Energy Waste Research Articles

Comparison of GFRP (Glass Fiber-Reinforced Polymer) and CFRP (Carbon Fiber-Reinforced Polymer) Composite Adhesive-Bonded Single-Lap Joints Used in Marine Environments

Macroscopic structures consisting of two or more materials are called composites. The decreasing reserves of the world’s oil reserve and the environmental pollution of existing energy and production resources made the use of recycling methods inevitable. There are mechanical, thermal, and chemical recycling methods for the recycling of thermosets among composite materials. The recycling of thermoset composite materials economically saves resources and energy in the production of reinforcement and matrix materials. Due to the superior properties such as hardness, strength, lightness, corrosion resistance, design width, and the flexibility of epoxy/vinylester/polyester fibre formation composite materials combined with thermoset resin at the macro level, environmentally friendly sustainable development is happening with the increasing use of composite materials in many fields such as the maritime sector, space technology, wind energy, the manufacturing of medical devices, robot technology, the chemical industry, electrical electronic technology, the construction and building sector, the automotive sector, the defence industry, the aviation sector, the food and agriculture sector, and sports equipment manufacturing. Bonded joint studies in composite materials have generally been investigated at the level of a single composite material and single joint. The uncertainty of the long-term effects of different composite materials and environmental factors in single-lap bonded joints is an important obstacle in applications. The aim of this study is to investigate the effects of single-lap bonded GFRP (glass fibre-reinforced polymer) and CFRP (carbon fibre-reinforced polymer) specimens on the material at the end of seawater exposure. In this study, 0/90 orientation twill weave seven-ply GFRP and eight-ply CFRP composite materials were used in dry conditions (without seawater soaking) and the hand lay-up method. Seawater was taken from the Aegean Sea, İzmir province (Selçuk/Pamucak), in September at 23.5 °C. This seawater was kept in different containers in seawater for 1 month (30 days), 2 months (60 days), and 3 months (90 days) separately for GFRP and CFRP composite samples. They were cut according to ASTM D5868-01 for single-lap joint connections. Moisture retention percentages and axial impact tests were performed. Three-point bending tests were then performed according to ASTM D790. Damage to the material was examined with a ZEISS GEMINESEM 560 scanning electron microscope (SEM). The SEM was used to observe the interface properties and microstructure of the fracture surfaces of the composite samples by scanning images with a focused electron beam. Damage analysis imaging was performed on CFRP and GFRP specimens after sputtering with a gold compound. Moisture retention rates (%), axial impact tests, and three-point bending test specimens were kept in seawater with a seawater salinity of 3.3–3.7% and a seawater temperature of 23.5 °C for 1, 2, and 3 months. Moisture retention rates (%) are 0.66%, 3.43%, and 4.16% for GFRP single-lap bonded joints in a dry environment and joints kept for 1, 2, and 3 months, respectively. In CFRP single-lap bonded joints, it is 0.57%, 0.86%, and 0.87%, respectively. As a result of axial impact tests, under a 30 J impact energy level, the fracture toughness of GFRP single-lap bonded joints kept in a dry environment and seawater for 1, 2, and 3 months are 4.6%, 9.1%, 14.7%, and 11.23%, respectively. At the 30 J impact energy level, the fracture toughness values of CFRP single-lap bonded joints in a dry environment and in seawater for 1, 2, and 3 months were 4.2%, 5.3%, 6.4%, and 6.1%, respectively. As a result of three-point bending tests, GFRP single-lap joints showed a 5.94%, 8.90%, and 12.98% decrease in Young’s modulus compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. CFRP single-lap joints showed that Young’s modulus decreased by 1.28%, 3.39%, and 3.74% compared to dry joints kept in seawater for 1, 2, and 3 months, respectively. Comparing the GFRP and CFRP specimens formed by a single-lap bonded connection, the moisture retention percentages of GFRP specimens and the amount of energy absorbed in axial impact tests increased with the soaking time in seawater, while Young’s modulus was less in three-point bending tests, indicating that CFRP specimens have better mechanical properties.

PET-PZT Dielectric Polarization: Electricity Harvested from Photon Energy

The effect of residual stress or heat on ferroelectrics used to convert photons into electricity was investigated. The data analysis reveals that when the PET-PZT piezoelectric transducer is UV-irradiated with a 405 nm wavelength, it becomes a photon–heat–stress electric energy converter and capacitator. Our objective was to evaluate the PET-PZT photon–heat–stress electric energy conversion performance and the role of the light’s wavelength and intensity. Converting waste energy from energy-intensive processes and systems is crucial to reducing the environmental impact and achieving net-zero emissions. To achieve these, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer.

