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Processes
Special Issues
Clean Combustion and Emission in Vehicle Power System, 2nd Edition
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Clean Combustion and Emission in Vehicle Power System, 2nd Edition

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Sustainable Processes".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 844

Special Issue Editor

Prof. Dr. Jiaqiang E

grade E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: green manufacturing; sustainable management and technology; field synergy analysis; biodiesel combustion in diesel engine; after-treatment system of automotive systems; multidisciplinary design optimization; intelligent information fusion; active control and signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollutants from vehicle power systems are not only harmful to the environment but also have detrimental effects on human health, and there is a global trend towards enforcing more stringent regulations on these exhaust gas constituents. As a result, many clean combustion and emission technologies, such as chemical looping combustion, mild combustion, porous media combustion, and plasma-assisted combustion, have been developed in the past 30 years. Through the application of these technologies, nitrogen oxide (NOx), carbon dioxide (CO2), and particulate matter (PM) emission from combustion can be mitigated effectively. Nowadays, clean combustion technologies are attracting more and more attention from researchers all over the world. To promote communication between researchers, we invite investigators to contribute original research articles, as well as review articles, that will stimulate the continuing efforts to understand the mechanisms, production, and controls related to clean combustion and emission technology in vehicle power systems.

The 1st edition link: https://0-www-mdpi-com.brum.beds.ac.uk/journal/processes/special_issues/combustion_emission

Prof. Dr. Jiaqiang E
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. Processes is an international peer-reviewed open access monthly 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

  • reaction kinetics
  • clean combustion and emission
  • after-treatment system
  • diagnostic techniques
  • laminar and turbulent flames
  • heat and mass transfer
  • novel combustion concepts, technologies, and systems
  • clean combustion instability

Published Papers (2 papers)

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Research

14 pages, 2981 KiB  
Article
Using a One-Dimensional Convolutional Neural Network with Taguchi Parametric Optimization for a Permanent-Magnet Synchronous Motor Fault-Diagnosis System
by Meng-Hui Wang, Fu-Chieh Chan and Shiue-Der Lu
Processes 2024, 12(5), 860; https://0-doi-org.brum.beds.ac.uk/10.3390/pr12050860 - 25 Apr 2024
Viewed by 196
Abstract
Hyperparameter tuning requires trial and error, which is time consuming. This study employed a one-dimensional convolutional neural network (1D CNN) and Design of Experiments (DOE) using the Taguchi method for optimal parameter selection, in order to improve the accuracy of a fault-diagnosis system [...] Read more.
Hyperparameter tuning requires trial and error, which is time consuming. This study employed a one-dimensional convolutional neural network (1D CNN) and Design of Experiments (DOE) using the Taguchi method for optimal parameter selection, in order to improve the accuracy of a fault-diagnosis system for a permanent-magnet synchronous motor (PMSM). An orthogonal array was used for the DOE. One control factor with two levels and six control factors with three levels were proposed as the parameter architecture of the 1D CNN. The identification accuracy and loss function were set to evaluate the fault-diagnosis system in the optimization design. Analysis of variance (ANOVA) was conducted to design multi-objective optimization and resolve conflicts. Motor fault signals measured by a vibration spectrum analyzer were used for fault diagnosis. The results show that the identification accuracy of the proposed optimization method reached 99.91%, which is higher than the identification accuracy of 96.75% of the original design parameters before optimization. With the proposed method, the parameters can be optimized with a good DOE and the minimum number of experiments. Besides reducing time and the use of resources, the proposed method can speed up the construction of a motor fault-diagnosis system with excellent recognition. Full article
(This article belongs to the Special Issue Clean Combustion and Emission in Vehicle Power System, 2nd Edition)
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15 pages, 1858 KiB  
Article
Diesel Adulteration Detection with a Machine Learning-Enhanced Laser Sensor Approach
by Bachar Mourched, Tariq AlZoubi and Sabahudin Vrtagic
Processes 2024, 12(4), 798; https://0-doi-org.brum.beds.ac.uk/10.3390/pr12040798 - 16 Apr 2024
Viewed by 382
Abstract
This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to [...] Read more.
This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to facilitate machine learning training. This innovative approach not only streamlines the detection process by eliminating the need for expensive equipment and specialized personnel but also enables on-site testing without extensive sample preparation. The sensor’s design, utilizing light refraction and reflection principles, allows for the accurate measurement of diesel adulteration levels. Validation results showcase the machine learning models’ high precision in predicting adulteration percentages, as evidenced by an R-squared value of 0.999 and a mean absolute error of 0.074. This research signifies a leap in sensor technology, offering a practical solution for rapid diesel adulteration detection, especially in developing countries, by minimizing reliance on advanced laboratory analyses. The sensor’s design aligns with the requirements for low-cost IoT technology, presenting a versatile tool for various applications. Full article
(This article belongs to the Special Issue Clean Combustion and Emission in Vehicle Power System, 2nd Edition)
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