Energy Consumption Requirements Research Articles

All-Inorganic Lead-Free Cs₂AgBiBr₆/ZnO Artificial Retina Synapse Based on Photoelectric Synergistic Dual-Mechanism for Neuromorphic Computing.

Adaptive learning capability of optoelectronic synaptic hardware holds promising application prospects in next generation artificial intelligence, and the development of biometric retina perception is sternly hampered by three crucial issues, including well-balance between excitatory and inhibitory, non-volatile multi-state storage, and optimal energy consumption. In this work, a novel Cs2AgBiBr6/ZnO non-volatile optoelectronic synapse is proposed and successfully programmed with optical excitatory and electronic inhibitory in the light of dual-mechanism: Lead-free perovskite Cs2AgBiBr6 guarantees abundant photogenerated carrier concentration, and the process of carrier capture and release occurs in ZnO layer, which can collaboratively modulate various synaptic plasticity behaviors depending on distinct stimulus. Consequently, multi-bit storage is attained with the dual-mechanism non-volatile memory (DNVM) as a function of consecutive light spikes. The energy consumption of the DNVM is 1.85 nJ at a single light spike, and an ultra-low one of 13.8 fJ is triggered with a single electrical pulse, which approximatively meets the requirement of the biological synaptic event energy consumption. The performance of the DNVM is further evaluated with the Pavlov's classical conditioning experiment and visual hardware system, offering an exciting paradigm for implementing on-chip adaptive visual perception and neuromorphic computing.

Traffic and Scenario Adaptive OFDM-IM for Vehicular Networks: A Fuzzy Logic Based Optimization Approach

Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) holds significant importance in vehicle-to-everything (V2X) communications, with its main advantages being outstanding spectral efficiency and strong interference resistance. However, the existing OFDM-IM systems in vehicular networks overlook actual vehicular network channels and the impact of scatterers, thus failing to accurately reflect the system performance. Moreover, these systems focus solely on the bit error rate (BER) and ignore user requirements for low energy consumption and high spectral efficiency. To address these issues, we propose a user demand- and scenario-adaptive OFDM-IM method that optimizes the OFDM-IM index parameter by considering the spectral efficiency, BER, and energy consumption. Firstly, considering non-line-of-sight components and roadside reflectors, we establish a vehicle-to-vehicle (V2V) communication channel model for straight road scenarios. Then, we construct a transmission framework for vehicular network communication using OFDM-IM. Specifically, we develop an energy efficiency maximization formula, in which fuzzy logic is used to adjust the weights of the three performance indicators to meet various environmental and user requirements. In detail, we discuss the minimum signal-to-noise ratio (SNR) required for OFDM-IM to achieve a lower BER than traditional OFDM in various vehicular communication scenarios. Thus, we can make appropriate choices based on the robustness of the simulation results. The simulation results presented in this paper indicate our method’s effectiveness in enhancing the system’s reliability, efficiency, and flexibility.

A Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System

Background: Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing, edge computing is provided as a new paradigm to effectively reduce processing latency, energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems, an intelligent task migration assignment mechanism based on blockchain is proposed, which jointly considers the factors of resource allocation, resource control and credit degree. Methods: In this paper, an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay, energy consumption, communication, and credit degree. Here, the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes, and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge. Results: Compared to the existing two benchmark mechanisms, the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost. Conclusion: To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.

Design and Modeling of a High-Peak-Power Distributed Electric Propulsion System for a Super-STOL UAV

Electric short takeoff and landing (eSTOL) aircraft utilize the slipstream generated by distributed propellers to significantly increase the effective lift coefficient and reduce the takeoff and landing distances. By utilizing the blown lift, eSTOL UAVs can achieve similar takeoff and landing site requirements as electric vertical takeoff and landing (eVTOL) UAVs, while having lower takeoff and landing energy consumption and thrust requirements. This research proposes a high-peak-power distributed electric propulsion (DEP) system model and overload design method for eSTOL UAVs to further improve the power and thrust of the propulsion system. The model considers motor temperature factors with the throttle input, which is solved through three-round iterative calculations. The experimental and simulation results indicate that the maximum error of the high-peak-power propulsion unit model without considering temperature is 19.52%, and the maximum error when considering temperature is 1.2%. The propulsion unit ground test indicates that the main factors affecting peak power are the duration of peak power and the temperature limit of the motor. Finally, the effectiveness of the propulsion system model is verified through ground tests and UAV flight tests.

Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework.

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. Meanwhile, a Long Short-Term Memory (LSTM) model in the cloud analyzes time-series data for predictive failure analysis, enhancing maintenance scheduling and operational efficiency. The framework's dynamic workload management algorithm optimizes task distribution between edge and cloud resources, balancing latency, bandwidth usage, and energy consumption. Experimental results show that the hybrid approach achieves a 35% reduction in latency, a 28% decrease in energy consumption, and a 60% reduction in bandwidth usage compared to cloud-only solutions. This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments.

Environmental and economic assessment of urban wastewater reclamation from ultrafiltration membrane-based tertiary treatment: Effect of seasonal dynamic

This study aimed to assess the environmental and economic performance of an ultrafiltration (UF) tertiary treatment of effluent from an urban wastewater treatment facility. Data from a UF demonstration plant composed of commercially available equipment, including industrial hollow-fiber membranes was used to project a full-scale facility. The results from the demonstration plant recommended different ranges of transmembrane fluxes and sparging air demands under summer and winter conditions to prevent excessive fouling. The energy balance of the full-scale facility would be 0.308 ± 0.112 kWh·m−3 in summer and 0.140 ± 0.040 kWh·m−3 in winter, with blowers' being the main consumers (86–93 %). CAPEX accounted for €0.030 ± 0.002·m−3 in summer and €0.027 ± 0.002·m−3 in winter and membrane acquisition represented 66–69 % of the investment cost. Energy expenditure was the major OPEX cost (66–79 %), with a total operating cost of €0.077 ± 0.023·m−3 and €0.042 ± 0.008·m−3 in summer and winter, respectively. The final average value obtained for the TAC was €0.107 m−3 in summer and €0.068 m−3 in winter. The environmental assessment confirmed optimizing energy consumption and membrane requirements as the main factors influencing environmental sustainability. Specifically, summer and winter emissions of 0.079–0.175 and 0.043–0.079 kgCO2eq·m−3 (Global warming potential); 8.1 · 10−4–1.7 · 10−3 and 4.8 · 10−3–8.1 · 10−3 m3·m−3 (water consumption); 0.019–0.041 and 0.010–0.019 kg oileq·m−3 (fossil fuel scarcity); and 1.4 · 10−4–2.9 · 10−4 and 7.7 · 10−4–1.4 · 10−4 kg Cueq·m−3 (mineral resource scarcity) were calculated, respectively. The obtained permeate quality complied with the most stringent Spanish and EU regulations.

Optimization of thermal performance in hybrid nanofluids for parabolic trough solar collectors using Adams–Bashforth–Moulton method

Solar energy is an environment-friendly renewable energy source that fulfills the energy consumption requirements of the world economy. Concentrated solar power is the most advanced technology that efficiently absorbs concentrated solar energy. This article investigates the solar energy storage and entropy generation in concentrated parabolic trough solar collectors for hybrid nanofluid flow due to a spinning tube inserted at the receiver's center. The effect of copper nanoparticles and multi-walled carbon nanotube mixture is studied using water-based fluid. Also, the influence of an external magnetic field is investigated with thermophoresis and Brownian motion of nanoparticles. The governing equations for the nanofluid flow are derived using the conservation principles of mass, momentum, energy, and concentration with boundary layer assumptions and no-slip boundary conditions. The governing equations are numerically solved using Adam Bashforth and Adam Moulten's fourth-order predictor-corrector numerical scheme along with the shooting approach. The numerical outcomes for the fluid velocity, temperature, Nusselt number, and entropy formation against various influential physical parameters have been discussed using graphs. It is observed that the augmenting Eckert number, thermophoretic diffusion parameter, and Brownian motion parameter escalates the thermal profiles. Also, an escalation of the radiation parameter and Lewis number enhances the Nusselt number. Thermal energy storage in solar collectors utilizing different terminologies is crucial for improving the efficiency and performance of solar thermal collectors.

Stable Millivolt Range Resistive Switching in Percolating Molybdenum Nanoparticle Networks.

