Energy Waste Research Articles

Navigating the High Costs of Energy Storage: Challenges and Solutions for Sustainable Integration

As global energy consumption continues to rise, it is imperative that energy supply systems are aligned to meet this growing demand. However, the current state of the energy industry is characterized by prohibitively high costs associated with energy storage systems, alongside significant inefficiencies and energy wastage. This paper first introduce two main energy storage system: Lithium-ion battery, Compressed Air Energy Storage(CAES), explores the underlying factors contributing to these elevated costs and examines the current energy landscapes of key regions, including Asian countries, particularly China, the United States, and Europe. Through a comparative analysis, the paper identifies the differences and underlying causes that drive these disparities. Furthermore, it proposes strategic solutions to address these cost challenges, such as reforming tax systems to incentivize innovation, enhancing supply chain efficiency through better integration and localization, and investing in research and development to drive technological advancements. By tackling these issues, the global energy sector can advance towards more cost-effective and sustainable energy storage solutions, thereby supporting the transition to a low-carbon future and ensuring energy security for generations to come.

Long-term health effects of a third-generation waste-to-energy plant: the experience of Turin (Italy).

The long-term study on adverse health effects of the third-generation waste-to-energy (WTE) plant located in Turin, Italy, is part of a broader health surveillance system. We considered 369 236 subjects living in areas with different levels of emission fallout, as well as a control group, from 1 January 2014, until the end of the follow-up period. Hospital admissions for cardiac diseases (ICD-IX: 390-429), ischaemic heart diseases (ICD IX: 410-414), chronic heart failure (ICD IX: 428.0, 428.2, 428.9), cerebrovascular diseases (ICD IX: 430-438), acute respiratory diseases (ICD IX: 460-466, 480-487), and COPD (ICD IX: 490-492, 494, 496) were evaluated for the population considered. Cox models were used, considering individual characteristics and overall environmental exposure. We also considered all births (n = 8296) of women residing in the area at the time of delivery during the study period. Log-binomial models were run separately for each outcome (sex ratio, multiple births, preterm births, on term low birthweight and small for gestational age births), adjusting for exposure to other pollution sources and maternal characteristics. Miscarriages were evaluated using hospital admissions registries. No relationship was found for the outcomes considered in the wider area, neither with hospital admissions nor with adverse reproductive outcomes. There is an association with chronic heart failure and ischaemic heart diseases in the maximum exposure area, but the small number of events suggests caution in interpreting this result. This study confirm results of other health surveillance lines, showing no evident harmful effects of the WTE plant.

The Potential Role of Africa in Green Hydrogen Production: A Short-Term Roadmap to Protect the World’s Future from Climate Crisis

The global need for energy has risen sharply recently. A global shift to clean energy is urgently needed to avoid catastrophic climate impacts. Hydrogen (H2) has emerged as a potential alternative energy source with near-net-zero emissions. In the African continent, for sustainable access to clean energy and the transition away from fossil fuels, this paper presents a new approach through which waste energy can produce green hydrogen from biomass. Bio-based hydrogen employing organic waste and biomass is recommended using biological (anaerobic digestion and fermentation) processes for scalable, cheaper, and low-carbon hydrogen. By reviewing all methods for producing green hydrogen, dark fermentation can be applied in developed and developing countries without putting pressure on natural resources such as freshwater and rare metals, the primary feedstocks used in producing green hydrogen by electrolysis. It can be expanded to produce medium- and long-term green hydrogen without relying heavily on energy sources or building expensive infrastructure. Implementing the dark fermentation process can support poor communities in producing green hydrogen as an energy source regardless of political and tribal conflicts, unlike other methods that require political stability. In addition, this approach does not require the approval of new legislation. Such processes can ensure the minimization of waste and greenhouse gases. To achieve cost reduction in hydrogen production by 2030, governments should develop a strategy to expand the use of dark fermentation reactors and utilize hot water from various industrial processes (waste energy recovery from hot wastewater).

