Energy Efficiency Of System Research Articles

A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration

With the development of Industry 4.0, discrete manufacturing systems are accelerating their transformation toward flexibility and intelligence to meet the market demand for various products and small-batch production. The flexible flow shop (FFS) paradigm enhances production flexibility, but existing studies often address FFS scheduling and automated guided vehicle (AGV) path planning separately, resulting in resource competition conflicts, such as equipment idle time and AGV congestion, which prolong the manufacturing cycle time and reduce system energy efficiency. To solve this problem, this study proposes an integrated production–transportation scheduling framework (FFSP-AGV). By using the adjacent sequence modeling idea, a mixed-integer linear programming (MILP) model is established, which takes into account the constraints of the production process and AGV transportation task conflicts with the aim of minimizing the makespan and improving overall operational efficiency. Systematic evaluations are carried out on multiple test instances of different scales using the CPLEX solver. The results show that, for small-scale instances (job count ≤10), the MILP model can generate optimal scheduling solutions within a practical computation time (several minutes). Moreover, it is found that there is a significant marginal diminishing effect between AGV quantity and makespan reduction. Once the number of AGVs exceeds 60% of the parallel equipment capacity, their incremental contribution to cycle time reduction becomes much smaller. However, the computational complexity of the model increases exponentially with the number of jobs, making it slightly impractical for large-scale problems (job count > 20). This research highlights the importance of integrated production–transportation scheduling for reducing manufacturing cycle time and reveals a threshold effect in AGV resource allocation, providing a theoretical basis for collaborative optimization in smart factories.

Adaptive digital twin integration with multilevel inverter control for energy efficient smart rehabilitation systems

This research proposes an innovative framework integrating adaptive Digital Twin (DT) models with Multi-Level Inverter (MLI) control to improve energy efficiency in advanced rehabilitation systems. By utilizing real-time monitoring and adaptive adjustment of power parameters through DT technology, the method achieves precise and dynamic control of devices such as prosthetics and exoskeletons. The incorporation of MLI ensures smooth and efficient power delivery, reducing harmonic distortion and enhancing overall energy utilization. Key outcomes include a 14.05% increase in energy efficiency, an 8.12% decrease in power ripple, and a 24.03% improvement in system response accuracy, enabling real-time optimization tailored to patient-specific rehabilitation needs. Furthermore, the proposed approach lowers operational costs by 7.01% through optimized energy usage and extended system lifespan. These results highlight the potential of this innovative method to advance rehabilitation systems through the integration of adaptive control and real-time digital modeling.

FPGA implementation of double-head SalsaNext: a CNN-based model for LiDAR point cloud segmentation

This study details the adaptation and deployment of a customized SalsaNext model for semantic segmentation of LiDAR point clouds on edge devices, benchmarked using the SemanticKITTI and Waymo Open datasets. We introduce an innovative multi-dataset training framework designed specifically for range image-based segmentation models. Central to this approach is our double-head SalsaNext model, which features two output heads to facilitate simultaneous training and inference on the Waymo and SemanticKITTI datasets. Following training, the model is streamlined by removing the head dedicated to Waymo, resulting in a compact, single-headed version optimized for SemanticKITTI. This simplified model is then quantized to employ fixed-point arithmetic, significantly enhancing computational efficiency and enabling real-time operation on the Xilinx Kria KV260 board. The quantization process markedly reduces resource consumption while preserving competitive accuracy. Our deployment on this low-power, FPGA-based platform underscores the potential of energy-efficient systems for advanced 3D semantic segmentation, with promising applications in autonomous systems and robotics. Experimental results validate the effectiveness of our training schema and the success of the optimized implementation of the double-head model on resource-constrained hardware.

Energy-Efficient HVAC Systems: Enhancing Sustainability in Low-Income Housing

In the recent trend of developing sustainable societies aspiring to net-zero initiatives, the use of energyefficient HVAC systems has become increasingly popular, specifically in low-income housing schemes. Conventional designs (HVAC) are subjected to high levels of energy consumption exacerbating increased cost of utility and severe environmental damages. This paper aimed to explore innovative and energy-efficient HVAC designs that can significantly contribute to developing sustainable societies with a net-zero future while fostering the environment of social equity. By conducting a comprehensive review of the contextual literature and related studies, the research highlights the significance of energy-efficient HVAC systems in developing sustainable societies and the key challenges preventing the smooth adaptation of modern HVAC systems.

