Search Results (50)

In this study, we comprehensively analyzed material consumption (fuel, hydraulic oil, lubricants, and AdBlue fluid) and estimated carbon dioxide emissions during logging operations. This study was carried out in the northeastern part of Poland. Four harvesters and four forwarders representing two manufacturers (John Deere-Deere & Co., Moline, USA, and Komatsu Forest AB, Umeå, Sweden) were analyzed to compare their operational efficiency and constructional influences on overall operating costs. Due to differences in engine emission standards, approximate greenhouse gas emissions were estimated. The results indicate that harvesters equipped with Stage V engines have lower fuel consumption, while large forwarders use more consumables than small ones per hour and cubic meter of harvested and extracted timber. A strong positive correlation was observed between total machine time and fuel consumption (r = 0.81), as well as between machine time and total volume of timber harvested (r = 0.72). Older and larger machines showed about 40% higher combustion per unit of wood processed. Newer machines meeting higher emission standards (Stage V) generally achieved lower CO₂ and other GHG emissions compared to older models. Machines with Stage V engines emitted about 2.07 kg CO₂ per processing of 1 m³ of wood, while machines with older engine types emitted as much as 4.35 kg CO₂ per 1 m³—roughly half as much. These differences are even more pronounced in the context of nitrogen oxide (NO_x) emissions: the estimated NO_x emissions for the older engine types were as high as ~85 g per m³, while those for Stage V engines were only about 5 g per m³ of harvested wood. Continuing the study would need to expand the number of machines analyzed, as well as acquire more detailed performance data on individual operators. A tool that could make this possible would be fleet monitoring services offered by the manufacturers of the surveyed harvesters and forwards, such as Smart Forestry or Timber Manager. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Air-pollution-related issues, including the rise in carbon dioxide emissions, require, among others, solutions that include using electric vehicles supplied by the energy obtained from renewable sources. These solutions also include the infrastructure for electric vehicle charging. However, the existing systems mostly employ independent subsystems (such as subsystems for the control of electric vehicle chargers, subsystems for the control of smart battery storage, etc.), leading to hardware redundancy, software complexity, increased hardware costs, and communication link complexity. An architecture of a system for remotely controlling a renewable-energy-source-powered sustainable electric vehicle charging station, which overcomes these deficiencies, is presented in this paper. Consideration is also given to the sizes and combinations of different parts (renewable sources, batteries, chargers, etc.) for various purposes (households, replacing current gas stations, big parking spaces in shopping centers, public garages, etc.). The ability to integrate a wide range of features into one system helps to optimize the use of several subsystems, including the ones that control electric vehicle chargers remotely, smart storage battery remote control, smart electricity meter remote control, and fiscal cash register remote control, creating a sustainable and economically efficient solution. In this manner, consumers of electric vehicles will have easier access to renewable-energy-powered sustainable charging stations. This helps to reduce the amount of air pollution and its harmful effects, including climate change, by promoting the use of electric vehicles that are powered by renewable energy sources. The energy independence and sustainability of the station were considered in such a way that the owner of the station achieves maximum economic benefits. Full article

Cities host over 50% of the world’s population and account for nearly 75% of the world’s energy consumption and 80% of the global greenhouse gas emissions. Consequently, ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with emphasis on the use and management of energy, under the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced. Electrification associated with the use of renewable energy generation is increasingly seen as the most efficient way to reduce the impact of energy use on GHG emissions and natural resource depletion. Electrification poses significant challenges to the development and management of the electrical infrastructure, requiring the deployment of Smart Grids, which emerge as a key development of Smart Cities. Our review targets the intersection between Smart Cities and Smart Grids. Several key components of a Smart City in the context of Smart Grids are reviewed, including elements such as metering, IoT, renewable energy sources and other distributed energy resources, grid monitoring, artificial intelligence, electric vehicles, or buildings. Case studies and pilots are reviewed, and metrics concerning existing deployments are identified. A portfolio of 16 solutions that may contribute to bringing Smart Grid solutions to the level of the city or urban settings is identified, as well as 11 gaps existing for effective and efficient deployment. We place these solutions in the context of the energy trilemma and of the Smart Grid Architecture Model. We posit that depending on the characteristics of the urban setting, including size, location, geography, a mix of economic activities, or topology, the most appropriate set of solutions can be identified, and an indicative roadmap can be built. Full article

