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Sustainable Engineering Applications of Artificial Intelligence
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Sustainable Engineering Applications of Artificial Intelligence

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

Deadline for manuscript submissions: closed (26 July 2024) | Viewed by 9409

Special Issue Editor

Dr. Hadi Bordbar

E-Mail Website
Guest Editor
1. School of Engineering, Aalto University, Espoo, Finland
2. Energy Recovery Inc., San Leandro, CA, USA
Interests: sustainable energy; energy conversion processes; artificial intelligence; engineering applications; fire protection engineering; heat and mass transfer; CFD; physics based modeling; HVAC analysis and design; energy efficiency; energy recovery; combustion; thermal design & control; spectral radiation; remote sensing; spectroscopy; flammability; air quality & control; aerospace engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 In recent years, significant technological advances in artificial intelligence have expanded the capabilities of computers and improved their development capabilities, increasing the sustainability, efficiency, process integration, and intensification of industrial systems. These advancements have opened up broad prospects for technological innovation and automation in various engineering fields. Therefore, the sustainable application of artificial intelligence technology is particularly important, which has also attracted the interest of many scholars. At the same time, there is more and more research on engineering applications based on artificial intelligence technology, and various opportunities and challenges have emerged. This Special Issue will present the most recent advancements in applying artificial intelligence (AI) techniques in sustainable engineering, including supervised and unsupervised learning and classification algorithms, recommenders, reinforcement learning, and deep learning, to various sustainable engineering applications, including but not limited to energy conversion processes, sustainable thermal design, control and management, rechargeable battery engineering, fire protection engineering, remote sensing, autonomous driving, sustainable aerospace engineering, combustion, heat and mass transfer, CFD, wildfire and fire protection engineering. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the application of artificial intelligence, data science, and machine learning in the following areas:·      

  • The design, control, and analysis of engineering systems;
  • Sustainable engineering applications;
  • Spectral radiation heat transfer;
  • Remote sensing and applications;
  • Fire protection engineering;
  • Power generation systems;
  • Enhancement of energy efficiency;
  • Interdisciplinary sustainability research;
  • Environmental technology;
  • HVAC design and analysis;
  • Air quality;
  • Turbomachinery;
  • Sustainable clean energy.

We look forward to receiving your contributions.

Dr. Hadi Bordbar
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

  • artificial intelligence
  • sustainable engineering applications
  • energy engineering
  • sustainable energy
  • machine learning
  • classification
  • computer vision
  • fire protection engineering
  • CFD
  • thermal control and design
  • HVAC
  • combustion
  • energy efficiency
  • environmental technology
  • remote sensing
  • spectral radiation
  • air quality
  • automation and control
  • power generation
  • clean energy

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Published Papers (4 papers)

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Research

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23 pages, 30954 KiB  
Article
A Deep CNN-Based Salinity and Freshwater Fish Identification and Classification Using Deep Learning and Machine Learning
by Wahidur Rahman, Mohammad Motiur Rahman, Md Ariful Islam Mozumder, Rashadul Islam Sumon, Samia Allaoua Chelloug, Rana Othman Alnashwan and Mohammed Saleh Ali Muthanna
Sustainability 2024, 16(18), 7933; https://doi.org/10.3390/su16187933 - 11 Sep 2024
Cited by 4 | Viewed by 2506
Abstract
Concerning the oversight and safeguarding of aquatic environments, it is necessary to ascertain the quantity of fish, their size, and their distribution. Many deep learning (DL), artificial intelligence (AI), and machine learning (ML) techniques have been developed to oversee and safeguard the fish [...] Read more.
Concerning the oversight and safeguarding of aquatic environments, it is necessary to ascertain the quantity of fish, their size, and their distribution. Many deep learning (DL), artificial intelligence (AI), and machine learning (ML) techniques have been developed to oversee and safeguard the fish species. Still, all the previous work had some limitations, such as a limited dataset, only binary class categorization, only employing one technique (ML/DL), etc. Therefore, in the proposed work, the authors develop an architecture that will eliminate all the limitations. Both DL and ML techniques were used in the suggested framework to identify and categorize multiple classes of the salinity and freshwater fish species. Two different datasets of fish images with thirteen fish species were employed in the current research. Seven CNN architectures were implemented to find out the important features of the fish images. Then, seven ML classifiers were utilized in the suggested work to identify the binary class (freshwater and salinity) of fish species. Following that, the multiclass classification of thirteen fish species was evaluated through the ML algorithms, where the present model diagnosed the freshwater or salinity fish in the specific fish species. To achieve the primary goals of the proposed study, several assessments of the experimental data are provided. The results of the investigation indicated that DenseNet121, EfficientNetB0, ResNet50, VGG16, and VGG19 architectures of the CNN with SVC ML technique achieved 100% accuracy, F1-score, precision, and recall for binary classification (freshwater/salinity) of fish images. Additionally, the ResNet50 architecture of the CNN with SVC ML technique achieved 98.06% and 100% accuracy for multiclass classification (freshwater and salinity fish species) of fish images. However, the proposed pipeline can be very effective in sustainable fish management in fish identification and classification. Full article
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)
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23 pages, 7756 KiB  
Article
Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning
by Xiangyuan Liu and Jianchun Fan
Sustainability 2024, 16(17), 7389; https://doi.org/10.3390/su16177389 - 27 Aug 2024
Cited by 1 | Viewed by 1162
Abstract
Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection [...] Read more.
Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability. Full article
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)
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20 pages, 7684 KiB  
Article
CFD—Assisted Expert System for N2-Controlled Atmosphere Process of Rice Storage Silos
by Phakkawat Angsrisuraporn, Chawit Samakkarn, Lertsak Lekawat, Sasathorn Singkhornart and Jatuporn Thongsri
Sustainability 2024, 16(5), 2187; https://doi.org/10.3390/su16052187 - 6 Mar 2024
Cited by 1 | Viewed by 1897
Abstract
Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to [...] Read more.
Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O2 concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD’s credibility. Significantly, CFD results revealed that the ES controlled N2 distribution throughout the silos and effectively reduced O2 concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP’s efficacy in controlling O2 concentration and insect extermination in the actual silos. The experimental results and owner’s feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES. Full article
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)
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Review

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41 pages, 2011 KiB  
Review
Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review
by Seyed Mohammad Shojaei, Reihaneh Aghamolaei and Mohammad Reza Ghaani
Sustainability 2024, 16(21), 9555; https://doi.org/10.3390/su16219555 - 2 Nov 2024
Viewed by 3000
Abstract
For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X [...] Read more.
For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas (PtG), Power-to-Liquid (PtL), and Power-to-Heat (PtH) systems. For example, deep reinforcement learning has improved real-time decision-making in PtG systems, reducing operational costs and improving grid stability. Additionally, predictive diagnostics powered by ML have increased system reliability by identifying early failures in critical components such as proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges such as data quality, real-time processing, and scalability remain, presenting future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors, such as heavy industry, transportation, and aviation, aligning with global sustainability goals. Full article
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)
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