Design and optimization of distributed energy management system based on edge computing and machine learning

With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.

Aligning Waste-to-Energy (WtE) Projects with Circular Economy Principles: A Comparative Analysis of the European Union and India's Path Forward

The growing global challenge of waste management has reached alarming levels, with approximately 4.5 million tonnes of waste generated every day worldwide—a figure that is expected to exceed 8 million tonnes by 2050, according to the World Bank. Although there have been notable improvements in how waste is managed, it has transitioned from being just a public concern to becoming a booming industry, particularly seen in the rise of the Waste-to-Energy (WtE) sector across the globe. Given this growing problem, many experts agree that addressing waste requires a shift toward a "circular economy," an approach aimed at minimizing waste altogether. This strategy is in line with the United Nations' 2030 Agenda for Sustainable Development, particularly Sustainable Development Goal 12, which focuses on responsible consumption and production. While WtE technologies seem like a sustainable option, the process of turning waste into energy using advanced facilities raises questions about their true compatibility with circular economy principles. The UN has proposed a framework to guide WtE projects, suggesting that these frameworks can help in the transition to a circular economy—especially in terms of energy recovery. However, this requires innovative public-private partnerships (PPPs) that emphasize people-first values and the adoption of best practices by both governments and the private sector simultaneously. The main focus of this paper is to highlight the need to align WtE projects with circular economy principles to ensure sustainable and effective waste management. While this model has made significant progress in EU ( European Union) countries, it is still in the early stages of development in India. This paper explores the framework used in the EU and discusses how India can successfully adopt and implement similar practices.

Advances and insights into modeling extracellular electron transfer in anaerobic bioprocesses.

Extracellular electron transfer (EET) plays an important role in maintaining redox balance in both natural and engineered anaerobic microbial systems, driving key biochemical processes such as energy generation, bioremediation, and waste degradation. While EET has been characterized in a limited number of microbes and applied in anaerobic digestion and bioelectrochemical systems, further research is needed to explore its mechanism across a broader range of microbial species and anaerobic processes. This review highlights advanced modeling frameworks that provide deeper insights into EET mechanisms and dynamics, aiming to optimize research efforts and minimize time and resource expenditure. Mechanistic models, encompassing thermodynamics and kinetics, are discussed for their utility in calculating conduction rates of electroactive microbes and assessing the energetics of medium chain carboxylic acids production. Genome-scale metabolic models are highlighted for elucidating the roles of cytochromes and conductive pili in the EET pathway. Machine learning is presented as a tool to improve model accuracy and predict EET mechanisms. Furthermore, the integration of quantum mechanics/molecular mechanics methods offers molecular-level insights into electron transfer, while quantum computing addresses limitations of classical computers by simulating complex electron transfer processes in multi-heme cytochromes. Developing advanced modeling techniques will complement experimental techniques, enabling precise predictions and optimization strategies for developing innovative and sustainable anaerobic biotechnologies.

Optimized Spectrophotometric Method for Creatine and Creatinine Quantification using Alpha Naphthol Sulpha Acetamide Dye

A new spectrophotometric method for the reliable determination of creatine and creatinine in plasma, urine and serum has been developed for routine clinical and pharmaceutical use. This technique makes use of the fact that creatine and creatinine can chemically interact with the azo dye Alpha naphthol Sulpha Acetamide (ASA), producing a compound that is an intense orange color and is measurable at 525 nm for creatine and 520 nm for creatinine, respectively. New features include Triton X-100 surfactant and ideal reaction conditions of 30 °C and pH 3 for creatinine and 20 °C and pH 5 for creatine, respectively. The method shows increased molar absorption within the specified concentration ranges and complies with Beer's law. This technique is more friendly to the environment than traditional colorimetric, enzymatic, chromatographic, and immunoassay procedures because it utilizes less hazardous chemicals and biodegradable surfactants. Ethanol is a safer solvent than traditional organic solvents utilized in many processes. It is more environmentally friendly and less harmful. The approach lowers energy consumption and waste by functioning at lower temperatures, consistent with the principles of green chemistry. This method guarantees accurate and dependable identification of creatine and creatinine while promoting environmentally sustainable laboratory practices.