Intermittent swimmers optimize energy expenditure with flick-to-flick motor control

Abstract Intermittent swimming behaviour is commonly observed in larval zebrafish, often attributed to energy-saving mechanisms. In this study, we utilize a hybrid approach combining deep reinforcement learning and the immersed boundary–lattice Boltzmann method to train a larval zebrafish-like swimmer to reach a target with minimized energy expenditure. We find that when the tail-beat period is fixed, continuous swimming emerges as the optimal strategy. However, when the tail-beat period is allowed to vary, intermittent swimming proves superior in energy performance, achieved through reductions in tail-beat amplitude and frequency. Our detailed analysis reveals that intermittent swimmers employ rapid backward tail flicks to attain high speeds, coupled with slower forward tail flicks and coasting phases to conserve energy. Furthermore, we derive scaling laws governing the swimming performance of trained fish. These results offer valuable insights into the intermittent swimming patterns of fish, with implications for understanding bio-inspired locomotion and informing the design of energy-efficient aquatic systems.

Logistics and environmental aspects of the formation of mining and transport systems of dump formation in deep pits using container technology

Improving the energy efficiency and environmental friendliness of waste disposal processes during open-pit mining of mineral deposits involves substantiating and developing a new mining and transport technology. Literary analysis combined with computer modeling based on the finite element method has made it possible to automate the work in designing container technology equipment at the design preparation stages. A new mining and transport system for waste disposal in deep quarries has been developed, which involves the use of container technology for moving rock mass in quarries, allowing for increased energy efficiency of transport systems and minimizing pollution of the quarry atmosphere. The proposed logistic scheme for transporting rock mass to dumps using container technology ensures the creation of a new energy-saving mining and transport system using quarry hoisting machines, which, in comparison with similar traditional systems, allows for a reduction in costs for lifting rock mass, a reduction in atmospheric gas pollution, and an improvement in mining modes. Container delivery of rock mass as a bulk and difficult-to-excavate material will allow for its single excavation and lifting from the quarry to the dump using quarry hoisting machines with a minimum tare coefficient.

Nanofluid magnetoconvection and entropy generation: a computational study for water treatment and resource management

This research exploration emerged from the critical need to revolutionize heat transfer techniques, particularly in pivotal domains like nuclear technologies, electronics and energy-efficient systems. The motivation for this study endeavour stemmed from the complex interrelation among nanofluids, magnetic fields and their potential for enhancing heat exchange. A pragmatic numerical approach is utilized to examine the Cu–H2O nanofluid flow situation within an enclosure featuring cooled vertical walls and a heat-generating source, while ensuring insulation for the remaining edges. The evaluation analyses the contribution of entropy, including total, viscous and thermal entropies, establishing a connection to real-world heat transfer challenges. The Galerkin finite element algorithm is utilized to solve the partial differential system of the modelled problem. The phenomena of entropy generation, fluid flow and heat transfer are studied under the influence of parameters such as the Hartmann number, Rayleigh number, magnetic field inclination angle and nanoparticle volume fraction. The study reveals that irreversibility increases with the magnetic field inclination angle, while entropy generation decreases with an increase in the Hartmann number. The primary innovation of this study is uncovering new dimensions with widespread practical implications by deciphering the complex dynamics of nanofluid convection with entropy generation and inclined magnetic influence. This research holds significant potential for advancing heat transfer applications in water treatment and resource management, aligning with the journal’s focus on sustainable and innovative water solutions.

Artificial Synaptic Properties in Oxygen-Based Electrochemical Random-Access Memory with CeO2 Nanoparticle Assembly as Gate Insulator for Neuromorphic Computing.

Beyond the von Neumann architecture, neuromorphic computing attracts considerable attention as an energy-efficient computing system for data-centric applications. Among various synapse device candidates, a memtransistor with a three-terminal structure has been considered to be a promising one for artificial synapse with controllable weight update characteristics and strong immunity to disturbance due to decoupled write and read electrode. In this study, oxygen ion exchange-based electrochemical random-access memory consisting of the ZnO channel and CeO2 nanoparticle (NP) assembly as a gate insulator, also as an ion exchange layer, is proposed and investigated as an artificial synapse device for neuromorphic computing. The memtransistor shows a tunable and reversible conductance change via oxygen ion exchange between ZnO and CeO2 NPs upon gate voltage application. The use of CeO2 enables efficient oxygen ion exchange with the ZnO channel due to its inherent property of easily absorbing and releasing oxygen ions by altering the valence state of the Ce cation. Additionally, the porous structure of the CeO2 NP assembly supports the oxygen reservoir function while retaining its insulating properties as a gate insulator, ensuring reliable device operation. Also, its porous nature enhancing oxygen ion exchange permits high-speed operation within tens of microsecond range. Based on the facilitated oxygen ion exchange, a highly linear and symmetric conductance modulation is achieved with good endurance over 104 pulses and excellent nonvolatile retention. Furthermore, the memtransistor mimics representative functions of the biological synapse such as paired-pulse facilitation, short-term (STP) and long-term plasticity (LTP), and the transition from STP to LTP as repeating learning cycles.

A Flexible and Stretchable Triboelectric Nanogenerator with Agarose/P(HEA-co-AA)-Al/NaCl Electrodes for Bio-Mechanical Energy Harvesting and Fall Detection.