Real-time monitoring, data-driven decisions, and energy consumption optimization have reached a new level with IoT advancement. However, a significant challenge faced by intelligent nodes and IoT applications resides in their energy requirements in the long term, especially in the case of gas or water smart meters. This article proposes an algorithm for smart meters’ energy consumption optimization based on IoT, LoRaWAN, and NB-IoT using microcontroller-based development boards, PZEM004T energy meters, Dragino LoRaWAN shield, or BG96 NB-IoT modules. The algorithm adjusts the transmission time based on the change in data in real-time. According to the experimental results, the energy consumption and the number of packets transmitted significantly decreased using LoRaWAN or NB-IoT, saving up to 76.11% and 86.81% of the transmitted packets, respectively. Additionally, the outcome highlights a notable percentage reduction in the energy consumption spike frequency, with NB-IoT achieving an 87.3% reduction and LoRaWAN slightly higher at 88.5%. This study shows that adaptive algorithms are very effective in extending the lifetime of IoT nodes, thereby providing a solid baseline for scalable, lightweight, energy-monitoring IoT applications. The results could help shape the development of smart energy metering systems and sustainable IoT. Full article

Ultrasonic flowmeters are essential sensor devices widely used in remote metering systems, smart grids, and monitoring systems. In these environments, a low-power design is critical to maximize energy efficiency. Real-time data collection and remote consumption monitoring through remote metering significantly enhance network flexibility and efficiency. This paper proposes a low-complexity structure that ensures an accurate time-of-flight (ToF) estimation within an acceptable error range while reducing computational complexity. The proposed system utilizes Hilbert envelope detection and a differentiator-based parallel peak detector. It transmits and collects data through ultrasonic transmitter and receiver transducers and is designed for seamless integration as a node into wireless sensor networks (WSNs). The system can be involved in various IoT and industrial applications through high energy efficiency and real-time data transmission capabilities. The proposed structure was validated using the MATLAB software, with an LPG gas flowmeter as the medium. The results demonstrated a mean relative deviation of 5.07% across a flow velocity range of 0.1–1.7 m/s while reducing hardware complexity by 78.9% compared to the conventional FFT-based cross-correlation methods. This study presents a novel design integrating energy-efficient ultrasonic flowmeters into remote metering systems, smart grids, and industrial monitoring applications. Full article

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and $R^2$ is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article

The integration of Artificial Intelligence (AI) and smart technologies into safety management is a pivotal aspect of the Fourth Industrial Revolution or Industry 4.0. This study conducts a systematic literature review to identify and analyze how AI and smart technologies enhance safety management across various sectors within the Safety 4.0 paradigm. Focusing on peer-reviewed journal articles that explicitly mention “Smart”, “AI”, or “Artificial Intelligence” in their titles, the research examines key safety management factors, such as accident prevention, risk management, real-time monitoring, and ethical implementation, across sectors, including construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other diverse industries. AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. For instance, in the energy and power sector, intelligent gas meters and automated fire suppression systems manage gas-related risks effectively, while in the health sector, AI-powered health monitoring devices and mental health support applications improve patient and worker safety. The analysis reveals a significant trend towards shifting from reactive to proactive safety management, facilitated by the convergence of AI with IoT and Big Data analytics. Additionally, ethical considerations and data privacy emerge as critical challenges in the adoption of AI technologies. The study highlights the transformative role of AI in enhancing safety protocols, reducing accident rates, and improving overall safety outcomes across industries. It underscores the need for standardized protocols, robust AI governance frameworks, and interdisciplinary research to address existing challenges and maximize the benefits of AI in safety management. Future research directions include developing explainable AI models, enhancing human–AI collaboration, and fostering global standardization to ensure the responsible and effective implementation of AI-driven safety solutions. Full article

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and $R^2$ is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations. Full article