UI UX design in smart home automation applications using the design thinking method

As technology advances, one issue that needs to be taken into account is the consumption of power resources as one of the supports. Technology use needs to be balanced with resource management for electricity use. Furthermore, there is electrical energy waste due to the community's growing requirement for electricity usage. Changes must be taken, such as utilizing energy-saving technologies or moving to ecologically friendly items, to help reduce the usage of electricity resources. Creating applications that are eco-friendly and encourage energy savings is one way that many application developers are now addressing the issues at hand. One such way is by utilizing smart home technology (Smart Home). The design thinking methodology is applied while creating interface design, specifically in the areas of UI and UX design. Every step of the design thinking process—empathize, define, ideate, prototype, and test—will encounter loops indefinitely until a specific circumstance is satisfied. Figma is the tool used in this application to build UI/UX design. By keeping an eye on devices that are no longer in use and tracking potential cost savings, the application design outcomes should be able to lower the use of electrical resources.

Green IT in Healthcare: Leveraging DevOps for Scalable Patient Services

Healthcare organizations encounter rising environmental threats that force them to use sustainable methods while maintaining top patient care standards. This research examines how healthcare uses Green IT to save energy and materials and build environmentally friendly technology networks. DevOps applications help unite developers and operations teams through automated tools and regular software delivery techniques. Through containerization, microservices, and cloud computing technologies, healthcare organizations can create scalable systems that reduce idle resources, which leads to less energy waste. Technology-enhanced monitoring and automation tools help organizations execute routine work more accurately while improving their operations. Healthcare providers who move their data centers to the cloud can make their operations more environmentally friendly while keeping their service quality high. Our research shows organizations how to build a sustainable healthcare system that protects nature and provides excellent medical care while providing practical ways to unite Green IT and DevOps.

On-Chip Cascaded Metasurfaces for Visible Wavelength Division Multiplexing and Color-Routing Meta-Display.

Integrating metasurfaces on-chip offers a promising strategy for modulating and extracting guided waves, suggesting tremendous applications in compact wearable devices. However, despite the full acquisition of on-chip manipulation of optical parameters, including phase, amplitude, and polarization, the functionality of on-chip metasurfaces remains limited by the lack of wavelength selectivity. Here, an on-chip approach to differentiate wavelength components is proposed in the visible regime for wavelength division multiplexing (WDM). Through horizontally cascading on-chip meta-atoms with structural dimension variation and optimization, different wavelength components propagating along the waveguide would be selectively extracted, realizing meta-demultiplexing functionality. More intriguingly, color nanoprinting images or holographic displays can be correspondingly enabled. This approach surpasses conventional free-space meta-devices in terms of exhibiting improved wavelength-selective allocation and eliminating the energy waste caused by spatial multiplexing. We envision that such an on-chip cascading strategy paves the way for next-generation WDM devices in photonic integrated circuits and wearable miniature meta-displays.

The Gasification of Marine and Coastal Resources for Syngas Production: A Review

Coasts are home to one-third of the human population. In the process of energy transition, local biomass and waste resources represent a renewable fuel that can substitute fossil fuels in order to reduce greenhouse gas emissions, hence including marine resources as part of the eligible feedstock for renewable energy production. Gasification regroups different technologies that aim to convert a solid fuel into a useful gas, and has several applications, such as heat production, power generation, and chemical synthesis. Gasification technologies regroup the traditional “dry” processes that use relatively dry fuels, but recent developments have been made with “wet” processes such as hydrothermal gasification, in sub- or supercritical conditions for the water, which can accept wet fuel. This review focuses on scientific articles that performed gasification of marine resources in order to produce a syngas. First, a definition of marine resources is made, followed by the presentation of marine resources studied in the literature. Secondly, this review presents the different types of gasification reactors and their operating conditions, followed by a summary of the different syngas produced with their composition as a performance indicator. Finally, this review exposes the limitations of the current literature and concludes with perspective propositions.

