Energy Efficiency Performance Research Articles

Impact of employee engagement on Organizational Productivity

This study aims at establishing the relationship between employee engagement and organizational performance with emphasis on communication, career growth and rewards. This research adopts a quantitative approach with data collected from one hundred workers across the organization departments. These tests indicated that the relationship between EE and actual performance is rather low, thus indicating that although engagement is a critical concept to consider, it cannot solely explain the performance of an organization. Some common factors such as communication and career management came out with a compromise to show that these factors are quite effective depending on how such factors are adopted or perceived by the employees. This paper proposes ways to increase employee participation, such as increasing communication clarity, providing equal opportunity for staff to receive recognition, and creating clear career paths. They are strategies which can assist the organizations in encouraging the employees and may ultimately increase productivity in the long run.

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

This study investigates the light olefin separation using dual dividing wall columns (DWCs) and compares various proportional-integral (PI) controller tuning methods. A novel process-conscious reinforcement learning (RL) approach, utilizing Deep Deterministic Policy Gradient (DDPG), is developed by incorporating domain-specific knowledge into the reward function to ensure compliance with critical process variables and product purity. The DDPG-based PI tuning is evaluated against traditional methods such as Aspen’s recommended initial tuning, Ziegler-Nichols (ZN), and Cohen-Coon (CC). The result shows that the DDPG method significantly enhances control stability and accuracy, reducing error by factors of 11.9, 2.3, and 1.6 compared to Aspen, ZN, and CC, respectively. Additionally, DDPG demonstrates superior energy efficiency, consuming 13% less energy than the next best method. It also reduces purification costs by up to 13.5% and CO2 emissions by 20% compared to other methods. This study highlights the potential of integrating advanced RL techniques into industrial process control, delivering substantial improvements in stability, energy efficiency, economic, and environmental performance.

Development and validation of intelligent load control for VRF air-conditioning system with deep learning based load forecasting

Variable refrigerant flow (VRF) air-conditioning systems have seen significant growth in Asia and its application is expanding globally. Despite the expanded application, most previous studies have focused on developing fault detection and diagnostics to achieve energy-efficient operations than on predicting power consumption. It's very difficult to predict the electrical consumption owing to its complex system configuration and various control strategies. A new control strategy is described for optimal adjustment of the desired target level based on time series forecasting using the optimized sequence to sequence model for VRF systems. Sequence to sequence (seq2seq) model with attention mechanism and Bayesian optimization is developed to predict accurate hourly and daily forecasts and rapid feedback control for fluctuating power consumption for VRF systems. The optimized seq2seq model is integrated into the intelligent load control (ILC). ILC can be used to manage VRF systems by dynamically prioritizing indoor units for curtailment using both quantitative inputs and qualitative rules. Overall, the results demonstrate that the deep learning based control allows coordination of the controllable loads of VRF systems in three commercial buildings. ILC with deep learning manages the power consumption within a desired target level, as well as indoor temperature reflecting the status of controlled indoor units, as objective functions of the control algorithm.

Non-volatile Fermi level tuning for the control of spin-charge conversion at room temperature

Current silicon-based CMOS devices face physical limitations in downscaling size and power loss, restricting their capability to meet the demands for data storage and information processing of emerging technologies. One possible alternative is to encode the information in a non-volatile magnetic state and manipulate this spin state electronically, as in spintronics. However, current spintronic devices rely on the current-driven control of magnetization, which involves Joule heating and power dissipation. This limitation has motivated intense research into the voltage-driven manipulation of spin signals to achieve energy-efficient device operation. Here, we show non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. We use a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion we perform ferromagnetic resonance and inverse Edelstein effect experiments. The sign change of output voltage is derived by the change of carrier type, which can be achieved solely by a voltage pulse. Our results provide an alternative approach for the electric-field control of spin-charge conversion, which constitutes a building block for the next generation of spin-orbitronic memory and logic devices.

