Energy Waste Research Articles

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.

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.

Solar-Driven Agri Bot: A Storage-Free, Eco-Friendly Farming Solution

This work presents an innovative solution for sustainable agriculture by utilizing solar power to directly drive its motor system without the need for energy storage. This bot is equipped with Light Dependent Resistors (LDRs) and an Arduino microcontroller, the bot adjusts motor operations based on light conditions for optimal power efficiency. A relay switch enables seamless switching between solar power and a backup battery, ensuring continuous operation in varying sunlight. The bot’s multi-motor design supports reliable movement, accommodating a range of agricultural tasks such as transporting loads, water spraying, and ploughing. Its trolley adds versatility for carrying goods, making it practical and multifunctional in the field. This eco-friendly system not only reduces energy waste but also minimizes costs and design complexity by foregoing energy storage. Offering a cost-effective, environmentally-friendly solution for modern agriculture, this bot demonstrates a valuable application of direct solar power in field operations, supporting greener, more sustainable farming practices.

Performance assessment of hybrid adsorption/humidification-dehumidification desalination cycle powered by dish/stirling concentrated solar power system for sustainable freshwater production maximization

To achieve sustainable development, it is crucial to prioritize green polygeneration systems capable of generating diverse energy outputs, including electricity, heat, and distilled water, with enhanced efficiencies, reduced costs, and environmentally friendly advantages. Therefore, this study explores an innovative integration for a solar-powered desalination system to present an eco-friendly desalination system that agrees with sustainability goals of eco-friendly clean water and net-zero energy. The proposed system utilizes the waste heat from the solar dish Stirling power engine to drive the adsorption desalination system that is combined with two ejectors, humidification-dehumidification desalination. The humidification-dehumidification utilizes the waste energy from the silica gel-based adsorption desalination system process to enhance the freshwater productivity of the designed integrated system. Comprehensive modeling for the hybrid system components has been formulated using MATLAB to assess the electrical performance and freshwater production of the proposed system. The study also expresses the effect of evaporative-condenser heat recovery in enhancing the freshwater productivity of the hybrid system. Moreover, the effects of cycle time, and evaporator pressure on the performances of the system with and without heat recovery. The findings indicate that the proposed system has the capability to generate an electrical power output of approximately 23.42 kW, achieving a solar-to-electricity efficiency of 23.40 %. Moreover, the proposed system also produces a water production of 47.42 L/hr with a gained output ratio of 2.74. At the same time, the cost estimate revealed that the price of generated freshwater by the planned desalination facility utilizing the heat recovery loop was approximately 0.54 $/m3.

Consumers Awareness and Attitude towards Sustainable Practices of 5-Star Category Hotels of Mumbai

Background: Hotels have embraced a number of green initiatives in response to growing environmental concerns and the worldwide movement towards sustainable development. However, customer awareness and attitudes towards these practices play a major role in the success of these projects. Objective: To examine the consumer awareness level towards sustainable practices exercised in 5-star category hotels of Mumbai and the consumers attitude towards sustainable practices exercised in these hotels. Methodology: The goals of the current study were achieved through the use of descriptive design. The information was gathered from guests who had stayed at Mumbai’s five-star hotels and came from all across India. Convenience sampling was the approach employed in this study to obtain data. A total of 270 respondents were given the questionnaire; 165 of them completed it, and 156 of those answers were deemed legitimate. The main tool used to collect data is the structured questionnaire. Descriptive statistics namely frequency, percentage, mean and standard deviation were calculated Microsoft excel was used for the data analysis. Results: The mean scores indicate varying levels of awareness, with energy conservation initiatives having the highest mean of 3.44 (SD = 1.56) and community engagement and support the lowest at 2.57 (SD = 1.31). The analysis of consumer attitudes towards sustainable practices in 5-star hotels in Mumbai indicates a strong preference for eco-friendly accommodations. The highest mean score of 3.69 (SD = 1.33) was recorded for the preference for sustainable hotels, however, support for community engagement and educational initiatives was, 2.79 (SD = 1.36) and 2.72 (SD = 1.36), respectively. Conclusion: Consumer awareness of sustainable practices in 5-star hotels in Mumbai is strongest in areas like energy conservation, waste management, and water conservation, which are well-implemented and promoted. Consumer generally have a positive attitude towards sustainability initiatives, particularly those that are visible, like energy and water conservation. Others require enhanced visibility and engagement to elevate their perceived importance.