Enhancing Utilization of Municipal Solid Waste Bottom Ash by the Stabilization of Heavy Metals

Waste-to-energy (WtE) is a key part of modern waste management. In the European Union, approximately 500 WtE plants process more than 100 million tons of waste yearly, while globally, more than 2700 plants handle over 500 million tons. Roughly 20% of the waste processed is bottom ash (BA). However, this ash can contain heavy metals in concentrations that may render it hazardous. This paper presents a study focusing on stabilizing municipal solid waste incineration BA using simple and industrially viable treatments. The Slovenian WtE plant operator wishes to install the stabilization process; thus, the samples obtained from the plant were treated (1) with a CO2 gas flow, (2) with water spraying, and (3) with a combination of water spraying and a CO2 gas flow under laboratory conditions. Thermodynamic calculations were applied to define potential reactions during the treatment processes in the temperature range from 0 to 100 °C and to define the equilibrium composition of the treated ash with additions of CO2 and water. The standard leaching test EN 12457-4 of treated ash shows a reduction of over 40% in barium concentration and over 30% in lead concentration in leachates.

Waste to Energy Conversion and Predictive maintenance in Circular Economics Through AI

This paper explores the transformative role of artificial intelligence (AI) in advancing the circular economy through waste-to-energy (WtE) conversion and predictive maintenance. By leveraging AI, waste management processes can be optimized to enhance the efficiency of resource recovery, minimize environmental impact, and generate renewable energy. Predictive maintenance, driven by AI, ensures the smooth operation of industrial equipment, reducing downtime and extending machinery lifespan. This integration supports sustainable production and aligns with important circular economy principles, including biomimicry, lean manufacturing, and the Butterfly Model. The study underscores the potential of AI-driven technologies in fostering a resilient and sustainable future by addressing critical environmental challenges and promoting efficient resource utilization.

Green DevOps in Practice: Using Technology for Environmental Impact.

Through effective technology adoption, this article explores how DevOps methods can significantly reduce carbon emissions and environmental footprints. Teams may quantify and optimize software system energy use by including sustainability indicators into DevOps processes, from deployment and monitoring to CI/CD pipelines. Green coding concepts and techniques like cloud optimization, containerization, and automated resource monitoring reduce waste and increase energy efficiency. Additionally, the paper presents methods for estimating the carbon footprint of software operations, offering practical advice for coordinating DevOps tactics with sustainability objectives. Through the integration of environmental responsibility into DevOps, this study demonstrates how technology can spur innovation and promote environmental sustainability.

Microstructures and Mechanical Properties of SUS 630 Stainless Steel: Effects of Age Hardening in a Tin Bath and Atmospheric Environments

This study investigates the solution-aging treatment of precipitation-hardening SUS 630 stainless steel, alongside an analysis of the carbon emissions generated by the energy consumed during aging treatments. By employing atmospheric and liquid tin as aging media, the research comprehensively explores the effects of aging treatments on the characteristics of 630 stainless steel. The maximum hardness value for the 630 stainless steel was observed after atmospheric aging at 500 °C for 1 h. The given 630 stainless steel obtained its maximum hardness value after atmospheric aging at 500 °C for 1 h, indicating that the formation of secondary precipitates strengthens the steel’s performance. By leveraging the intrinsic characteristics of liquid tin, using it as an aging medium (Sn bath aging) significantly improves the efficiency of the aging process, achieving mechanical properties comparable to those of atmosphere-aged steel. The 630 stainless steel aged in a Sn bath exhibited a refined martensitic matrix with substantial precipitate formation, contributing to superior impact toughness and dynamic fatigue resistance. With an equivalent mass and performance, Sn bath aging notably reduced the duration of the treatment compared to atmospheric aging, leading to substantial energy savings and reduced carbon emissions. The Sn bath treatment, recognized in metallurgical science and heat treatment for its excellent thermal conductivity and recyclability, shows potential to enhance process efficiency and enable low carbon emissions in the heat treatment industry. By highlighting the differences between aging methods, this study provides solutions for optimizing heat treatment processes and thereby achieving industrial advancement and sustainability goals.