Design of reliable and fast Schmitt trigger 10T SRAM cells using in-memory computing

Abstract Microprocessors, integrated data storage systems, and cache memories are just a few applications that can benefit from Static random access memory (SRAM). Computing In memory provides a solution for the von Neumann bottleneck problems. Simultaneously, it removes needless repeated data transmission between CPU and memory. Schmitt trigger was utilized for energy-efficient operation to get high reading and writing capacity. Two cross-coupled inverters with a transistor in between increase the design writing process. Furthermore, voltage boost logic is prioritized to maximize the node capacity. Using two dummy columns and dummy rows in RAM, the Replica technique controls the flow of signals via the core. The simulated results are compared to the designs that already exist. The simulation results show that the leakage power of the proposed design is 0.75 nw at 0.6 volts. The write static noise margin (WSNM) of the proposed cell is twice that of SEDF10T, which can be helpful for high-performance computing applications.

Nestlé's role in global food security, climate change and consumer health

Nestlé, as a global leader in the food and beverage industry, plays a pivotal role in addressing critical challenges related to food security, climate change, and consumer health. This paper examines Nestlé's strategies and initiatives aimed at contributing to global food security by enhancing sustainable agricultural practices, reducing food waste, and promoting responsible sourcing. The study also explores the company's efforts to mitigate the impact of climate change through carbon reduction initiatives, sustainable packaging, and energy-efficient operations. Additionally, Nestlé's focus on consumer health is analyzed, highlighting its commitment to offering nutritious products, promoting healthy lifestyles, and reducing harmful ingredients such as sugar and salt in its offerings. By evaluating Nestlé's multi-faceted approach, this paper underscores the company's potential to influence positive change at the intersection of food security, environmental sustainability, and public health, while also identifying areas for future improvement and broader impact.

Design method of multi-stage air treatment system with circulating air for high efficiency in year-round working conditions

Owing to the large temperature rise/drop during the fresh air treatment process, the multi-stage treatment systems for fresh air have good energy-saving effects. However, the energy-saving potential of multi-stage treatment systems for circulating air and the design of a multi-stage circulating air treatment (MCAT) system that operates efficiently under variable conditions throughout the year remain unclear. Therefore, this study proposed a multi-condition design method to ensure the annual energy-efficient operation of the circulating air system. Firstly, the appropriate number of air treatment stages (NUM) and heat transfer temperature difference between air and water (Δt) for each single condition are determined and an appropriate heat transfer area is obtained. Subsequently, an MCAT system suitable for multiple working conditions throughout the year is designed by searching for the common values of the appropriate heat transfer area per stage. Finally, three types of buildings and six cities located in different climate zones are selected to explore the adaptability of the appropriate Δt. The results show that (1) for each single working condition, the appropriate ranges of NUM and Δt are 1–2 and 2–6 °C respectively, which can keep the system energy consumption at a low level. (2) There is generally a common range for multiple heat transfer areas per stage designed under different working conditions, and an MCAT system designed based on this common range can achieve energy-efficient operation under multiple annual conditions. (3) In an office building in Beijing, compared to the traditional one-stage air treatment system, the maximum and the annual average energy-saving rates of the MACT system designed based on the multi-condition design method are 16.1 % and 6.3 %, respectively.

Energy-efficient configuration and scheduling framework for electric construction machinery collaboration systems

The electrification of construction machinery has created a perceptible future trend of the development of electric construction machinery collaboration systems (ECMCSs). However, there is a lack of research on energy-efficient operation of ECMCS. This paper proposes a theoretical configuration and scheduling framework promoting the applications of ECMCSs. In the configuration stage, this paper considers the effect of charging time and proposes an electric matching factor to achieve an optimal system configuration. In the scheduling stage, a multi-objective scheduling problem is formulated for achieving energy-efficient system operation, which considers the transport volume, cost and idle time. A validation of the framework was carried out using a case study that found the optimal system solution, while the advantages of the considered ECMCS compared to a fossil fuel-powered system were discussed. The impact of battery and charging technology developments was also assessed. This framework can be widely applied to deployment of ECMCSs.

