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Advances in Zero Energy Buildings
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Advances in Zero Energy Buildings

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (1 October 2023) | Viewed by 4879

Special Issue Editors

Prof. Dr. Mohammad Ali Abdelkareem

E-Mail Website
Guest Editor
Dept. of Sustainable and Renewable Energy Engineering, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates
Interests: energy efficiency; sustainable development goals (SDGs); fuel cells; renewable energy resources; techno-economic feasibility analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Hussein M. Maghrabie

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Faculty of Engineering, South Valley University, Qena 83521, Egypt
Interests: energy efficiency; sustainable development goals (SDGs); building-integrated photovoltaic/thermal (BIPVT) systems; renewable energy resources; techno-economic feasibility analysis
Dr. Enas Taha Sayed

E-Mail Website
Guest Editor
Dept. of Chemical Engineering Department, Minia University, Minia 61111, Egypt
Interests: energy efficiency; sustainable development goals (SDGs), fuel cells; microbial fuel cells; renewable energy resources; techno-economic feasibility analysis

Special Issue Information

Dear Colleagues,

Advancements in Zero Energy Buildings (ZEBs) could exceptionally reduce energy consumption and greenhouse gas emissions. This special issue is intended for Advances in Zero Energy Buildings at sustainability, materials, energy efficiency, technologies, environmental evaluation, etc. This includes analytical, experimental, and simulation work on Zero Energy Buildings that can serve the growing benefits of renewable energy resources. Submissions with novel material synthesis and applications in storage devices are encouraged, as well as significant results on device implementation and development. This applies to both lab-scale and large-scale applications. This special issue of Sustainability aims to collect the contributions of researchers to introduce the opportunities for future advances in Zero Energy Buildings (ZEBs).

Prof. Dr. Mohammad Ali Abdelkareem
Dr. Hussein M. Maghrabie
Dr. Enas Taha Sayed
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. 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

  • sustainability perspective
  • renewable energy resources
  • energy storage
  • advanced materials
  • hybrid systems
  • energy efficiency measures
  • environmental evaluation
  • cost-effective solutions
  • optimization of design and operation

Published Papers (4 papers)