Bibliometric Analysis of Global Publications on Management, Trends, Energy, and the Innovation Impact of Green Hydrogen Production

The aim of this bibliometric analysis was to evaluate the evolution of scientific research in hydrogen, focusing on green hydrogen production, storage, and utilization. Articles from prominent databases were analyzed, revealing a strong emphasis on sustainable hydrogen technologies through keywords like “hydrogen production”, “green hydrogen”, and “solar power generation”. Mature research areas include production methods and electrolysis, while emerging topics such as energy efficiency and policy are gaining traction. The most-cited papers, from Energy Conversion and Management to the International Journal of Hydrogen Energy, cover techno-economic assessments and case studies on deploying hydrogen technologies. Key findings highlight the variability of the Levelized Cost of Hydrogen (LCOH) across technologies and regions. Deep learning applications, including Fast Fourier Transform-based forecasting and explainable AI models, are transforming production optimization, while Life Cycle Assessment (LCA) emphasizes renewable energy’s role in reducing carbon intensity and resource consumption. Diverse strategies, such as fiscal incentives for wind energy and use of urban waste, underline the importance of local solutions. This analysis reflects the rapid growth of hydrogen research, driven by international collaboration and innovations in sustainable production, storage, and optimization. It is hoped that this paper will help to shed more light on the current and future understanding of green hydrogen.

Energy efficiency of waste gasification plants in the national municipal waste management system

To achieve high levels of municipal waste recovery through a system employing mechanical-biological waste processing technologies, effective management of the over-sieve fraction of mixed municipal waste (preRDF) is crucial. This preRDF cannot be landfilled due to its combustion heat exceeding 6 MJ/kg. Therefore, thermal treatment of waste and subsequent energy recovery become pivotal in the national waste management system, particularly amidst energy crises and fluctuating energy prices. Waste-derived energy can serve as a valuable renewable energy source. To ascertain the true efficiency of the plant in terms of energy, environmental impact, and economics, it is vital to organize the concepts of energy efficiency for thermal waste treatment plants. The energy efficiency of a waste gasification plant should be comprehensively assessed from three standpoints: energy efficiency of thermal waste treatment (i.e., energy efficiency index), energy efficiency of recovering chemical energy contained in waste (actual energy efficiency of the plant), and the efficiency of renewable energy production. A thermal waste processing plant qualifies as a renewable energy source, when it generates electricity and heat from the biodegradable fraction of waste. This article endeavours to determine the potential contribution of chemical energy from the biodegradable waste fraction, relying on preRDF fraction test results..

CARBONLY

This project focuses on the development and implementation of a Carbon Footprint App designed to empower individuals and organizations to track and reduce their carbon emissions. By providing real-time tracking, personalized recommendations, and engaging features such as gamification and community challenges, the app aims to foster sustainable practices and raise awareness about the environmental impact of everyday activities. The methodology involves collecting user data on energy use, transportation, food consumption, and waste management, converting this data into carbon dioxide equivalents, and offering actionable advice to lower emissions. The app is applicable across various sectors, including personal use, corporate sustainability, policy support, education, supply chain management, urban planning, and environmental certification. The results demonstrate that informed individual actions, such as skipping a weekly cup of coffee or reducing chocolate consumption, can collectively lead to significant reductions in carbon emissions. This project underscores the importance of individual responsibility in combating climate change and promotes a community-driven approach to achieving a more sustainable future. Key Words: Carbon Footprint, Real-time Tracking, Gamification, Personalized Recommendations, Sustainability

Review on the Air Temperature and Humidity Produce by Solar Dryer and Potential to be Reused

This article presents a comprehensive review of recent advancements in the reuse of waste heat from solar food dryers, a strategy that holds significant potential to improve system efficiency and sustainability. Solar drying systems typically discharge air at temperatures between 40°C and 70°C, resulting in a substantial loss of free solar thermal energy up to 50% of total energy. This review introduces novel approaches to capturing and reusing this low-quality heat, including the integration of desiccant materials that can boost drying efficiency by up to 64%, and innovative designs like rotating dryer wheels, which increase effective heat gains by an average of 153%. Unlike previous studies, this article not only aggregates and analyzes field test data such as outlet temperatures, humidity levels, and heat recovery efficiencies but also identifies practical and scalable solutions for heat reuse, such as water heating, space heating, and heating nearby cold rooms. By providing quantitative results and exploring the potential for continuous 24-hour operation through advanced heat management techniques, this review offers new insights and practical guidelines for engineers and researchers aiming to make solar drying processes more energy-efficient and commercially viable. This work is particularly relevant for those interested in developing sustainable agricultural practices, as it highlights the most promising methods for reducing energy waste and enhancing the overall performance of solar dryers. The novel synthesis of existing technologies and the identification of key areas for future research make this article a valuable resource for advancing the field of solar drying.