An efficient CNN accelerator for pattern-compressed sparse neural networks on FPGA

Currently, the sparsity of weights and activations are mainly utilized to improve the energy efficiency and computational performance of CNN accelerators. However, the irregular sparsity of weights and activations can lead to problems such as mapping access conflict and imbalanced workload, which poses great challenges to the efficient computation of sparse networks. To address those problems, this paper proposes a CNN accelerator for pattern-compressed sparse neural networks on FPGA to achieve efficient inference acceleration. It mainly includes three parts: first, we propose a convolutional kernel grouping fusion method to achieve a balanced workload of weights. Second, we propose an activation hash mapping conflict reduction method to drastically reduce the mapping access conflict rate. Finally, we further propose a hierarchical mapping computing architecture to achieve efficient processing of mapping access conflicts and balanced adaptation of weight loads. To verify the effectiveness of the method proposed in this paper, our accelerator is designed and implemented on the Zynq UltraScale+ MPSoC ZCU102 evaluation board and evaluated by running VGG16 and ResNet50 networks. The experimental results show that the method proposed in this paper can reduce the mapping conflicts by more than 97 % and improve the utilization rate of processing element (PE) by 1.67×. Furthermore, the accelerator implemented in this paper can achieve 393.2 GOPs throughput, 1.40–3.25× computational performance, and 1.11–7.34× energy efficiency improvement.

The art of orchestrating nautical services in a port call: A literature classification

Abstract Global carbon dioxide emissions are forecast to increase by around 15% by 2050 compared to 2020. In order to move towards a sustainable transition to net zero emissions by 2050, more energy-efficient operations are a prerequisite. In response, terminals are looking for ways to optimise port processes to reduce energy consumption while also keeping waiting time and costs low. This paper focuses on optimisation approaches for the individual actors in the nautical service that can support these objectives. A comprehensive review, classification and discussion of the relevant literature on current methods and procedures for the stakeholders of the nautical service is conducted. In this regard, current challenges and limitations are identified. The results of the classification scheme show that tugboats in particular are examined in more detail. The objective is often to reduce costs and increase safety in the port area. For this purpose, mathematical approaches, or various types of algorithms are usually applied. Future research activities could be based on more detailed analyses of the approaches used in the identified papers.

Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.

Joint optimization of metro train timetable and stopping plan for the first service period: An integrated energy-efficient and time-saving model

This paper addresses the energy conservation challenge in metro systems during the first service period, characterized by large train headways and low passenger demands. A novel train timetabling method incorporating an express-local mode is proposed to maximize the overlap time between accelerating and braking trains, thereby facilitating the utilization of regenerative braking energy (RBE). Local and express trains depart alternately, with skip-stop strategies implemented for express trains. Considering the interests of operator and passengers for energy and travel time savings, a bi-objective model is proposed to jointly optimize train timetable and stopping plan. NSGA-II is applied to solve the model and obtain Pareto optimal solutions. Numerical experiments based on Ningbo Metro Line 1 data validate the effectiveness of proposed methods, showcasing significant enhancement in RBE utilization and notable reduction in passenger travel time. The proposed express-local mode establishes stable tracking relationships within train pairs, ensuring effective RBE utilization. Comparative analyses reveal that deadheading is more effective than flexible skip-stop for energy-efficient and time-saving metro operations.

Application of operating scenarios and analysis of unstable flow characteristics at various angles of inlet guide vane.

