Special Issue "Supply Chain Management for Bioenergy and Bioresources"

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

Deadline for manuscript submissions: closed (31 March 2020).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dionysis Bochtis
Website
Guest Editor
Institute for Bio-economy and Agri-Technology (IBO), Centre for Research and Technology – Hellas (CERTH), 38333 Volos, Greece
Interests: operations management; supply chain automation; agri-business; ICT-agri; bioeconomy
Special Issues and Collections in MDPI journals
Prof. Dr. Charisios Achillas
Website
Guest Editor
Department of Logistics, Technical Educational Institute of Central Macedonia, Serres, Kilkis, Katerini, Greece
Interests: reverse logistics; circular economy; industrial symbiosis; sustainable development; biomass supply chain; bio-energy and bio-recourses
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern world, the competitiveness of bioenergy and/or bioresources-related activities heavily depend on the effectiveness of Supply Chain Management. A large number of multi-disciplinary topics are involved in the bioresources and bioenergy production fields. Although the technical issues related to the topic have been well discussed and do not represent major barriers, supply chain management issues, such as design of the network, collection, storage, or transportation of bioresources, are still considered as fundamental questions that need to be answered towards optimal exploitation of bioenergy and bioresources. Moreover, modelling of material and energy flows; the identification of the dynamic character of the supply chains; available reverse logistics (waste management) alternatives; the economic, social, and environmental sustainability of bioresource supply chains; and novelty in applied business models and decision support framework towards efficient supply chain management for bioenergy and bioresources present critical operational sustainability issues and business-making potential.

This Special Issue seeks to contribute to the bioenergy and bioresources agenda through enhanced scientific and multi-disciplinary knowledge to boost the performance efficiency of supply chain management and support the decision-making processes of stakeholders. To that end, we therefore invite papers on innovative technical developments, reviews, case studies, and analytical and assessment papers from different disciplines, that are relevant to supply chain management for bioenergy and bioresources.

Prof. Dr. Dionysis Bochtis
Prof. Dr. Charisios Achillas
Guest Editors

Manuscript Submission Information

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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 2000 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 supply chain
  • bio-energy
  • bio-recourses
  • reverse logistics
  • sustainability
  • renewable energy
  • operations management
  • decision support systems

Published Papers (7 papers)

