental
  • agricultural waste
  • green household waste
  • biorenewables
  • renewable energy
  • biobased fuels and chemicals
  • biomass pretreatment and fractionation
  • agriculture
  • agronomy
  • sustainability of energy
  • biorefinery
  • green raw materials
  • biofuel cells

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

19 pages, 8762 KiB  
Article
Influence of the Parameters of an Agricultural Biogas Plant on the Amount of Power Generated
by Maciej Kuboń, Zbigniew Skibko, Andrzej Borusiewicz, Wacław Romaniuk, Jakub St. Gajda, Olivia Kłosowska and Zbigniew Wasąg
Appl. Sci. 2024, 14(10), 4200; https://doi.org/10.3390/app14104200 - 15 May 2024
Viewed by 788
Abstract
Energy from biogas is widely available, inexpensive, and often contributes to waste management, making it one of the most promising renewable energy sources. The main factors influencing this process’ efficiency include the substrates’ chemical composition, temperature, and digester load. This paper presents the [...] Read more.
Energy from biogas is widely available, inexpensive, and often contributes to waste management, making it one of the most promising renewable energy sources. The main factors influencing this process’ efficiency include the substrates’ chemical composition, temperature, and digester load. This paper presents the possibilities offered by a biogas plant built at a farm specialising in dairy cows. The dependence of the power generated in the micro biogas plant on its technical parameters was analysed in detail. Studies carried out by the authors in an agricultural microgas plant (with an electrical output of 40 kW) have shown that they are designed to maintain continuous energy production, despite changing process parameters such as digester mass level, biogas height, temperature or slurry flow into the digester. However, from the point of view of the amount of electricity generated, changes would have to be made to the design of the biogas plant. Firstly, a more powerful generator would have to be installed to cover the electricity requirements of the equipment installed in the biogas plant so that power close to the rated capacity of the biogas plant is still sent to the grid. Secondly, replacing the two existing agitators of the digestion mass (9 kW each) with more agitators of lower power (e.g., four agitators of 4.5 kW each) would be necessary. These should be programmed so that one of the agitators operates at any given time (the operating time of a given agitator should depend on the composition of the digestate). Full article
(This article belongs to the Special Issue Application of Biomass Energy Technology)
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16 pages, 10493 KiB  
Article
Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters
by Onder Kabas, Uğur Ercan and Mirela Nicoleta Dinca
Appl. Sci. 2024, 14(2), 652; https://doi.org/10.3390/app14020652 - 12 Jan 2024
Cited by 2 | Viewed by 1082
Abstract
Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the [...] Read more.
Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy. Full article
(This article belongs to the Special Issue Application of Biomass Energy Technology)
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