Drivers of Industrial Final Energy Consumption in Ghana: A Sectoral Decomposition Analysis of Energy Supply and Demand Drivers.

Energy plays a crucial role in determining Ghana's socioeconomic growth. The rapid growth of demand and the increasing difficulties of energy supply pose a significant challenge to Ghana’s efforts to achieve universal energy access. This study aims to assess the drivers of industrial final energy consumption in Ghana using a sectorial decomposition of energy demand and supply drivers. It applies the additive Logarithmic Mean Divisia Index (LMDI) to decompose industrial final energy consumption in Ghana between 2000-2022. Changes in Final Energy Consumption (FEC) were analyzed by quantifying the contributions from three different factors: activity effect, structure effect, and intensity effect. FEC increased significantly throughout the period with the activity effect being the major promotor. The structure effect promoted and inhibited FEC growth within the 2000-2022 period while the intensity effect largely contributed to a drop in final energy consumption. The activity effect resulted in the net growth of FEC by 136,403 TJ. The effect of the economic structure was oscillating resulting in a net growth of FEC by 9,635.45 TJ. As an inhibitor to FEC growth, the intensity effect resulted in a decline of FEC by 114,081 TJ. The results of this study can help the government to reduce energy consumption by encouraging and promoting energy-saving practices, and incentivizing the adoption of green technologies can effectively curb wasteful energy consumption and foster a culture of responsible energy usage across all sectors of the economy.

Emerging Trends on Hybrid Artificial Intelligence Framework for Transforming Heavy Machinery with Robots and Energy Efficiency

Today, heavy machinery can be equipped with robots operated by artificial intelligence (AI) to streamline operations, decrease human labor, and increase efficiency. Intelligent technologies like these can boost productivity by carrying out routine tasks with extreme accuracy. Since robots reduce risks to human workers, robots with AI algorithms and sensors could be used in hazardous environments. In certain contexts, including building site management, mining, or handling hazardous materials, safety must take precedence above everything else. Analytics from this study, aided by AI, have opened the door to predictive maintenance procedures. To detect equipment problems, the Hybrid Artificial Intelligence Framework (HAIF) examines sensor data in conjunction with historical patterns. The objective is to avoid expensive failures and downtime. Optimization of equipment utilization, energy consumption, and fuel consumption can be achieved by applying AI-driven machine learning algorithms. Several advantages arise from energy waste, including cost avoidance and enhanced operational efficacy. Robots can do exact alignments, measurements, and operations with the help of AI vision technologies. This precision significantly impacts the manufacturing, agricultural, and construction industries. Artificial intelligence has the potential to optimize the allocation of resources, the use of heavy equipment, and supply networks by evaluating massive amounts of data.