To overcome the limitations of the conventional Von Neumann architecture, inspiration from the mammalian brain has led to the development of nanoscale neuromorphic networks. In the present research, molybdenum nanoparticles (NPs), which were produced by means of gas phase condensation based on magnetron sputtering, are shown to be the constituents of electrically percolating networks that exhibit stable, complex, neuron-like spiking behavior at low potentials in the millivolt range, satisfying well the requirement of low energy consumption. Characterization of the NPs using both scanning electron microscopy and scanning transmission electron microscopy revealed not only pristine shape, size, and density control of Mo NPs but also a preliminary proof of the working mechanism behind the spiking behavior due to filament formations. Furthermore, electrostatics COMSOL Multiphysics simulations of the morphology of the NPs provided evidence that the stable switching is due to the as-deposited stellate Mo NPs creating high electric field strengths, while keeping them separated, not seen before in other percolating networks based on spherical NPs. Hence, our results show the working mechanism behind switching in percolating Mo NP networks and show that they are very promising for realistic neuromorphic systems.

Sequential extraction of hawthorn pectin: An attempt to reveal their original mode of being in plants and functional properties

Hawthorn is rich in pectin, which is much higher than most cultivated fruits, but conventional extraction methods do not meet the requirements of low energy consumption and green production. Pectin in hawthorn is divided into soluble and insoluble parts, and with the ripening of hawthorn, the original pectin is converted into soluble pectin and pectic acid under the action of enzymes. Therefore, based on the characteristics of hawthorn pectin, this study sequentially extracted hawthorn pectin using water-soluble pectin (WSP) and hot acid-soluble pectin (HAP) method, verifying the feasibility of extracting hawthorn pectin with pure water at room temperature, and systematically analyzing and comparing the physicochemical properties and functional characteristics of the two methods. The combination of texture analysis and gel rheology revealed that WSP formed a more uniform and dense network structure during the gelation process. Additionally, microscopic observations and emulsification index results indicated that the emulsion prepared with WSP (WSE) had a smaller particle size and better stability. This indicates that hawthorn pectin is suitable for extraction with pure water at room temperature, which can maintain its good physical properties while reducing energy consumption, providing a new approach for the large-scale extraction of pectin in the food industry.

Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy

Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across the lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we find the emergence of the cortical hierarchy revealed by the highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increases in a biphasic pattern featured by initial rapid and subsequently slower rate, and break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices are associated with enhanced language and motor skills, and frontoparietal association cortices with cognitive skills. The study discovers emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.

An overview of the constructions of conveyors for moving bulk materials, comparison and study of their parameters

The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand. For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure. The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption. When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.

A low-power consumption security data fusion model for Industrial Internet of Things

Industrial Internet of Things (IIOT) is an important part of manufacturing industry in the future, and the front-end sensor data acquisition network is the core component of it. Due to the limited energy of IIOT, reducing transmission interaction, energy consumption, and computational complexity in the process of data transmission is the key and difficult point in designing model of IIOT. In this paper, a lightweight green security data fusion model for IIOT is proposed, called LCSDF model, which aims to achieve data security and integrity verifiability by using less energy consumption. The simulation results show that the proposed model can ensure data security and verifiability, and meet the energy consumption requirements of the system. The performance of the algorithm is also satisfactory, reflecting the lightweight, green, and security of the model.

Research on the Impact of Digital Transformation on Corporate ESG - A Case Study of Jingdong Logistics

The purpose of this research is to analyze the impact that digital transformation has on the environmental, social, and governance (ESG) elements of businesses by using Jingdong Logistics as a case study. Jingdong Logistics' environmental, social, and governance (ESG) indicators have been impacted both directly and indirectly by the digital transformation that the company has undergone, as indicated by the study of data changes and measures taken both before and after the transition. This digital transition has led to huge reductions in greenhouse gas emissions, which is good news for the environment; but, it also creates challenges in terms of energy consumption requirements. Although there has been an increase in the number of training hours and a drop in the number of workplace injuries, there has been an increase in the number of full-time employees, while there has been an increase in employee turnover. Even though there was an increase in the number of vendors, anti-corruption incidents decreased. Regarding the governance side of things, improvements were made to data security and transparency. According to the findings of the inquiry, digital transformation had a positive impact on the environmental, social, and governance (ESG) of Jingdong Logistics; nevertheless, it also brought up new challenges that call for continued attention and growth.

DNN Adaptive Partitioning Strategy for Heterogeneous Online Inspection Systems of Substations

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved.