In this study, we analyzed the performance characteristics of an axial flow pump with different angles of internally installed inlet guide vanes (IGVs). We predicted the pump's performance based on changes in the IGV angle and analyzed the impact of these angle variations on pump operation in the low-flow region. Additionally, we used real operational data from two sewage treatment plants to propose efficient operational scenarios. For turbulence flow analysis, the Reynolds-averaged Navier-Stokes equations were discretized based on the finite volume method. The grid formation was evaluated using the grid convergence index to select the optimal grid. Then, the internal flow was analyzed in detail through transient-state analysis. Through fast Fourier transform analysis, we confirmed that adjusting the IGV angle during pump operation in the low-flow region in response to load changes results in more stable operation compared with the existing method (valve control). Overall, our findings verified that energy reduction and efficient operation can be achieved through IGV angle adjustment compared with valve control.

Green cell scheduling and performance management of manufacturing system based on heat loss optimization

Heat loss is an important loss in the manufacturing process, which has a significant impact on the overall system performance and resource utilization efficiency. Therefore, optimizing heat loss can not only improve manufacturing efficiency, but also promote the green transformation of enterprises. The purpose of this paper is to explore the green unit scheduling and performance management strategy of manufacturing system based on heat loss optimization, so as to improve the energy efficiency and overall performance of manufacturing system. A new scheduling model is proposed, which combines heat loss analysis with green management strategy. By using multi-objective optimization algorithm, the scheduling of manufacturing cells is optimized, focusing on the minimization of heat loss and the maximization of production efficiency. The thermal energy loss characteristics of each process and the operating state of the equipment are considered in the model, and the energy efficiency performance of different scheduling schemes is verified by simulation experiments. The experimental results show that the optimized scheduling scheme significantly reduces the thermal energy loss of the manufacturing system, significantly improves the overall production efficiency, and effectively improves the green performance indicators, including the reduction of energy consumption and waste emission. Through the scheduling and management strategy based on the optimization of heat loss, the green production goal of the manufacturing system is realized. The research of this paper provides a new idea and method for manufacturing enterprises to realize sustainable development while pursuing economic benefit.

Comparative analysis of hydrogel-based thermotropic glazing and fixed horizontal overhangs in building performance

Thermotropic (TT) glazing is able to achieve energy-saving capabilities similar to external shading devices by dynamically regulating solar heat gain transmitted through windows in response to ambient climatic conditions. However, the building performance of TT glazing compared to traditional shading devices remains underexplored. This study aims to bridge this knowledge gap by comparing the building performance of hydrogel-based TT glazing and fixed horizontal external shading devices in terms of building energy consumption, daylighting performance, and thermal comfort. Experimental tests were conducted over a continuous ten-day period, focusing on the indoor temperature and illumination distribution within experimental rooms equipped with either TT glazing or fixed horizontal external shading device. Building performance models were developed using EnergyPlus and validated with experimental data. Subsequently, a comparative analysis was then conducted to evaluate the impact of TT glazings and fixed horizontal external shading devices on building energy consumption, daylighting performance, and thermal comfort across five typical climate conditions in China. The findings indicated that: (1) In five climate zones, TT provided the greatest energy savings in West-facing orientations, followed by East-facing orientations. (2) TT can replace fixed horizontal external shading device with a certain length to achieve similar building energy efficiency and indoor thermal comfort performance. (3) In regard to daylighting performance, TT achieved the desired values of sUDI<100lx,50 % and sUDI300–3000lx,50 % across all four orientations in the five climates. (4) In terms of thermal comfort, TT improved thermal comfort by reducing the annual daytime average PPDoverheating across all orientations in the five climate zones.