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Research

17 pages, 4555 KiB  
Article
A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency
by Yaping Wu, Xiaolong Wu, Yuanwu Xu, Yongjun Cheng and Xi Li
Sustainability 2023, 15(19), 14402; https://0-doi-org.brum.beds.ac.uk/10.3390/su151914402 - 30 Sep 2023
Viewed by 823
Abstract
Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, [...] Read more.
Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, as one of the various types of fuel cells, are renowned for their capability of combined heat and power generation. They can achieve an efficiency of up to 90% in operation. Furthermore, due to the fact that water is the byproduct of their electricity generation process, they are extremely environmentally friendly, contributing significantly to humanity’s sustainable development. With the advancement of renewable energy technologies and the increasing emphasis on sustainable development requirements, predicting and optimizing the efficiency of SOFC systems is gaining importance. This study leverages data collected from an SOFC system and applies an improved neural network structure, specifically the dendritic network (DN) architecture, to forecast thermoelectric efficiency. The key advantage of this method lies in the adaptive neural network algorithm based on the dendritic network structure without manually setting hidden nodes. Moreover, the predicted model of thermoelectric efficiency is validated using 682 and 1099 h of operational data from the SOFC system, and the results are compared against a conventional machine learning method. After comparison, it is found that when the novel method with adaptive characteristics proposed was used for SOFC system efficiency prediction, the MAE and RMSE values were both lower than 0.014; the result is significantly better than from other traditional methods. Additionally, this study demonstrated its effectiveness in predicting the thermoelectric efficiency of SOFC systems through secondary experiments. This study offers guidance on enhancing SOFC systems thermoelectric efficiency. Therefore, this study provides a foundation for the future industrialization of fuel cell systems. Full article
(This article belongs to the Special Issue Advances in Zero Energy Buildings)
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15 pages, 5815 KiB  
Article
Fuel Economy Energy Management of Electric Vehicles Using Harris Hawks Optimization
by Hegazy Rezk, Mohammad Ali Abdelkareem, Samah Ibrahim Alshathri, Enas Taha Sayed, Mohamad Ramadan and Abdul Ghani Olabi
Sustainability 2023, 15(16), 12424; https://0-doi-org.brum.beds.ac.uk/10.3390/su151612424 - 16 Aug 2023
Cited by 1 | Viewed by 1018
Abstract
Fuel cell hybrid electric vehicles (FCEVs) have gained significant attention due to their environmentally friendly nature and competitive performance. These vehicles utilize a fuel cell system as the primary power source, with a secondary power source such as a battery pack or supercapacitor. [...] Read more.
Fuel cell hybrid electric vehicles (FCEVs) have gained significant attention due to their environmentally friendly nature and competitive performance. These vehicles utilize a fuel cell system as the primary power source, with a secondary power source such as a battery pack or supercapacitor. An energy management strategy (EMS) for FCEVs is critical in optimizing power distribution among different energy sources, considering factors such as hydrogen consumption and efficiency. The proposed EMS presents an optimized external energy maximization strategy using the Harris Hawks Optimization to reduce hydrogen consumption and enhance the system’s efficiency. Through a comparative simulation using the Federal Test Procedure (FTP-75) for the city driving cycle, the performance of the proposed EMS was evaluated and compared to existing algorithms. The simulation results indicate that the proposed EMS outperforms other existing solutions in terms of fuel consumption reduction, with a potential reduction of 19.81%. Furthermore, the proposed energy management strategy also exhibited an increase in system efficiency of 0.09%. This improvement can contribute to reducing the reliance on fossil fuels and mitigating the negative environmental impacts associated with vehicle emissions. Full article
(This article belongs to the Special Issue Advances in Zero Energy Buildings)
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13 pages, 3597 KiB  
Article
Optimal Parameter Identification of Single-Sensor Fractional Maximum Power Point Tracker for Thermoelectric Generator
by Abdul Ghani Olabi, Hegazy Rezk, Enas Taha Sayed, Tabbi Awotwe, Samah Ibrahim Alshathri and Mohammad Ali Abdelkareem
Sustainability 2023, 15(6), 5054; https://0-doi-org.brum.beds.ac.uk/10.3390/su15065054 - 13 Mar 2023
Cited by 3 | Viewed by 1159
Abstract
A thermoelectric generator (TEG) is used for converting temperature difference and into DC directly to electric energy based on the Seebeck effect. This new technology has attracted researchers of sustainable energy. The energy obtained from the TEG depends on the temperature difference between [...] Read more.
A thermoelectric generator (TEG) is used for converting temperature difference and into DC directly to electric energy based on the Seebeck effect. This new technology has attracted researchers of sustainable energy. The energy obtained from the TEG depends on the temperature difference between the two sides of the TEG. A reliable MPP “maximum power point” tracker (MPPT) is mandatory to guarantee that the TEG is working close to the MPP under different operational conditions. There are two common methods that have been widely used to track the MPP: hill climbing (HC) and incremental conductance (INR). The HC method is very fast in tracking the MPP; however, oscillation can occur under a high steady state. On the contrary, the INR method needs more time to track the MPP but does not oscillate around the MPP. To overcome these issues, fractional control is adopted. Furthermore, the proposed MPPT requires only a single current sensor, as opposed to conventional MPPTs, which require at least two sensors: current and voltage sensors. The cost of the control system is reduced when the number of sensors is reduced. Hunger games search optimization is used to estimate the parameters of a single sensor optimized fractional MPPT (OFMPPT). During the optimization process, three parameters were assigned as decision variables: proportional gain, integral gain, and order, with the objective function being the TEG’s energy. The results demonstrated the superiority of OFMPPT in both transient and steady state compared to HC and INR. Full article
(This article belongs to the Special Issue Advances in Zero Energy Buildings)
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12 pages, 5733 KiB  
Article
Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production
by Hegazy Rezk, A. G. Olabi, Mohammad Ali Abdelkareem, Hussein M. Maghrabie and Enas Taha Sayed
Sustainability 2023, 15(3), 1878; https://0-doi-org.brum.beds.ac.uk/10.3390/su15031878 - 18 Jan 2023
Cited by 5 | Viewed by 1250
Abstract
Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast [...] Read more.
Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM. Full article
(This article belongs to the Special Issue Advances in Zero Energy Buildings)
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