Utilizing Agricultural Residues from Hot and Cold Climates as Sustainable SCMs for Low-Carbon Concrete

Supplementary cementitious materials (SCMs), such as fly ash, slag, and silica fume, predominantly derived from industrial waste, are widely utilized in concrete due to their proven ability to enhance both its mechanical and durability properties. Moreover, these SCMs play a crucial role in mitigating the carbon footprint of concrete by reducing its cement content, which is responsible for approximately 8% of global CO2 emissions. However, the sustainability and long-term availability of conventional SCMs are increasingly under scrutiny, particularly in light of the impending shutdown of coal-fired power plants, which threatens the future supply of fly ash. As a result, the concrete industry faces an urgent need to identify alternative SCMs to maintain and advance eco-friendly practices. This article stands out from previous reviews by employing a bibliometric analysis to comprehensively explore the use of commonly utilized agricultural ashes (rice husk, palm oil, and sugarcane bagasse), prevalent in tropical and subtropical regions as SCMs. Additionally, it provides valuable insights into the potential of cold-weather crops (e.g., barley, canola, and oat) that demonstrate promising pozzolanic reactivity. The study critically evaluates and compares the physical and chemical characteristics of agricultural ashes from both hot and cold climates, assessing their influence on the fresh, mechanical, and durability properties of concrete. It also addresses the challenges and limitations associated with their use. Furthermore, in line with the United Nations and Environmental Protection Agency (EPA) sustainability goals, the review evaluates the environmental benefits of using agricultural ashes, emphasizing waste reduction, resource conservation, and energy savings. This comprehensive review paper should deepen the understanding of agricultural ashes as sustainable SCMs, providing a strategic direction for the construction industry to adopt low-carbon concrete solutions across various climates while promoting advancements in production methods, performance standards, and emerging technologies such as hybrid materials and 3D printing.

Harnessing artificial intelligence for business optimization: Enhancing efficiency and sustainability in SMEs

Small and Medium Enterprises (SMEs) play a crucial role in driving economic growth and innovation, yet they often struggle with limited resources, operational inefficiencies, and sustainability challenges. In this context, artificial intelligence (AI) presents a transformative opportunity for optimizing business processes, improving decision-making, and promoting sustainable practices. This review explores the integration of AI technologies to enhance efficiency and sustainability in SMEs, focusing on practical applications such as automation, predictive analytics, and intelligent resource management. By leveraging AI, SMEs can automate repetitive tasks, optimize supply chains, and streamline financial operations, leading to cost reductions and productivity gains. Additionally, AI-powered customer engagement tools, such as chatbots and personalized marketing systems, allow SMEs to better understand customer behavior and enhance service delivery. On the sustainability front, AI enables real-time monitoring of energy consumption, waste management, and resource utilization, helping SMEs align with environmental and regulatory standards. However, the adoption of AI in SMEs faces significant challenges, including data quality issues, limited budgets, and a lack of technical expertise. This review proposes a strategic approach to implementing AI solutions, emphasizing the need for tailored tools, effective data management, and employee training. By examining successful case studies, we highlight how AI-driven optimization has empowered SMEs to overcome operational hurdles and achieve sustainable growth. As the business landscape becomes increasingly competitive and sustainability-focused, integrating AI is not only beneficial but essential for SMEs seeking long-term resilience. This study aims to provide SMEs with actionable insights into adopting AI technologies, ultimately fostering efficiency, innovation, and sustainability in their operations. Keywords: Artificial intelligence, Small and medium enterprises (SMEs), Business Optimization, Review.

Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace

This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts.