Advancing Sustainability through Energy Innovation and Climate Action: Insights from ETUT

This study explores the potential of sustainable energy solutions to address global energy challenges and mitigate climate change. Oguz Han Engineering and Technology University (ETUT) serves as a case study, displaying its innovative approaches to energy efficiency, waste management, and renewable energy production. The university's implementation of energy-saving devices, smart building automation systems, and LED lighting demonstrates a commitment to reducing carbon emissions and promoting sustainable practices. Additionally, the study investigates the conversion of plastic waste into liquid fuel and the production of bioethanol from cotton stalks as potential solutions for waste management and renewable energy generation. Further, the research explores the deposition of thin film bismuth vanadium tetraoxide for hydrogen production, highlighting its potential as a clean energy source. Overall, this study emphasizes the importance of sustainable energy solutions and the role of academic institutions in driving innovation and promoting a greener future.

Photoelectric-coupled multilayer smart glass synergistically regulated with doped nanoparticles of GaAs and IST phase change layers

Windows are the main part of the building energy exchange. Most of the solar energy that passes through the windows is reflected or converted into heat loss, which causes a great waste of energy. To improve the energy utilization, this work presents the design of a multilayer glass with a GaAs layer doped by nanoparticles and an IST (In3SbTe2) phase change layer. The dielectric function of the doped layer is obtained by the Maxwell-Garnett effective medium theory. The transmittance and the photoelectric conversion efficiency are calculated through the transfer matrix method. With the genetic algorithm, the structure parameters are optimized to achieve the high values of both average transmittance in human-eye response band and photoelectric conversion energy. The average transmittance of the human eye response band for the structure containing only GaAs doped layer reaches 0.5–0.6, and the average photovoltaic conversion efficiency is above 0.2. After adding the IST phase change layer, the difference of ranges of the spectral regulation between the two phases in the metal nanoparticle doped structure can reach more than 0.25. The light transmission regulation is realized by active phase change. This work provides an optimization-based analysis approach and a possible design of the multilayer smart glass.

A Diagnostic Approach to Improving the Energy Efficiency of Production Processes—2E-DAmIcS Methodology

This article presents the issue of energy waste in manufacturing processes, focusing on reducing unnecessary energy consumption and CO2 emissions. A significant challenge in modern production is identifying and minimizing energy waste, which not only increases operational costs but also contributes to environmental degradation. An improvement methodology referred to as 2E-DAmIcS is proposed. A distinguishing feature of the methodology is a risk map of energy waste in the production process. Application of the methodology is demonstrated using the example of a lead–acid battery production process. It is shown that even small but well-diagnosed changes to the process make it possible to significantly reduce energy consumption. The proposed methodology offers practical tools for managers and decision-makers in various industries to systematically identify and minimize energy waste. It highlights the importance of cross-disciplinary collaboration among specialists in technology, energy consumption, and statistical analysis to optimize energy use. By applying this approach, companies can achieve both financial savings and environmental benefits, contributing to more sustainable production practices.

On-demand beamforming and wide dynamic power range for WPT and EH applications

Abstract This work delves into advancements in wireless power transfer (WPT) and radiofrequency (RF) energy harvesting (EH), focusing on on-demand beamforming and wide-dynamic power range technologies. These innovations promise significant improvements in efficiency and adaptability for wireless energy systems. For transmitting RF power, on-demand beamforming enhances power delivery precision by accurately targeting specific devices, minimizing energy waste, and maximizing received power. This technology is particularly useful in dynamic environments with constantly changing device positions, ensuring stable power levels and effective real-time power transfer, such as for mobile device charging. For receiving RF power, wide-dynamic power range implementation allows EH and WPT systems to adjust power output across a broad spectrum, optimizing energy use and extending device lifespan. This capability supports scalability, accommodating devices with varied power needs, also enabling new applications in consumer electronics, healthcare, smart homes, and cities, and enhancing energy management in smart infrastructures. Additionally, this study explores three-dimensional (3D)-printable antennas and RF circuitries for battery-free applications. The versatility of 3D printing allows the creation of complex, efficient, and customizable RF components, fostering innovative battery-free solutions. Integrating on-demand beamforming and wide-dynamic power range technologies in EH systems promise improved energy transfer efficiencies, reduced losses, and sustainable, cost-effective wireless power systems.