Optimum Design and Dynamics Analysis of Intelligent Prosthetic Knee Joint

Background: The intelligent prosthetic knee joint is an essential rehabilitation device for amputee patients to recover from mobility disorders. It must ensure excellent performance when installed on the human body. However, issues such as excessive energy consumption and poor stability during walking are challenges in the design of such prosthetic devices. Objective: To address the issues of wasteful energy expenditure and instability in intelligent prosthetic knee joints during walking, an optimized model of a double rocker prosthetic knee joint mechanism was established with the minimum peak driving torque of the active rod as the optimization goal. Methods: The optimal model was established with the minimum peak driving moment of the prosthetic knee joint mechanism as the optimization objective. Under the constraint conditions of performance and structure, the model was optimized by the composite method, and the optimal structural parameters of the prosthetic knee joint mechanism were obtained. Then, according to the trajectory of the instantaneous rotation center of the knee joint, the double rocker mechanism is optimized so that the prosthesis can better simulate the movement trajectory of normal people. Finally, according to the designed virtual prototype model, a physical prototype of the intelligent prosthetic knee joint test platform is built, the knee joint Angle data is collected, and the collected data is analyzed accordingly. Results: ADAMS software was used to simulate the virtual motion of the optimized model. The results showed that the optimized intelligent prosthetic knee joint mechanism has excellent bionic performance. Its peak driving torque is reduced by 40% and the range of driving torque is reduced by 36%, significantly improving the stability and endurance of the intelligent prosthetic knee joint. Through the experimental test of an intelligent prosthetic knee joint, it is verified that the optimized intelligent prosthetic knee joint meets the normal needs of the human body. Conclusion: The results confirm that the optimized prosthetic knee joint mechanism exhibits excellent bionic performance, and the reduction in driving torque enhances its stability and energy efficiency, validating the correctness and rationality of the proposed optimization model.

Electronic Equipment Power Usage Control and Monitoring System in the Home Internet Of Things (IOT) Based

The objectives of this study are (1) to achieve energy efficiency and cost savings (2) Internet of Things (IoT)-based control and monitoring systems using Node MCU ESP32 as a data processing center (3) enabling data processing from PZEM-004T sensors and sending control commands to solid state relays (SSR) based on user input via a website application. The implementation of this system shows significant potential in reducing energy consumption and costs in households. With real-time feedback on energy consumption, users can make wiser decisions about the use of electronic equipment, thereby reducing energy waste. Remote control capabilities allow users to manage electronic equipment more effectively, improve security, and reduce unnecessary energy consumption. This study shows that manual electricity usage reaches 9.59%, while with the implementation of the IoT system it is only 5.49%, so there is a saving in electricity consumption of 4.1%. This proves that the IoT system is more effective and efficient in managing the power consumption of electronic equipment.

Waste harvesting: lessons learned from the development of waste-to-energy power plants in Indonesia

Purpose The purpose of this research is to analyse the development and implementation of waste-to-energy (WtE) power plants as a strategy to manage municipal solid waste (MSW) in Indonesia. It aims to identify key determinants for the successful adoption of WtE technology in developing countries, focusing on the Benowo WtE power plant, Indonesia’s first large-scale WtE project. Design/methodology/approach This study reviews the development of the Benowo WtE power plant, examining political, regulatory and financial factors influencing its implementation. It analyses the roles of national and local government institutions, highlighting critical elements for the successful deployment of WtE projects in a developing country context. Findings The review shows that the success of WtE projects in Indonesia is significantly influenced by strong local government commitment, supported by political, financial and regulatory backing from the national government. The Benowo WtE power plant’s development demonstrates that with adequate support, WtE can be a viable solution for managing MSW and generating energy in developing countries. The study also identifies barriers to broader WtE adoption in similar contexts. Research limitations/implications The study is limited to 12 WtE projects in Indonesia, with in-depth analysis focused on a single case, potentially overlooking challenges and opportunities in other regions. Furthermore, the study does not include an economic analysis, such as a feasibility assessment. Future research could expand to multiple WtE projects and incorporate primary data collection to provide more comprehensive findings, including feasibility studies of each project. Practical implications The findings provide practical guidance for policymakers and stakeholders in developing countries, identifying critical areas for successful WtE deployment and offering a blueprint based on the Benowo WtE power plant. Social implications Implementing WtE projects can improve waste management, reduce pollution, enhance public health, create jobs and stimulate local economies. The study underscores the importance of government and community collaboration in addressing environmental challenges and fostering shared responsibility. Originality/value This research offers novel insights into WtE technology development in a developing country, specifically focusing on Indonesia’s first large-scale WtE power plant. By analysing the Benowo WtE power plant, the study provides valuable lessons and strategies for other developing nations aiming to adopt WtE solutions for MSW management, emphasising the critical role of government support.