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The fourth industrial revolution has necessitated the integration of sustainability and customer centricity into modern manufacturing systems, thereby emphasising their significance. However, the production process itself is characterised by inherent uncertainties. Hence, this paper primarily focuses on the multi-objective flexible job shop rescheduling problem with uncertain processing time and new job insertions. Two fuzzy constraints are incorporated: fuzzy processing time and fuzzy due date, which aim to model the uncertainty of processing time and ensure flexibility in meeting deadlines. The objective function aims to optimise both customer satisfaction and energy consumption. To address two types of rescheduling problems: time delay caused by fuzzy features and new job insertions, this paper is dedicated to providing a hybrid adaptative rescheduling strategy that combines a satisfaction-driven policy with two specific rescheduling methods. Moreover, an enhanced version of the non-dominated sorting genetic algorithm II (NSGA-II) is developed, incorporating population initialisation rules and multiple genetic operators to obtain solutions of superior quality. Experimental studies have demonstrated the impact of time delay caused by fuzzy features on customer satisfaction. The proposed hybrid adaptative rescheduling strategy and methods exhibit significant improvements in rescheduling plan performance under uncertain environments. [ABSTRACT FROM AUTHOR]

The dramatic increase in Chinese residents' income has driven a sharp increase in the purchase of household durable metal products (HDMPs), which are classified into automobiles and other durable products, mainly appliances and electronics. To explore the energy efficiency potential of the closed-loop supply chain (CLSC) for the HDMP industry in China, we develop a comprehensive evaluation framework based on dynamic material flow analysis and life cycle assessment. The results show that the demand for appliances and electronics and for electric cars will reach peaks in 2028 and 2035, respectively, and annual scrap for appliances and electronics and for electric cars will reach peaks in approximately 2036 and 2040, respectively. The recycling of scrap iron, aluminium, and copper can achieve energy conservation of approximately 72%, 94%, and 82%, respectively, compared with metal primary production, while remanufacturing processes can further reduce energy consumption by approximately 68%, 57%, and 72%, respectively. If the energy consumption of metal production, recycling rate, and remanufacturing rate can reach internationally advanced levels by 2035, approximately 50.5 Mtce (million-ton coal equivalent) will be saved compared to low-speed development by 2050; among these factors, the regeneration of waste metals can achieve the most significant energy efficiency effect. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

Our study uses EnergyPlus simulations to examine whole-building demand and energy end-use profiles for different design options and then uses these outputs to evaluate cost and carbon impacts of each scenario in Xendee, a modeling platform designed to “right size” and balance investments in distributed energy resources (DER). Our results show that efficiency measures are key to meet the ambitious performance metrics for this project; however, most of the technology potential occurs for heating, ventilation and air conditioning (HVAC) or domestic hot water (DHW) loads which are a relatively small portion of a mid-rise multifamily building’s overall energy use. The most meaningful strategies to reduce or shift loads for this building include DHW load shifting, energy recovery ventilation, dynamic ventilation, and ceiling fans. Envelope strategies improve overall annual building performance but become an issue when lower heat loss increases cooling during the critical afternoon peak. Compared to efficient, packaged air source heat pumps, a hydronic heating and cooling system (also serving DHW loads) with thermal energy storage has the best energy performance, highest load shifting capability, and best thermal resilience during outages. But because heating and cooling demands are small and hydronic systems are expensive, the net benefits of thermal energy storage are not substantial. Sizing on-site generation and storage systems to cover the “worst case” outage conditions significantly drives up system size and cost. Even small deviations from 100% coverage (95%, or 99%) can dramatically reduce size and cost without a very meaningful change in resilience.

Conventional wisdom and standard industry practice is to setback zone temperature setpoints when commercial buildings are unoccupied at night. The HVAC systems then operate in warmup mode to recover zone temperatures prior to the start of occupancy, sometimes with an optimal start algorithm. These strategies were intended to reduce HVAC energy consumption when originally developed decades ago but are due for re-examination given the significant changes in HVAC systems that have since occurred. In particular, the changes currently underway with the movement toward electrification present new design considerations and priorities. Warming up a building as fast as possible may not be the best strategy in terms of energy use, operating cost, or carbon emissions. This article discusses some of the downfalls of conventional morning warmup practices, suggests an improved strategy, and shows the results from a pilot field demonstration test.

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

Conventional wisdom and standard industry practice is to setback zone temperature setpoints when commercial buildings are unoccupied at night. The HVAC systems then operate in warmup mode to recover zone temperatures prior to the start of occupancy, sometimes with an optimal start algorithm. These strategies were intended to reduce HVAC energy consumption when originally developed decades ago but are due for re-examination given the significant changes in HVAC systems that have since occurred. In particular, the changes currently underway with the movement toward electrification present new design considerations and priorities. Warming up a building as fast as possible may not be the best strategy in terms of energy use, operating cost, or carbon emissions. This article discusses some of the downfalls of conventional morning warmup practices, suggests an improved strategy, and shows the results from a pilot field demonstration test.