Environmental assessment of gasification and green hydrogen potential role in waste management decarbonization

Achieving sustainability involves implementing a circular economy model and decarbonizing the waste management sector. The development of innovative technologies that enable the reduction of emissions and resource recovery is one of the sector's greatest challenges. From this perspective, the waste-to-methanol (WtM) process represents a viable prospect, the potential environmental impacts of which have never been studied from a life-cycle perspective. A cradle-to-grave LCA analysis was conducted, studying WtM technology and its potential implementation of green hydrogen (WtM-GH) in the process, through a comparison with traditional waste-to-energy (WtE).The results showed that WtM-GH performs best for global impacts, with a global warming potential of 3.52·107 kgCO2eq instead of 1,04·108 kgCO2eq proper of the WtE. Positive effects take place also for the fossil resource scarcity. Looking for non-toxic regional/local impacts WtE represents the best scenario, with the best performance on water consumption (7.96·107 m3) and ionising radiation (2.82·107 kBq Co-60eq). Cumulative impact results, obtained through the normalization, found that WtM is the best scenario, thanks to the less toxic impact compared to the other two, especially for freshwater ecotoxicity (5.26·106 kg 1,4-DCB) and marine ecotoxicity (6.73·106 kg 1,4-DCB).WtM-GH allows the production of 1366 kg of methanol per ton of Refuse Derived Fuel, thanks to the conversion of the entire CO2 contained in the waste, enhancing the value of by-products and carbon credits on the market and making the process more economically sustainable.

Energy-saving and carbon reduction analysis of series/parallel combination for heat pump and chiller in a hotel building

Taiwan is known for its hot and humid climate, where buildings rely on air conditioning systems to provide comfortable conditions. Specifically in commercial buildings such as hotels, apart from being required to provide comfortable indoor conditions, they need to provide hot water 24 h for guests, leading to the common application of chiller and heat pump systems in hotels. With the rise of the Taiwanese tourism industry, many hotels have been newly built or renovated. However, during the planning and construction stages, considerations may be limited by personnel experience, operational difficulties, installation costs, or the desire to simplify operations. This often results in the separate consideration of heat pump systems and chiller systems, leading to unnecessary resource and energy waste. Therefore, this study takes place at a hotel building in Taichung located in Central Taiwan. Designing a series/parallel system between the heat pump and chiller systems. Through a control system and a real-time recording system, controllable factors were analyzed, such as system equipment supply/return water temperature, outdoor relative and enthalpy, and occupancy rate change. Additionally, energy-saving potential was evaluated from the collected data through linear regression. The results show that when the outdoor air temperature, outdoor enthalpy, and occupancy rate are low, the parallel system is more energy-efficient due to the higher energy consumption of the chiller under low load conditions, which is suitable for winter. Conversely, the series system is more energy-efficient than under hot weather and high indoor load conditions, making it suitable for use in the summer.

Utilization of virtual reality as a learning tool to increase students’ pro-environmental behavior at universities: A maximum likelihood estimation approach

This research explores the effect of virtual reality technology on enhancing university students’ environmental knowledge (ENK), environmental attitudes (ENAs), and pro-environmental behaviors (PEBs). The primary objective is to evaluate how immersive virtual reality (VR) experiences influence students’ understanding of pressing environmental issues like climate change, deforestation, and pollution. A sample of 336 students from Universitas Negeri Makassar who participated in a VR-based environmental education class were surveyed through structured questionnaires distributed via Google Forms. Stratified random sampling was employed to ensure a diverse illustration of the student population. The study applies covariance-based structural equation modeling using the AMOS program to evaluate the correlations among the variables of ENK, attitudes, and behaviors. The results indicate that VR significantly enhances students’ ENK by providing an interactive and immersive learning platform that simplifies complex ecological concepts. VR was shown to positively influence students’ ENAs by simulating real-world environmental degradation consequences, fostering emotional connection and concern. A robust link was also identified between improved ENAs and increased PEBs, such as waste reduction and energy saving. These findings demonstrate that VR can be an actual tool for driving behavioral change in the context of environmental education, offering a promising approach to addressing global sustainability challenges. The analysis adds to the expanding body of literature on technology-enhanced learning, highlighting the potential of VR to promote sustainability through education and fostering a more profound commitment to environmental stewardship among students.