Recent advancements in wearable electronics for fall detection have shown significant potential, yet challenges remain in developing reliable, energy-efficient systems capable of continuous monitoring in real-world conditions. In this work, a double-network (DN) conductive hydrogel, comprising NaCl-coordinated agarose and poly (2-hydroxyethyl acrylate-co-acrylic acid) (Agarose/P(HEA-co-AA)-Al/NaCl) (APA-hydrogel), was synthesized. The agarose forms a stable first network, while the second network is established by P(HEA-co-AA) through aluminum ion coordination. Immersion in NaCl solution leads to the formation of hydrated sodium ions ([Na(H2O)n]+), which are anchored within the hydrogel matrix via hydrogen bonding and metal coordination. The resulting APA-hydrogel was applied as a triboelectric nanogenerator (APA-TENG), demonstrating excellent performance with an open-circuit voltage of 900 V, a short-circuit current of 73.42 μA, and a peak power output of 3.52 mW at a 3 MΩ load. APA-TENG shows strong potential for energy harvesting and powering low-power devices, as well as real-time sensing for motion and fall detection, making it highly suitable for wearable and assistive technologies powered by human activity.

A Review of Design Considerations for a Voltage-Controlled Oscillator (VCO) in Automotive Radar Applications

In advancing automotive radar systems, Voltage-Controlled Oscillators (VCOs) design is critical due to their role in generating radar frequencies within specific bandwidths. The increasing demand for precise and reliable radar performance in autonomous driving has led to a deeper examination of VCO design parameters. This paper addresses the challenges faced in designing VCOs explicitly for automotive radar applications, focusing on issues such as phase noise, tuning range, and energy efficiency. The principal problem is ensuring that the VCO can operate effectively within the prevalent automotive radar frequency bands, particularly the K-band (around 24 GHz) and the 77 GHz spectrum while minimizing phase noise, which is detrimental to radar performance. This paper analyzes and highlights the essential design considerations contributing to developing efficient and reliable VCOs for automotive radar systems. A comprehensive review methodology examines existing literature and current VCO designs in automotive radar applications. The review identifies the impact of phase noise on radar efficiency, emphasizing the need for a VCO with low phase noise to enhance range and sensitivity. It also explores the flexibility required in the VCO's tuning range, which is crucial for adapting to various radar functionalities. Furthermore, the paper underscores the importance of energy efficiency in vehicular radar systems. It discusses how power-conservative VCO designs can extend battery life and reduce energy consumption, critical factors in developing sustainable automotive technologies. The findings from this review provide valuable insights into the complexities of VCO design for automotive radar, offering guidance for future innovations in autonomous driving and vehicle safety systems.

Numerical analysis of various nanofluids in circular and square plate solar collector with spiral and U-bend collector pipe designs

Nanofluids are being used to improve solar collector heat transfer due to rising demand for efficient solar energy systems. Six nanofluids at 2%, 3%, and 4% volumetric concentrations are tested in square and circular flat plate solar collectors with planar spiral and U-bend pipes. ANSYS Fluent 2022 R1 was used for a full Finite Volume Method (FVM) simulation utilizing the SIMPLE k-ϵ turbulence model, with a constant heat flow of 700 W/m² and variable flow velocities of 0.5, 1, 2, and 4 m. A 2% concentration of aluminium oxide (Al2O3) nanofluid has the maximum heat transfer coefficient, Nusselt number, and thermal performance regardless of collector design. Silver (Ag) nanofluid has exceptional pressure drop at 3% and 4% concentrations. U-bend collectors have a thermal performance ratio of 1.23, compared to spiral collectors' 1.09, showing improved thermal performance with nanofluid concentration. Silicon dioxide (SiO2) and diamond nanofluids perform poorly in all conditions, suggesting they are unsuitable for solar thermal systems. Despite their limitations, Al2O3 and Ag nanofluids can improve solar collector efficiency, according to this study. The understanding can inform nanofluid solar thermal system development and applications.

Field-Induced Phase Transitions in Cuprate Superconductors for Cryogenic in-Memory Computing.

Energy-efficient cryogenic memory systems play a critical role in a wide spectrum of applications focused on ultra-energy-efficient information and communication technologies, such as quantum computing or superconducting electronics. Neuromorphic systems, known for their superior energy efficiency, have emerged as a promising approach for in-memory computing. Specifically, strongly correlated oxides that exhibit Mott metal-insulator transitions through field-induced oxygen movement are of great interest for analog memory and neuromorphic computing. Yet, optimizing their performance at low temperatures may prove challenging due to their reliance on ionic motion. In this study, superconducting structures composed of strongly correlated YBa2Cu3O7 - x (YBCO) combined with ferromagnetic (LSMO) are investigated to obtain non-volatile multilevel memristive switching effects with high performance at cryogenic temperatures. This research reveals the presence of two competing switching mechanisms, which are attributed to the movement of oxygen vacancies and electric carriers within these structures. It is determined that a phase transition induced by the movement of holes is the primary factor influencing the switching dynamics at low temperatures. Additionally, a physics-based compact model is proposed that accurately replicates the experimental findings and provides a tool for circuit-leveldesign.

