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Selected Papers from iTIKI IEEE ICASI 2022 in Energies
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Selected Papers from iTIKI IEEE ICASI 2022 in Energies

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 3269

Special Issue Editors

Prof. Dr. Sheng-Joue Young

E-Mail Website
Guest Editor
Department of Electronic Engineering, National United University, Miaoli City 36063, Taiwan
Interests: semiconductor physics; optoelectronic devices; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang-Wen Ji

E-Mail Website
Guest Editor
Department of Electro-Optical Engineering, National Formosa University, Yunlin 632, Taiwan
Interests: nano-optoelectronics; photo detector, nano-materials
Special Issues, Collections and Topics in MDPI journals
Dr. Stephen D. Prior

E-Mail Website
Guest Editor
Aeronautics, Astronautics and Computational Engineering, University of Southampton, Southampton SO16 7QF, UK
Interests: microsystem design; nanotechnology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 8th IEEE International Conference on Applied System Innovation 2022 (IEEE ICASI 2022, https://2022.icasi-conf.net/) will be held in Sun Moon Lake, Nantou, Taiwan on April 21-23, 2022, and will provide a unified communication platform for a wide range of topics. A Special Issue of Energies, entitled “Selected Papers from iTIKI IEEE ICASI 2022”, will provide related scientific research, technology development, and policy and management studies, including both reviews and regular research papers. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. The full experimental details must be provided so that the results can be reproduced.

This Special Issue welcomes high-quality papers from iTIKI IEEE ICASI 2022. We invite investigators to contribute original research articles, as well as review articles, to this Special Issue. Potential topics include, but are not limited to:

  • Energy fundamentals;
  • Primary energy sources;
  • Secondary energy sources and energy carriers;
  • Energy exploration;
  • Intermediate and final energy use;
  • Energy conversion systems;
  • Energy policy;
  • Exergy;
  • Energetics;
  • Energy research and development.

Prof. Dr. Sheng-Joue Young
Prof. Dr. Liang-Wen Ji
Dr. Stephen D. Prior
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.

Published Papers (2 papers)

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Research

15 pages, 2567 KiB  
Article
Enhanced Biodiesel Synthesis via a Homogenizer-Assisted Two-Stage Conversion Process Using Waste Edible Oil as Feedstock
by Ming-Chien Hsiao, Peir-Horng Liao, Kuo-Chou Yang, Nguyen Vu Lan and Shuhn-Shyurng Hou
Energies 2022, 15(23), 9036; https://doi.org/10.3390/en15239036 - 29 Nov 2022
Cited by 1 | Viewed by 1137
Abstract
In this study, a homogenizer in conjunction with a two-stage process was utilized to facilitate biodiesel production from waste edible oil (WEO). This paper contributes to the improvement of the yield and the shortening of the reaction time for biodiesel synthesis. Sulfuric acid [...] Read more.
In this study, a homogenizer in conjunction with a two-stage process was utilized to facilitate biodiesel production from waste edible oil (WEO). This paper contributes to the improvement of the yield and the shortening of the reaction time for biodiesel synthesis. Sulfuric acid was used in the first stage which was the esterification of the free fatty acids (FFA) of the WEO; then the transesterification reaction of triglycerides took place in the second stage with an alkaline catalysis. The present investigation aimed to explore the parameters affecting the reactions, including homogenizer speed, alcohol/oil molar ratio, catalyst dosage, reaction temperature, and reaction time. Under the operating conditions of the first stage (the reaction temperature was 65 °C, the homogenizer speed was 8000 rpm, the methanol/oil molar ratio was 15:1, and the amount of sulfuric acid was 4 wt%), the acid value fell to below 2 mg KOH/g after 10 min. The best base-catalyzed conditions in the second stage were: homogenizer speed of 8000 rpm, NaOH catalyst concentration of 1 wt%, methanol/oil molar ratio of 9:1 (mol/mol), reaction temperature of 65 °C, and reaction time 10 min. Consequently, the conversion rate from WEO to biodiesel achieved 97% after only 20 min, in line with the EU EN14214 standard, which requires a biodiesel production rate of at least 96.5%. Full article
(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2022 in Energies)
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30 pages, 6554 KiB  
Article
Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data
by Rong-Jong Wai and Pin-Xian Lai
Energies 2022, 15(10), 3838; https://doi.org/10.3390/en15103838 - 23 May 2022
Cited by 5 | Viewed by 1547
Abstract
In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of [...] Read more.
In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of data transmission cost, it brings more challenges to the solar PV power generation forecast. Because power operators usually need real-time solar PV power generation as a basis for the power dispatch, but considering the cost of communication, they cannot always provide corresponding historical power generation data in real time. In this study, an intelligent solar PV power generation forecasting mechanism combined with weather information is designed to cope with the issue of the absence of real-time power generation data. Firstly, the Pearson correlation coefficient analysis is used to find major factors with a high correlation in relation to solar PV power generation to reduce the computational burden of data fitting via a deep neural network (DNN). Then, the data preprocessing, including the standardization and the anti-standardization, is adopted for data-fitting or real-time solar PV power generation data to take as the input data of a long short-term memory neural network (LSTM). The salient features of the proposed DNN-LSTM model are: (1) only the information of present solar PV power generation is required to forecast the one at the next instant, and (2) an on-line learning mechanism is helpful to adjust the trained model to adapt different solar power plant or environmental conditions. In addition, the effectiveness of the trained model is verified by six actual solar power plants in Taiwan, and the superiority of the proposed DNN-LSTM model is compared with other forecasting models. Experimental verifications show that the proposed forecasting model can achieve a high accuracy of over 97%. Full article
(This article belongs to the Special Issue Selected Papers from iTIKI IEEE ICASI 2022 in Energies)
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