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. Full article

Buildings play a significant role in the rise of energy consumption and carbon emissions. Building operations are responsible for 28% of the world’s carbon emissions. It is crucial, therefore, to evaluate the environmental impact of various buildings’ operational phase in order to implement sustainable strategies for the mitigation of their energy usage and associated carbon footprint. While numerous studies have been conducted to determine the carbon footprint of conventional building operation phases, there are still a lack of actual data on the operational carbon (OC) emissions of mass timber buildings. There is also a lack of research pertaining to the operational carbon of buildings within larger campuses and their inherent energy usage. This study, therefore, aims to quantify empirical data on the carbon footprint of a mass timber building, using, as a case study, the recent Adohi Hall building, situated at the University of Arkansas, Fayetteville. The study also aims to examine and identify the best energy use scenarios for the campus building under consideration. The research team obtained data on Adohi Hall’s energy consumption, fuel input usage, and other utilities (such as water, electricity, chilled water, and natural gas) accounting for the operation of the building from 2021 to 2023, a span of three years. The University of Arkansas Facilities Management (FAMA) provided the data. The study relies on the life cycle assessment (LCA) as its primary approach, with SimaPro 9, Ecoinvent v3.7 database, DataSmart, version 2023.1 and the U.S. Life Cycle Inventory (USLCI) database utilized to model the energy and water consumption of Adohi Hall during the operational phase (B6 & B7). The results indicate 4496 kg CO₂ eq emissions associated with the operation per square meter of Adohi Hall over its 50-year lifespan. The study also examines various scenarios of fuel sources leading to carbon emissions and provides insights into reduction strategies during the operational phase of buildings. Among them, the electricity based on a cleaner fuel source diversification, according to realistic expectations and technological advancements projections, results in a 17% reduction in Adohi Hall’s OC. Due to the usage of the combined heat and power (CHP) plant on the campus of the University of Arkansas as a complementary source of electricity and heating for Adohi Hall, the resulting carbon emission is approximately 21% (20.73%) less when compared to similar buildings in the same city but outside the campus. The study, therefore, reveals that CHP plant development is a highly effective strategy for building OC reduction. Full article

Coalbed methane is released externally due to coal mining activities. Given its low concentration, which renders utilization challenging, China annually vents approximately 285 billion cubic meters of coalbed methane into the atmosphere, leading to significant energy waste and greenhouse gas emissions. To enhance the utilization rate of coalbed methane, mitigate these emissions, and promote a “green and low-carbon” energy supply, this article investigates pressure swing adsorption technology for purifying coalbed methane and analyzes the advantages, disadvantages, and application scopes of three processes: separation based on equilibrium effects, kinetic effects, and steric hindrance effects. The research findings reveal that equilibrium effect-based adsorption is particularly advantageous for purifying low-concentration coalbed methane, effectively capturing methane (CH₄). Conversely, when dealing with medium- to high-concentration coalbed methane, methods leveraging kinetic effects prove more favorable. Within the context of equilibrium effects, activated carbon serves as a suitable adsorbent; however, achieving high-purity products entails substantial energy consumption. The methane saturation adsorption capacity of novel activated carbons has reached 2.57 mol/kg. Kinetic effect-based adsorbents, primarily carbon molecular sieves and zeolite molecular sieves, are characterized by lower energy demands. Currently, coal-based molecular sieves have achieved a CH₄/N₂ equilibrium separation factor of 4.21, and the amount of raw coal required to produce one ton of carbon molecular sieve has decreased to 2.63 tons. In light of the rapid advancement of intensive coal mining operations and the swift implementation of smart mine construction, there is an urgent need to intensify research on large-scale purification technologies for low-concentration coalbed methane. This will provide the technical foundation necessary for achieving “near-zero emission” of mine gas and facilitate the achievement of the goals of carbon peak and carbon neutrality. Full article

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses. Full article

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO₂ consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels. Full article

This study investigates the water–energy–carbon (WEC) nexus in cities across four countries, namely the United Kingdom (UK), the United States of America (USA), Australia (AUS), and South Korea (KOR), over a decade, from 2011 to 2021. The primary objective is to assess the impact of smart water metering (SWM) implementation on the WEC nexus, with a specific focus on domestic hot water (DHW) consumption and associated greenhouse gas (GHG) emissions. The analysis of the collected data reveals diverse patterns among cities with varying levels of SWM implementation. Notably, cities with higher SWM implementation demonstrated significant reductions in water consumption, indicating the effectiveness of the efficient water consumption and demand management achieved through SWM. The study emphasizes the importance of addressing GHG emissions related to water heating, with the carbon intensity of water heating identified as a critical factor in this context. To achieve net reductions in GHG emissions, intensive efforts are required to simultaneously decrease both DHW consumption and the carbon intensity of water heating. The research findings highlight the potential for substantial GHG emissions reductions by combining SWM implementation with the decarbonization of water heating. By recognizing the interdependencies within WEC systems, this study underscores the significance of SWM in advancing toward a carbon-neutral society. In conclusion, this study contributes valuable insights into the WEC nexus and emphasizes the role of SWM in achieving sustainability goals. It advocates for integrated policies to effectively address the interconnected issues of the WEC nexus for effective climate change mitigation. Full article