Techno-Economic Optimisation of Green and Clean Hydrogen Production

AbstractEnergy is typically generated from fossil fuels, leading to significant greenhouse gas (GHG) emissions. Therefore, cleaner energy needs to be used to reduce GHG emissions in the energy sector. Hydrogen (H2) is identified as a potential resource suitable for replacing fossil fuels as H2 burns with oxygen to produce water (H2O) and generates no emissions as a result of this. However, H2 is normally produced through steam reforming of natural gas, which is a fossil fuel. Clean H2 can be produced if its derived from renewable pathways, such as solar powered water electrolysis, gasification of biomass, etc. However, determining a feasible renewable pathway is challenging. In addition, storage of H2 is another challenge as the energy density of H2 is considerably low. To increase the energy density, H2 must stored at high pressure and low temperature. This causes high storing costs for H2 before being transported to the end-users and high energy consumption requirements. H2 production from renewable sources is also lower in efficiency when compared with conventional production technology. Thus, it is critical to develop a systematic optimisation tool to analyse and optimise the production of clean H2 to overcome the abovementioned challenges. This work presents an optimisation model to optimise the production of clean H2 based on total annualised cost, yield, efficiency, storage and energy consumption of each technology. To illustate the proposed model, a case study with several scenarios, such as an economically feasible and clean H2 process and optimal H2 production and storage technologies in terms of energy consumption, is solved.

Optimizing Air Contactor Design for Efficient Carbon Capture in Direct Air Capture Systems: A Multi-Disciplinary Approach

As carbon dioxide (CO2) emissions continue to rise, direct air capture (DAC) has emerged as an important technology for removing CO2 from ambient air to combat climate change. The air contactor design is a critical factor impacting DAC system performance and costs. This research describes a strategy for improving the design of a single air contactor unit to increase CO2 capture flux while minimizing costs using optimization techniques grounded in experimental data .The research focuses on a 100 m2 slab air contactor modeled utilizing cooling tower and scrubber principles. The system includes the chemical, mechanical, and structural engineering disciplines. The mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area are four major design characteristics that were maximized.The model integrated requirements for low air velocity, pressure drop, and energy consumption, as well as adequate gas-liquid interaction via structured packing selection and depth.The design space was explored using a multi-objective optimization strategy based on gradient-based and heuristic methods. Initial sampling via a design of experiments methodology demonstrated response surface trends in the objective function across the design space. The response surfaces mapped the optimization objective as a function of the design variables. Using gradient information, sequential quadratic programming efficiently located an optimal design vector that met all constraints on the design variables and feasibility requirements. The constraints included bounds on mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area. The optimized air contactor design achieved a CO2 capture flux of 10,440 kg/m2-yr at a cost of $ 0.068 per kg CO2 captured. The developed optimization framework maximizes contactor performance while decreasing operational costs. A multi-objective genetic algorithm approach was used to identify the trade-offs between the two competing objectives of maximized CO2 capture and minimized cost by expanding the formulation. The Pareto-optimal front of the optimization results offered numerous optimized solutions with specific DAC system setups. This methodology, as well as the optimum configurations identified, serve as the foundation for constructing larger-scale DAC systems, in future. Ongoing research aims to improve solution approaches by including advanced optimization software packages such as DAKOTA, which offers global optimization algorithms, as well as to boost model accuracy via integrating additional choice variables. Future optimization and modeling studies will include contactor interactions and the modeling of a full DAC facility to boost confidence that a globally optimal design has been established.Overall, this study illustrated an efficient optimization strategy for air contactor design and contributes to the improvement of sustainable carbon capture technology, which is crucial to controlling rising global CO2 levels. Contactor configuration optimization enables superior DAC system designs at lower costs, hence promoting the urgent deployment of negative emissions solutions.