IMPORTANCE OF INDUSTRIAL SYMBIOSIS STRATEGIES ON ENERGY EFFICIENCY IMPROVEMENT IN ORGANIZED INDUSTRIAL ZONES: KONYA OIZ CASE

Industrial symbiosis (IS) refers to the integration and optimization of numerous industrial systems and processes and is inspired by the nature. By allowing the exchange of materials, energy, other resources, byproducts or valuable waste among companies, the goal is to increase resource efficiency and decrease potential waste. In this study, the IS relevant concepts and examples from the literature are briefly discussed, and a survey is conducted in Konya organized industrial zone. The primary obstacle faced during the survey study is the companies' reluctance to communicate and share information. The survey explores the current level of knowledge, existing energy efficiency efforts, and organizations' willingness to address the topic if grants and subsidies are available. Following the survey, exchange opportunities from the literature are compiled, considering the sectoral structure of the selected zone. Sectoral pairings are formed to encourage businesses and to demonstrate applicability and symbiosis opportunities. Hence, outcomes show how industrial symbiosis has an impact on energy and resource efficiency and waste reduction within selected industrial zone. In addition, its connection and relationship with government incentives, investments and energy efficiency networks are also discussed. As a result, it has been found that there is significant symbiosis potential in a growing and developing region like the Konya industrial zone, through sectoral matching of companies. By increasing awareness among companies, providing financial and legal support, and assisting them in setting targets, inter-company symbiosis efforts can be facilitated and expanded.

Guest Editorial: Patience in AI Development Is a Virtue: Why 99% Correct Is 100% Wrong

In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences. For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards. Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest. Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations. The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards. However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value. Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies. While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic. Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability. Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance. Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.

Digital Dynamic Agent (DDA) Driven Energy Contract Negotiation

Abstract Digital Dynamic Agent (DDA) introduces the new paradigm of digital dynamic agent-driven energy contract negotiation, which is dynamic and real-time in adjustment. Long-term static contracts normally produce one-sided traditional market outcomes. On the contrary, DDA driving will be continuous in nature, at a level of rapid negotiations over the underlying transactions. This paper proposes a new generic DDA framework that negotiates energy contracts between grid agents and renewable generation sources, especially wind farms equipped with battery storage. The agents dynamically update their strategy concerning realtime market conditions, demand, and storage capacity. By simulation over one week with real data from the wind farms, it is evident that the DDA framework is better adapted in using battery storage for significantly lesser wastage of energy. Interestingly, the system secured energy at competitive prices in both live energy and capacity markets; it showed an improvement in cost savings compared to conventional static contracts. It also allows for effective energy procurement within diverse market states, which enables greater flexibility and responsiveness in energy management through the optimization of the negotiation process. The current paper highlights the capability of the framework to enhance negotiation outcomes while allowing the broader transition toward renewable energy markets.

A smart home energy management system based on human activity recognition and deep reinforcement learning

Smart home energy management systems (SHEMS) can help users save on energy costs by controlling various household loads in response to environmental changes. However, human activity is closely related to household energy consumption, which can lead to potential energy waste. Additionally, smart homes often feature multiple types of distributed energy resources (DER), and how to leverage their potential for participating in grid scheduling without significantly impacting users remains an unresolved issue. This paper proposes an intelligent home energy management method based on human activity recognition (HAR) and deep reinforcement learning. By using activity recognition results derived from intrusive load monitoring (ILM), we categorize contexts into two groups. The agent employs the soft actor-critic (SAC) algorithm for real-time control. In the first context, the goal is to ensure user satisfaction while reducing energy costs. In the second context, the focus is on controlling DER to participate in grid ancillary services to generate revenue. The proposed agent is trained using a real dataset, and simulations validate the effectiveness of the algorithm in home energy management.

Forecasting Thermoelectric Power Generation through Utilization of Waste Heat from Building Cooling Systems based on Simulation

The transformation of sustainable energy use is one of the main challenges facing the world today. Waste heat from industrial processes and conventional power plants is one form of energy waste that is often wasted. In this context, research on thermoelectric energy conversion systems is a very relevant and important topic to research. This research investigates about the potential for utilizing thermoelectric elements with stacked materials that utilize waste heat from building cooling systems. Numerical simulation was chosen to carry out further analysis regarding the potential energy produced and the efficiency of the materials used. Three-dimensional design and ANSYS is used as a three-dimensional simulation analysis tool. The research results show that thermoelectric systems with stacked materials have the potential to produce energy from waste heat. Material (Cu2Se+, BiTe+) (Bi2S3-, CuFeS-) with 10mm legs has the highest output power of 32.82mW whereas (PbSe+, BiTe+) (Bi2S3-, AgInSe-) with a leg length of 20 mm has the highest efficiency value of 25.97%, and a power value of 6.92mW. Full system research can produce values ​​that more closely resemble actual conditions.

