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Energies
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High Value-Added Utilization of Biomass and Biofuels
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High Value-Added Utilization of Biomass and Biofuels

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

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 2150

Special Issue Editors

Dr. Andrzej Bryś

E-Mail Website
Guest Editor
Department of Fundamental Engineering and Energetics, Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska St. 164, 02-776 Warsaw, Poland
Interests: the use of biomass for energy purposes; biomass conversion; the drying of biological materials; combustion; combustion equipment; heat and mass transfers; and liquid biofuels and their application, design and construction
Dr. Weronika Tulej

E-Mail Website
Guest Editor
Department of Fundamental Engineering and Energetics, Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska St. 164, 02-776 Warsaw, Poland
Interests: the drying and quality of the dried material; the drying and preparation of biomass for energy purposes, biomass conversion, combustion, heat and mass transfer
Prof. Dr. Joanna Bryś

E-Mail Website
Guest Editor
Department of Chemistry, Institute of Food Sciences, Warsaw University of Life Sciences (WULS—SGGW), Nowoursynowska St. 166, 02-787 Warsaw, Poland
Interests: lipids; food analysis; food science and technology; fat
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern world, problems related to energy, its acquisition or conversion are highly important. The increase in the global population, the growing demand of societies for energy and the reduction in fossil fuel resources make it necessary to identify new energy sources, in particular renewable energy, which emits the least pollution in the natural environment. New biomass and liquid biofuel conversion technologies are as important as the diversification of new energy sources. New technologies contribute to a more efficient use of biofuels, as well as make the obtained energy less harmful to the environment. All processes related to refining biofuels result in a significant increase in their value as fuel and open new possibilities for their use in various industries, as well as by individual consumers.

This Special Issue aims to present and disseminate the latest developments related to, i.a., enrichment, conversion processes, modeling, shaping, the use of biofuels, as well as devices in which biofuels are used, along with determining the impact on the surrounding environment.

Topics of interest for publication include, but are not limited to:

  • Determination of the physicochemical properties of biomass intended for high-quality biofuels.
  • All aspects related to biofuel conversion—modeling, theoretical and practical analyses; computer simulations.
  • The enrichment of biomass, forming, thickening, drying and grinding.
  • Processes of the pyrolysis, gasification and torrefaction of biomass.
  • The fermentation process, biogas plants and biogas.
  • Liquid biofuel, esters and ethanol.
  • The use of agricultural and forestry waste for energy purposes.
  • The use of food industry waste for energy purposes.
  • Designing a line for the production of biofuels or its individual elements.
  • The modeling of processes related to energy production from biofuels.
  • Technical and economic analyses; determination of efficiency and environmental impact.
  • Analysis of the impact of the use of biofuels on the environment.
  • Thermal and thermogravimetric analyses.
  • Designing biomass burning equipment.
  • Analysis of the composition of flue gases resulting from the combustion of high-quality fuels.

Dr. Andrzej Bryś
Dr. Weronika Tulej
Prof. Dr. Joanna Bryś
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
  • biomass conversion
  • combustion
  • waste management
  • computer modeling and simulation

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Published Papers (1 paper)

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Research

20 pages, 5196 KiB  
Article
Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning
by Inna Tryhuba, Anatoliy Tryhuba, Taras Hutsol, Agata Cieszewska, Oleh Andrushkiv, Szymon Glowacki, Andrzej Bryś, Sergii Slobodian, Weronika Tulej and Mariusz Sojak
Energies 2024, 17(7), 1786; https://doi.org/10.3390/en17071786 - 8 Apr 2024
Cited by 3 | Viewed by 1572
Abstract
The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient [...] Read more.
The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages. Full article
(This article belongs to the Special Issue High Value-Added Utilization of Biomass and Biofuels)
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