Designing energy-efficient industrial processes: A multi-criteria decision analysis for sustainable manufacturing solutions

This research examines energy-efficient solutions in industrial manufacturing through a Multi-Criteria Decision Analysis (MCDA) approach to discover optimal methods for improving energy consumption alongside cost-efficiency and environmental sustainability. The production activities within the industrial sector including chemical processing, metalworking and food manufacturing make up substantial portions of world energy usage. This study examines the role of advanced technologies including heat recovery systems and high-efficiency furnaces together with energy-efficient refrigeration systems in reducing energy consumption within industrial sectors. This research employs the MCDA framework which combines the Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW) methods. The research examines how energy-efficient solutions perform during a six-month period by measuring energy savings alongside cost reductions and environmental benefits. The study results reveal significant energy reductions alongside cost savings between 10% to 30% while chemical production achieved a 25% decrease in energy consumption. The observed environmental improvements that include a 30% decrease in carbon emissions from chemical production demonstrate how these technologies can advance sustainable industrial practices.

Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks

<span>For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node localization (NL) in WSNs is complicated for recent researchers. WSN localization focuses on finding sensor nodes (SNs) in two dimensions. WSN NL provides decision-making information in packets sent to base stations. This article describes modeling of chimp optimization algorithm node localization system in wireless sensor networks (MCOANL-WSN). The MCOANL-WSN approach uses metaheuristic optimization to locate unknown network nodes. To simulate chimpanzees' cooperative hunting behavior, the MCOANL-WSN approach includes chimp optimization algorithm (COA) into the NL process. The system uses mathematical modeling to represent node collaboration to improve placements. COA-based localization is being proposed for dynamically responding to resource-constrained and dynamic WSNs. Wide-ranging simulations may assess the MCOANL-WSN system's scalability, energy efficiency, and localization accuracy. The findings demonstrate the superiority of the new modeling method over current NL schemes in improving WSN reliability and efficiency in various applications.</span>

Temporal stability of public acceptability of novel and established energy technologies

This study examines how stable public acceptability judgements towards novel and established energy technologies are over time, which is important to consider in decision-making about the transition to low-carbon and energy-efficient systems. We conducted two longitudinal survey experiments, one with a convenience sample of students and another with a representative sample of Dutch adults, to explore the extent to which acceptability judgements towards energy technologies are stable over time and to examine potential factors influencing stability of acceptability judgements, including technology novelty, people’s knowledge about a technology, ambivalence towards a technology, perceived importance of the technology, and personal values. We also tested if stability affects citizenship behaviors (e.g., signing petitions, supporting political candidates) towards energy technologies. As expected, acceptability judgements are less stable for novel (i.e., geothermal energy and CCS) than for established technologies (i.e., wind and nuclear energy). Moreover, the more ambivalent people felt towards a technology and the less an energy technology was personally important to them, the less stable their acceptability judgements. Yet, neither knowledge nor personal values were significantly related to stability of acceptability judgements. Interestingly, acceptability judgements were associated with citizenship behavior regardless of how stable acceptability judgements were. We discuss the theoretical and practical implications of our findings.

Effective Energy Efficiency of Cell-Free mMIMO Systems for URLLC With Probabilistic Delay Bounds and Finite Blocklength Communications

Effective Energy Efficiency of Cell-Free mMIMO Systems for URLLC With Probabilistic Delay Bounds and Finite Blocklength Communications

Investigating the impact of operating parameters on the energy efficiency of evaporative precooling systems in data centers in hot and humid climates

Investigating the impact of operating parameters on the energy efficiency of evaporative precooling systems in data centers in hot and humid climates

Present and future challenges for hydraulic reliability and energy efficiency in collective irrigation systems: A participatory modelling approach

Present and future challenges for hydraulic reliability and energy efficiency in collective irrigation systems: A participatory modelling approach

36th International Conference on efficiency, cost, optimization, simulation and environmental impact of energy systems (ECOS 2023) – Advancing sustainable energy systems

36th International Conference on efficiency, cost, optimization, simulation and environmental impact of energy systems (ECOS 2023) – Advancing sustainable energy systems

A blockchain-based LLM-driven energy-efficient scheduling system towards distributed multi-agent manufacturing scenario of new energy vehicles within the circular economy

A blockchain-based LLM-driven energy-efficient scheduling system towards distributed multi-agent manufacturing scenario of new energy vehicles within the circular economy