A comprehensive power management strategy for the effective sizing of a PV hybrid renewable energy system with battery and H2 storage

In the present work a detailed Power Management Strategy (PMS) of a Photovoltaic Hybrid Renewable Energy System (PV-HRES) with battery, H2 storage/re-electrification, and diesel generator (DG) back-up has been developed. The PV-HRES was regarded to employ commercial PV modules, battery cells, and power electronics, as well as commercial alkaline electrolyzer (EL) and PEM fuel cell (FC), both regarded to variably operate following the fluctuations of PV-power excesses/shortages. The scope was to demonstrate the operability of the PMS and to utilize the PMS for the rational sizing of the PV array, the batteries' bank and the hydrogen storage capacity, for a given load and a solar irradiation annual profile. The annual operation of the PV-HRES was simulated for 440 combinations of PV-arrays and battery/H2 storage capacities, with constant EL and FC capacity, aiming for: (i) exclusively renewable power generation, (ii) rational PV-array sizing and (iii) annually balanced H2 generation/consumption. The operation algorithm was based upon the battery's State of Energy (SOE) variation between 20 and 95 % of their nominal energy capacity (BNEC). Below 20 %, the PV-energy deficits were covered by the FC, in case of sufficient stored H2, whereas beyond 95 %, energy excesses were converted to H2, in case of H2 storage volume availability. Increased PV-arrays and batteries led to the exhaustion of H2 storage volume and to significant dumped renewable energy waste (up to 23 % of the PV generation), whereas decreased PVs and battery capacities led to H2 exhaustion, throughout the annual operation. Out of the 440 examined combinations, 40 were found to simultaneously: i) nullify the DG generation, for exclusively renewable energy generation, ii) prevent both battery and H2 storage volume exhaustion, thus eliminating excessive energy wastes and rationalizing the PV-array size, and iii) balancing the annual hydrogen generation/consumption. To fulfill the 1.48 kW average load demand, the analysis pointed out an optimum PV-HRES of 10.23 kWp nominal PV power, 120 kWh battery storage capacity, and 4 m3 H2 storage volume, at 100 bars maximum pressure; for which the annual PV generation exceeded by 25.8 % the annual load demand.

A study on the implementation of eco-friendly design techniques in hospitality space (restaurant)

Eco-friendly interior design, also known as sustainable design, is a growing trend that prioritizes creating spaces that are both beautiful and environmentally responsible. It involves making conscious choices about materials, finishes, and overall design to minimize the negative impact on the planet. This study explores the implementation of eco-friendly design techniques in hospitality spaces, with a focus on restaurants. As the hospitality industry, particularly restaurants, faces increasing pressure to adopt sustainable practices, this research examines how eco-friendly design can reduce environmental impact while maintaining operational efficiency and aesthetic appeal. Restaurants, known for their high energy consumption and waste production, are ideal candidates for integrating sustainable design elements. The study analyses key eco-friendly design strategies. Sustainable materials, including recycled and locally sourced products, contribute to lowering the carbon footprint of restaurants. By examining case studies of restaurants successfully implementing these practices, the study provides insights into best practices and potential challenges.

Design and Implementation of An Energy-Efficient Extension Board with Integrated Overcurrent Protection for Home Appliances

Electrical extension boards are a major cause of electrical fires in homes, university hostels, offices, and other key applications worldwide, often due to overloading beyond their rated capacity. Additionally, users frequently forget to turn off appliances like televisions, portable fans, or electric irons, resulting in unnecessary energy waste. These Extension boards, commonly used to power multiple electrical appliances, are widespread in homes and universities, but overloading them can lead to overheating, burning, and potential fire hazards. This is often due to exceeding the board’s rated capacity, which reduces load resistance and increases current flow, particularly in parallel connected domestic loads. To address this risk, this paper presents an innovative energy efficient extension board equipped with overcurrent protection. The system continuously monitors the current drawn by connected appliances and automatically isolates them via a relay mechanism when the load exceeds the rated capacity of 13 amperes. The overcurrent status is displayed on an LCD. A microcontroller, interfaced with a current sensor and relay, manages the process, ensuring the safety of the extension boards and home appliances. Additionally, a buzzer notifies the user of the system's status. Furthermore, the system features a timer function to manage energy consumption based on user defined settings. Both simulation and experimental prototype were employed to verify the design, and the results showed a strong correlation between the expected and actual performance, demonstrating the practicality of the proposed system.