Adoption of residential behind-the-meter solar photovoltaic-plus-storage systems (PVESS) is driven, in part, by customer demand for backup power. However, there is limited understanding of how these systems perform over a range of building stock conditions that will evolve with future efficiency and electrification trends, posing challenges for identifying optimal electric resiliency investments. This study quantifies how residential energy consumption impacts the capability of PVESS to provide home backup power during long-duration power interruptions. We model statistically representative distributions of the residential building stock and estimate storage sizes required to provide backup power as a series of building envelope efficiency, load flexibility, and electrification measures are applied. For the baseline building stock, median storage size requirements range from 10 kWh in temperate weather conditions to 90 kWh in hot climates for a 3-day power interruption. Applying energy efficiency and temperature set-point adjustments reduce storage size requirements by 2–45 kWh (16%–53 %). In hot locations, heat pump retrofits reduce median storage sizing by an additional 10–30 kWh while in cold locations, they drive 10–50 kWh of storage capacity increase. Our results suggest that bi-directional EV charging may be essential to enabling PVESS backup of heating and cooling, given their typically large kWh sizes.

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant. [ABSTRACT FROM AUTHOR]

In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69-91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.

The compound impacts of heatwaves and power outages pose a serious indoor heat-related health risk for residents living in disadvantaged communities (DACs) with limited or no air conditioning. In this study we selected 13 heat vulnerable multifamily buildings in El Monte, in Los Angeles County, and employed CityBES to evaluate their energy and thermal resilience performance. A retrofit package with seven passive and low-power active measures—cool roof, cool wall, window solar film, air sealing, internal blinds, natural ventilation, and ceiling fan—was evaluated under 2018 weather conditions and projected 2058 future weather conditions. Results show: (1) under the 2018 weather conditions, the retrofit package reduces the peak electricity load by 19 % and reduces the annual energy cost by $183 per housing unit; (2) the housing units without air conditioning would face heat danger conditions throughout the heatwave period. Although the retrofit package could reduce the heat danger hours by 50 % in 2018 and 34 % in 2058, air conditioning is a life-essential need for residents during heatwaves. These results indicate that, during the decision making of energy and climate retrofits for housing in DACs, policymakers and building owners should consider the co-benefits of reducing indoor heat-related mortality while reducing energy cost.

Natural gas combustion to serve space heating hot water systems causes approximately one third of large commercial building energy use in California. This project evaluated an innovative set of non-proprietary, cost-effective methods to reduce energy consumption and associated emissions from these systems. The project demonstrated 70% natural gas savings and substantial electricity savings in two large office buildings, yielding total utility cost savings of approximately $110,000 (or $0.5/ft²) per year. The project also conducted detailed studies on distribution losses and boiler efficiency in several buildings; measured performance of key components in laboratory tests; gathered and analyzed data from hundreds of buildings to evaluate actual performance of these systems; and provided a public dataset to inform future retrofits, research, and code development. The research also highlighted characteristics that make a building a good candidate for retrofit so these results can be scaled. Market transformation activities included 10 journal and conference publications, policy recommendations and a design guide. Based on these findings and other recent work, the opportunity for similarly large emissions reductions appears to be common within the existing large commercial building stock. The resources provided by this project can aid stakeholders in achieving California’s goals to decarbonize buildings.

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementationsexist. This paper describes the development and deployment of an open-sourceWi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the plannedintegration with the building energy management and control system at the building scale. Overa year of Wi-Fi data was gathered into distinct academic periods, including fall and springsemester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

An experimental analysis of the performance of packed-bed latent thermal energy storage system using paraffin wax as the phase change material is carried out for plain and dimpled spherical capsules. Two different inlet temperatures (70°C and 35°C) of heat transfer fluid (HTF), the flow rates ranging from 1 L/min to 5 L/min. The study reports charging time, energy and exergy efficiency. The dimpled spherical capsules charge faster than plain capsules, the energy and exergy efficiency analysis showed that dimpled capsules yields higher energy efficiency of 90.86%, 97.12% and 89.06% respectively for 1 L/min, 3 L/min and 5 L/min. [ABSTRACT FROM AUTHOR]

The study aims to optimize the poly-generation of power and urea through the thermochemical conversion of biomass feedstock into hydrogen-rich syngas using steam gasification. This hydrogen-rich syngas serves as a crucial intermediate for producing valuable products, including power and urea. Biomass wastes such as date pits, manure, sludge, and food waste are utilized in this poly-generation process. Using Aspen Plus process modeling software, the system integrates biomass steam gasification with the cogeneration of urea and power, simulating the system's performance. A sensitivity analysis assesses the effects of carbon dioxide utilization and the power-to-urea splitting ratio on the system's overall energy and heat demands. The optimized results of the thermodynamic assessment of the integrated system demonstrate an overall energy efficiency of 52.30% and exergy efficiency of 56.40%, resulting in a power generation of 39.49 MW, a urea production rate of 2.8 kg/s, and a total steam production of 15.56 kg/s. These findings provide valuable insights for determining the optimal power ratio to urea production. • Optimal feedstock blending strategies are employed to enhance hydrogen yield. • Biomass steam gasification yields hydrogen-rich syngas for power and urea synthesis. • Aspen Plus simulation models integrated biomass gasification and cogeneration system. • Sensitivity analysis examines CO 2 utilization and power-to-urea ratio effects. • The system achieves 52.30% energy efficiency and 56.40% exergy efficiency. [ABSTRACT FROM AUTHOR]

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. [ABSTRACT FROM AUTHOR]

ABSTRACT This research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments. [ABSTRACT FROM AUTHOR]

IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protocol compared to the Ride algorithm, highlighting its potential for optimizing network performance and improving resource utilization in IoUT environments. [ABSTRACT FROM AUTHOR]