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Research

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Open AccessArticle
Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System
Energies 2020, 13(9), 2317; https://doi.org/10.3390/en13092317 - 07 May 2020
Cited by 1
Abstract
(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies [...] Read more.
(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand. Full article
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Open AccessArticle
Decision Support System to Implement Units of Alternative Biowaste Treatment for Producing Bioenergy and Boosting Local Bioeconomy
Energies 2020, 13(9), 2306; https://doi.org/10.3390/en13092306 - 06 May 2020
Cited by 8
Abstract
Lately, the model of circular economy has gained worldwide interest. Within its concept, waste is viewed as a beneficial resource that needs to be re-introduced in the supply chains, which also requires the use of raw materials, energy, and water to be minimized. [...] Read more.
Lately, the model of circular economy has gained worldwide interest. Within its concept, waste is viewed as a beneficial resource that needs to be re-introduced in the supply chains, which also requires the use of raw materials, energy, and water to be minimized. Undeniably, a strong link exists between the bioeconomy, circular economy, bioproducts, and bioenergy. In this light, in order to promote a circular economy, a range of alternative options and technologies for biowaste exploitation are currently available. In this paper, we propose a generic methodological scheme for the development of small, medium, or large-scale units of alternative biowaste treatment, with an emphasis on the production of bioenergy and other bioproducts. With the use of multi-criteria decision analysis, the model simultaneously considers environmental, economic, and social criteria to support robust decision-making. In order to validate the methodology, the latter was demonstrated in a real-world case study for the development of a facility in the region of Serres, Greece. Based on the proposed methodological scheme, the optimal location of the facility was selected, based on its excellent assessment in criteria related to environmental performance, financial considerations, and local acceptance. Moreover, anaerobic digestion of agricultural residues, together with farming and livestock wastes, was recommended in order to produce bioenergy and bioproducts. Full article
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Open AccessArticle
Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique
Energies 2020, 13(6), 1427; https://doi.org/10.3390/en13061427 - 19 Mar 2020
Cited by 5
Abstract
Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to [...] Read more.
Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country’s certain socio-economic and political conditions. In Brazil, the adoption of photovoltaic solar energy has been motivated not only by the energy matrix diversification but also from the shortages, problems, and barriers that the Brazilian energy sector has faced, lately. However, PSE development is affected by various factors with high uncertainty, such as political, social, economic, and environmental, that include critical operational sustainability issues. Thus, an elaborate modelling of energy management and a well-structured decision support process are needed to enhance the performance efficiency of Brazilian PSE supply chain management. This study focuses on the investigation of certain factors and their influence on the development of the Brazilian PSE with the help of Fuzzy Cognitive Maps. Fuzzy Cognitive Map is an established methodology for scenario analysis and management in diverse domains, inheriting the advancements of fuzzy logic and neural networks. In this context, a semi-quantitative model was designed with the help of various stakeholders from the specific energy domain and three plausible scenarios were conducted in order to support a decision-making process on PSE sector development and the country’s economic potential. The outcome of this analysis reveals that the development of the PSE sector in Brazil is mainly affected by economic and political factors. Full article
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Open AccessArticle
A Cloud-Based In-Field Fleet Coordination System for Multiple Operations
Energies 2020, 13(4), 775; https://doi.org/10.3390/en13040775 - 11 Feb 2020
Cited by 2
Abstract
In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its [...] Read more.
In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations—hoeing cultivation and seeding—as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode. Full article
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Open AccessArticle
Energy Footprint of Mechanized Agricultural Operations
Energies 2020, 13(3), 769; https://doi.org/10.3390/en13030769 - 10 Feb 2020
Cited by 2
Abstract
The calculation of the energy cost of a cultivation is a determining factor in the overall assessment of agricultural sustainability. Most studies mainly examine the entire life cycle of the operation, considering reference values and reference databases for the determination of the machinery [...] Read more.
The calculation of the energy cost of a cultivation is a determining factor in the overall assessment of agricultural sustainability. Most studies mainly examine the entire life cycle of the operation, considering reference values and reference databases for the determination of the machinery contribution to the overall energy balance. This study presents a modelling methodology for the precise calculation of the energy cost of performing an agricultural operation. The model incorporates operational management into the calculation, while simultaneously considering the commercially available machinery (implements and tractors). As a case study, the operation of tillage was used considering both primary and secondary tillage (moldboard plow and field cultivator, respectively). The results show the importance of including specific operation parameters and the available machinery as part of determining the accurate total energy consumption, even though the field size and available time do not have a significant effect. Full article
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Open AccessArticle
Dry Above Ground Biomass for a Soybean Crop Using an Empirical Model in Greece
Energies 2020, 13(1), 201; https://doi.org/10.3390/en13010201 - 01 Jan 2020
Abstract
A new empirical equation for the estimation of daily dry above ground biomass (D-AGB) for a hybrid of soybean (Glycine max L.) is proposed. This equation requires data for three crop dependent parameters; leaf area index, plant height, and cumulative crop evapotranspiration. [...] Read more.
A new empirical equation for the estimation of daily dry above ground biomass (D-AGB) for a hybrid of soybean (Glycine max L.) is proposed. This equation requires data for three crop dependent parameters; leaf area index, plant height, and cumulative crop evapotranspiration. Bilinear surface regression analysis was used in order to estimate the factors entering in the empirical model. For the calibration of the proposed model, data yielded from a well-watered soybean crop for the year 2015, in the experimental field (0.1 ha) of the agricultural University of Athens, were used as a reference. Verification of the validity of the model was obtained by using data from a 2014 cultivation period for well-watered soybean cultivation (100% of crop evapotranspiration water treatment), as well as data from three irrigation treatments (75%, 50%, 25% of crop evapotranspiration) for two cultivation periods (2014–2015). The proposed method for the estimation of D-AGB may be proven as a useful tool for estimations without using destructive sampling. Full article
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Review

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Open AccessReview
Green, Yellow, and Woody Biomass Supply-Chain Management: A Review
Energies 2019, 12(15), 3020; https://doi.org/10.3390/en12153020 - 06 Aug 2019
Cited by 6
Abstract
Various sources of biomass contribute significantly in energy production globally given a series of constraints in its primary production. Green biomass sources (such as perennial grasses), yellow biomass sources (such as crop residues), and woody biomass sources (such as willow) represent the three [...] Read more.
Various sources of biomass contribute significantly in energy production globally given a series of constraints in its primary production. Green biomass sources (such as perennial grasses), yellow biomass sources (such as crop residues), and woody biomass sources (such as willow) represent the three pillars in biomass production by crops. In this paper, we conducted a comprehensive review on research studies targeted to advancements at biomass supply-chain management in connection to these three types of biomass sources. A framework that classifies the works in problem-based and methodology-based approaches was followed. Results show the use of modern technological means and tools in current management-related problems. From the review, it is evident that the presented up-to-date trends on biomass supply-chain management and the potential for future advanced approach applications play a crucial role on business and sustainability efficiency of biomass supply chain. Full article
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