Preparation of biomass fiber/polylactic acid foam using an oven-type free foaming method for energy savings and performance enhancement

Given the inherent challenges of low melt strength and slow crystallization rate associated with polylactic acid (PLA), this study introduces a sustainable and efficacious approach to enhance PLA crystallization utilizing biomass tomato peel pomace powder (TP) as a heterogeneous nucleating agent. ZnO-modified azodicarbonamide (ADC) was used as the chemical blowing agent. The blends were prepared with the aid of a torque rheometer, and TP/PLA composite foams were successfully prepared by the oven-type free foaming method. The empirical results demonstrate that the TP/PLA composite foam exhibits reduced processing requirements and superior comprehensive properties compared to pure PLA foam. Specifically, the composite containing 5 wt% TP achieved optimal foaming performance with 3 phr blowing agents and a foaming duration of 10 min. Furthermore, the annealing treatment markedly enhanced the compression modulus and strength of the foams. Under annealing conditions of 110 °C for 30 min, the compressive strength of the foam increased by 483.5 % relative to the untreated sample. The development of this composite foam signifies a novel direction for energy-efficient performance enhancement and holds significant potential for applications in building insulation and packaging industries.

Comparison and regional suitability evaluation of different backfill source heat pumps using TRNSYS

As global energy demand and carbon emissions continues to rise year by year, the use of renewable energy for heating becomes increasingly urgent. This study employs solar and geothermal energy to supply heat to mining facilities. Initially, three distinct heating systems for mining areas were modeled in TRNSYS: the Backfill Source Heat Pump (BSHP), the Solar Assisted Backfill Source Heat Pump (SABSHP), and the Solar Assisted Backfill Source Heat Pump with Seasonal Heat Storage (SABSHP-SHS). Following a decade of simulation, the optimal system was identified through a comparative analysis that encompassed backfill temperature equilibrium, the inlet and outlet water temperatures for buried pipes, Coefficient of Performance (COP), Partial Load Ratio (PLR), and economic factors. Over the 10-year simulation period, the COP and PLR of the SABSHP-SHS system outperformed both the BSHP and SABSHP systems, showing higher energy efficiency and better heat pump performance. To further assess the regional adaptability of the SABSHP-SHS system, simulations were conducted across four regions with diverse climatic profiles: Shijiazhuang, Yan’an, Xining, and Hegang. The analysis focused on heat collection, efficiency, backfill temperature variation, system energy consumption, and the energy efficiency ratio. The findings indicate that Hegang exhibits the highest solar heat collection efficiency at 89.9% during the non-heating season, while Xining achieves a significantly higher heat collection efficiency of 54.1% during the heating season. These results suggest that the SABSHP-SHS system is particularly effective in regions with pronounced seasonal variations, characterized by extended cold winters and brief, intense summers.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Connected Dominating Set Construction Using the Hunger Games Search Optimisation Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications.

Energy, economic and environmental analysis of a photovoltaic-thermal integrated dual-source heat pump system under a system coefficient of performance − based switching strategy

The photovoltaic-thermal integrated dual-source heat pump system can offer performance improvement by addressing the limitations of a single heat pump system, which not only improves energy efficiency and system performance but also reduces pollutant emissions. However, the operational performance of the system is significantly affected by different switching strategies between two heat sources, which has not been given adequate attention from researchers. Therefore, this study introduced a system coefficient of performance − based switching strategy, which can alternate between air-source heat pump and solar water heat pump modes to make full use of air and solar energy, to optimize the operation of the photovoltaic-thermal integrated dual-source heat pump system and compare with the conventional water temperature of the heat storage tank − based switching strategy. A TRNSYS model of the system was developed and validated, and the simulated results demonstrated the better performance of system coefficient of performance − based switching strategy in energy, economic and environmental perspectives. The average photoelectric and photothermal conversion efficiency under system coefficient of performance − based switching strategy were higher than those under water temperature of the heat storage tank − based switching strategy by 1.1 % and 4.0 %, respectively, with average modified coefficient of performance of 7.43 and 6.55. Furthermore, the system not only achieved savings of 5.63 % in annual electrical cost and 5.33 % in yearly operational cost but also reduced standard coal by 19.25 kg and total pollutant emissions by 242.55 kg per year under system coefficient of performance − based switching strategy, compared to water temperature of the heat storage tank − based switching strategy.