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Computational Intelligence for Sustainable Development
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Computational Intelligence for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7129

Special Issue Editor

Prof. Dr. Sławomir Francik

E-Mail Website
Guest Editor
Department of Mechanical Engineering and Agrophysics, University of Agriculture in Krakow, 31-120 Kraków, Poland
Interests: artificial neural network; multi-criteria decision making; multi-criteria evaluation of technical systems; mechanical properties of biomaterials; biomass densification; agriculture engineering

Special Issue Information

Dear Colleagues,

Classic methods of computational intelligence (CI), such as artificial neural networks, fuzzy systems, and evolutionary computing, have been used by scientists for several decades, and the number of their applications increases every year. These methods have been and are used in virtually all areas of scientific research. There are also new methods of CI.

One of the tasks of modern science is to provide solutions that enable sustainable development in all its aspects. At the UN Sustainable Development Summit in 2015, the 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals was adopted.

The aim of this Special Issue is to collect the results of scientific research using CI and make them available to as many interested people as possible. The collected results are those which, using the methods of CI, enable the achievement of the 17 Sustainable Development Goals. Scientific articles can be either of a research or review nature.

The topics of the articles should mainly concern the following sustainable development goals:

Goal 2) Zero hunger (End hunger, achieve food security and improved nutrition and promote sustainable agriculture)

Goal 3) Good health and well-being (Ensure healthy lives and promote well-being for all at all ages)

Goal 6) Clean water and sanitation (Ensure availability and sustainable management of water and sanitation for all)

Goal 7) Affordable and clean energy (Ensure access to affordable, reliable, sustainable, and modern energy for all)

Goal 9) Industry, innovation, and infrastructure (Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation)

Goal 11) Sustainable cities and communities (Make cities and human settlements inclusive, safe, resilien,t and sustainable)

Goal 12) Responsible consumption and production (Ensure sustainable consumption and production patterns)

Goal 14) Life below water (Conserve and sustainably use the oceans, seas, and marine resources for

sustainable development)

Goal 15) Life and land (Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss)

Contributions containing both theoretical and practical results obtained in this area are welcome.

Prof. Sławomir Francik
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. Sustainability 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

  • Computational intelligence
  • Artificial intelligence
  • Artificial neural network
  • Fuzzy theory
  • Evolutionary computation
  • Support vector machine
  • Soft computing
  • Machine learning
  • Deep learning
  • Data mining
  • Decision support
  • Operations research
  • Modeling
  • Simulation
  • Optimization
  • Sustainable manufacturing
  • Sustainability in production
  • Smart farming
  • Smart agriculture
  • Precision agriculture
  • Forest management
  • Precision forestry
  • Renewable energy sources

Published Papers (3 papers)

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Research

26 pages, 9506 KiB  
Article
The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus
by Sławomir Francik, Bogusława Łapczyńska-Kordon, Norbert Pedryc, Wojciech Szewczyk, Renata Francik and Zbigniew Ślipek
Sustainability 2022, 14(5), 3062; https://doi.org/10.3390/su14053062 - 6 Mar 2022
Cited by 1 | Viewed by 1368
Abstract
The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the [...] Read more.
The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models. Full article
(This article belongs to the Special Issue Computational Intelligence for Sustainable Development)
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15 pages, 3286 KiB  
Article
Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry
by Abdullah K. Alanazi, Seyed Mehdi Alizadeh, Karina Shamilyevna Nurgalieva, John William Grimaldo Guerrero, Hala M. Abo-Dief, Ehsan Eftekhari-Zadeh, Ehsan Nazemi and Igor M. Narozhnyy
Sustainability 2021, 13(24), 13622; https://doi.org/10.3390/su132413622 - 9 Dec 2021
Cited by 11 | Viewed by 3026
Abstract
To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly [...] Read more.
To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage. Full article
(This article belongs to the Special Issue Computational Intelligence for Sustainable Development)
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27 pages, 345 KiB  
Article
Sustainable Development Goals Analysis with Ordered Weighted Average Operators
by Betzabe Ruiz-Morales, Irma Cristina Espitia-Moreno, Victor G. Alfaro-Garcia and Ernesto Leon-Castro
Sustainability 2021, 13(9), 5240; https://doi.org/10.3390/su13095240 - 7 May 2021
Cited by 5 | Viewed by 1777
Abstract
The present research proposes a new method to analyze the sustainable development goals (SDGs) index using ordered weighted average (OWA) operators. To develop this method, five experts evaluated and designated the relative importance of each of the 17 SDGs defined by the United [...] Read more.
The present research proposes a new method to analyze the sustainable development goals (SDGs) index using ordered weighted average (OWA) operators. To develop this method, five experts evaluated and designated the relative importance of each of the 17 SDGs defined by the United Nations (UN), and with the use of the OWA and prioritized OWA (POWA) operators, rankings were generated. With the results, it is possible to visualize that the ranking of countries can change depending on the weights related to each SDG because the OWA and POWA operator methods can capture the uncertainty of the phenomenon. Full article
(This article belongs to the Special Issue Computational Intelligence for Sustainable Development)
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