Investigation of Dry-Made NMC 811 Electrodes with Varying Binder and the Conductive Agent Contents

Lithium-ion battery electrode materials consist of active materials (AM), conductive agents, and binders. Although conductive agents and polymeric binders occupy a small portion of the electrode, they play an indispensable role. Conductive agents are needed to improve the electronic conductivity of the electrode, while binders ensure mechanical integrity by providing particle/particle cohesion and electrode-film/substrate adhesion. Optimizing the interactions of active materials, conductive agents, and binders is crucial for achieving the desired electrode performance and durability. Although multiple studies have explored the impact of electrode composition for slurry-based electrodes, it is still unclear how composition affects the electrodes made by dry processes. In this study, we performed a detailed investigation of the effects of the different electrode compositions by changing the ratio of polyvinylidene difluoride (PVDF) binder and carbon black (CB) for dry-made LiNi0.8Mn0.1Co0.1O2 (NMC 811) electrodes. The electrostatic spray deposition method was used as a dry electrode fabrication process due to its many advantages, such as reduced energy consumption and inexpensive processing requirements. We investigated electrodes of four different compositions, 96:3:1, 96:2:2, 90:7.5:2.5, and 90:5:5 (AM:PVDF: CB), corresponding to PVDF/CB mass ratios of 1:1 and 3:1.We found that electrodes with a high PVDF/CB ratio of 3:1 have larger impedance than electrodes with a low PVDF/CB ratio of 1:1 due to the insulating aggregates of PVDF alone, causing deteriorated electrochemical performance. At the PVDF/CB ratio of 1:1, an even distribution of PVDF/CB on the AM particle surfaces and in the pores between the AM particles was observed. For both PVDF/CB ratios of 1:1 and 3:1, increasing the total amount of PVDF and CB (e.g., decreasing the AM amount) in the composite electrode enhanced both the C-rate and cycling performance. However, electrodes with the PVDF/CB ratio of 1:1 at high AM content (96%) still showed a comparable C-rate performance to that with lower AM content (90%). Thus, dry-made electrodes with high AM content can achieve a good high C-rate performance, especially for high-energy-density applications.This study provides a better understanding of the effects of CB/PVDF, which is key to finding an optimal structure and composition of the dry-made electrodes.

Spatiotemporal cerebral blood flow dynamics underlies emergence of the limbic-sensorimotor-association cortical gradient in human infancy.

Infant cerebral blood flow (CBF) delivers nutrients and oxygen to fulfill brain energy consumption requirements for the fastest period of postnatal brain development across the lifespan. However, organizing principle of whole-brain CBF dynamics during infancy remains obscure. Leveraging a unique cohort of 100+ infants with high-resolution arterial spin labeled MRI, we found the emergence of the cortical hierarchy revealed by the highest-resolution infant CBF maps available to date. Infant CBF across cortical regions increased in a biphasic pattern with initial rapid and sequentially slower rate, with break-point ages increasing along the limbic-sensorimotor-association cortical gradient. Increases in CBF in sensorimotor cortices were associated with enhanced language and motor skills, and frontoparietal association cortices for cognitive skills. The study discovered emergence of the hierarchical limbic-sensorimotor-association cortical gradient in infancy, and offers standardized reference of infant brain CBF and insight into the physiological basis of cortical specialization and real-world infant developmental functioning.

Pressure-Induced Enhancement in Chemical Looping Reforming of CH4: A Thermodynamic Analysis with Fe-Based Oxygen Carriers.

Chemical looping reforming of methane (CLRM) with Fe-based oxygen carriers is widely acknowledged as an environmentally friendly and cost-effective approach for syngas production, however, sintering-caused deactivate of oxygen carriers at elevated temperatures of above 900 °C is a longstanding issue restricting the development of CLRM. Here, in order to reduce the reaction temperature without compromising the chemical-looping CH4 conversion efficiency, we proposed a novel operation scheme of CLRM by manipulating the reaction pressure to shift the equilibrium of CH4 partial oxidation towards the forward direction based on the Le Chatelier's principle. The results from thermodynamic simulations showed that, at a fixed reaction temperature, the reduction in pressure led to the increase in CH4 conversion, H2 and CO selectivity, as well as carbon deposition rate of all investigated oxygen carriers. The pressure-negative CLRM with Fe3O4, Fe2O3 and MgFe2O4 could reduce the reaction temperature to below 700 °C on the premise of a satisfactory CLRM performance. In a comprehensive consideration of the CLRM performance, energy consumption, and CH4 requirement, NiFe2O4 was the Fe-based OCs best available for pressure-negative CLRM, especially for an excellent syngas yield of 23.08 mmol/gOC. This study offered a new strategy to address sintering-caused deactivation of materials in chemical looping from the reaction thermodynamics point of view.

Path optimization of underwater vehicles in multi-obstacle environment based on energy constraint strategy

PurposeTo solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy.Design/methodology/approachFirstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment.FindingsThe simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles.Originality/valueThe method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.