Energy Depletion Problem Research Articles

LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines.

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (LSTM) autoencoder to perform outlier detection. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.

Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future.

Heterogeneous catalysts, reaction kinetics, and reactor designs for methanol production from carbon dioxide: A critical review

AbstractThe process of preparing methanol from carbon dioxide is one of the ways to solve the environmental problems caused by greenhouse gases, the problem of fossil energy depletion, and the problem of fuel exhaust emissions. However, after decades of development, the process of preparing methanol from carbon dioxide is still unable to achieve large‐scale industrialization. This critical review sharply points out the problems and obstacles that need to be solved urgently on the industrialization road of carbon dioxide methanol preparation process and summarizes its progress. The problems faced by the methanol production industry from carbon dioxide are first the thermodynamic constraints, second the lower reaction rate and conversion effect, and finally the energy loss. In order to solve these problems, this paper first introduces the active sites, structural effects, and dynamic changes of copper‐based catalysts, as well as the unique reaction mechanism and doping modification of indium‐based catalysts. Zinc and zirconium promoters and some metal oxide supports can form unique interactions with active components to improve the catalytic performance of the catalyst. Next, various kinetic models and applications of methanol production from carbon dioxide are summarized, which is an important bridge linking laboratory and industrialization; the advantages and disadvantages of fixed bed reactor and paddle reactor were compared. Finally, the full text is summarized and prospected.

A comparison study between the lithium sulphur battery and fuel cell in new energy vehicle applications

In recent years, the development of new energy vehicles has been rapid to solve the emerging energy depletion and carbon emission problems. Lithium-sulfur batteries and fuel batteries have become the most competitive two development directions in the new generation of power batteries for electric vehicle applications. Lithium-sulfur battery has a high specific capacity, low production cost and is environmentally friendly. However, some problems include low coulomb efficiency and short life span in the process of lithium-sulfur battery production. Fuel cells are also environmentally friendly, energy-efficient and drive efficiently. But fuel cells are extremely expensive, and their longevity problems regarding the membranes and electrode materials and supporting infrastructure, such as the hydrogen and oxygen storage obstacles, must also be overcome. This paper focuses on analyzing the two new energy vehicle power resource principles, advantages and disadvantages of two kinds of typical batteries, the current development bottleneck, and respectively put forward suggestions for future development.

Nanocellulose-based advanced materials for flexible supercapacitor electrodes

Nanocellulose is sustainable, has excellent properties and a unique structure that can be used to create environmentally friendly supercapacitor electrodes with pore structures. Supercapacitor, as a high-performance electrochemical energy storage device, has the advantages of high power density, long cycle life, fast charging and discharging, and high energy density. Currently, the problem of energy depletion is becoming more and more serious, and in order to solve this problem, people are beginning to replace traditional petroleum-based materials with environmentally friendly biomass-based materials. Nanocellulose, as a derivative of cellulose, the most widely distributed biomass in nature, has high specific surface area, biodegradability, high strength and good dispersibility. This review discusses and summarizes the latest research on nanocellulose-based supercapacitor electrode materials. First, the physical and chemical properties of nanocellulose and the preparation strategy are presented. Second, the types of electrodes are introduced in terms of electrodes with nanocellulose as substrate and those with other support materials. Next, recent studies on the use of these electrodes for flat supercapacitors and fiber-based supercapacitors are presented. Finally, the future prospects and challenges of using nanocellulose in supercapacitor electrode materials are outlined.

Dispatch for energy efficiency improvement of an integrated energy system considering multiple types of low carbon factors and demand response

Faced with the problem of fossil energy depletion and the power industry’s low-carbon requirements, low-carbon technologies in collaboration with market mechanisms, supplemented by flexible resources, are critical to achieving the low-carbon operation of integrated energy systems (IES). This paper establishes an IES considering multiple types of low-carbon factors and demand response. Firstly, the IES is deemed to participate in the carbon trading market and introduce a ladder-type carbon trading mechanism at the low-carbon policy level. Then, at the low-carbon technology level, carbon capture power plants and power-to-gas equipment are introduced to refine the modeling of the power-to-gas process. Secondly, the integrated energy system includes pluralistic energy storage technology and demand response to increase the IES’s flexibility. Based on multiple types of low carbon factors and demand response mechanisms, the scheduling model for energy efficiency improvement is constructed with the lowest sum of the cost of wind abandonment penalty, the cost of purchasing energy, the cost of equipment operation and maintenance, and the cost of carbon trading as the optimization objective, and solved by a two-stage optimization method. Five energy efficiency indicators are presented to efficiently evaluate dispatching results: wind power consumption rate, carbon trading cost, actual carbon emissions, total cost, and load fluctuation. Finally, according to an arithmetic test system based on various operation scenarios, the proposed model may increase the IES’s comprehensive energy efficiency under the coupling effect of multiple types of low-carbon factors and demand response.

Cleaner production strategy tailored versatile biocomposites for antibacterial application and electromagnetic interference shielding

The clean and sustainable development of biomass resources has attracted tremendous attention to alleviate the increasingly serious energy depletion and environmental problems. Hence, it is urgently needed to design a green product through a cleaner production strategy to bridge the gap between materials and sustainability toward environmental protection and practicality. Herein, a bamboo fiber/polybutylene succinate (PBS) biocomposite that carries silver nanoparticles (AgNPs) with electromagnetic interference (EMI) shielding and antibacterial activity, as well as the performance of interface reinforcement, were prepared using a green, efficient, and scalable bionic strategy of PDA coupling with minimal environmental footprint. The proposed bamboo fiber/PBS biocomposites, which showed multifunctionality and environmental adaptability, revealed good antibacterial activity, where the average inhibition zone diameters against E. coli and S. aureus bacteria reached 5.37 ± 0.36 and 3.96 ± 0.74 mm, respectively. Simultaneously, gains in the EMI shielding effectiveness (∼29.72 dB) and mechanical properties (improved by 24.58% in tensile strength, 39.13% in tensile modulus, 14.60% in flexural strength, and 19.67% in flexural modulus) were realized. Overall, this work displays a novel pathway toward eco-friendly and multifunctional nature fiber/polymer composites for high-value utilization.

On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems

In recent times, wireless energy transfer has become an effective solution to charge devices due to its efficiency and reliability. In a typical Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer technique can solve the energy depletion problem with the aid of a Wireless Charging Vehicle (WCV), thereby enabling the network to extend its lifetime. However, sensor nodes in a WRSN still have their energies depleted before it gets replenished by the WCV. In this paper, we proposed a scheme that prioritizes sensor nodes for charging and also developed efficient algorithms to improve on existing charging schemes so as to extend the lifetime of the WRSN. Firstly, an inspection algorithm was developed to visit and inspect sensor nodes in the network so as to determine the sensor nodes to charge. Secondly, a greedy charge algorithm was introduced to ascertain the shortest distance the WCV needs to travel and, lastly, an energy for nodes’ algorithm was proposed to determine the stopping point and when the WCV needs to return to the base station. Simulation experiments were also conducted to determine the performance of our scheme. The simulation experiments revealed that our proposed scheme made significant improvements when compared to other schemes in literature using several metrics.

Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks

The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes’ energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms.

Research on Ultra-low Permeability Oil Development System Based on Oilfield Fracturing Water Displacement System

In order to realize the effective water injection development of oilfield reservoirs, large-scale fracturing technology was introduced. The simulation confirmed that the large-scale fracturing well has the characteristics of high initial production, fast water injection effect, and rapid water cut rise after the effect., the research results show that compared with elastic development, water injection development can solve the problem of rapid formation energy depletion, and at the same time increase the reservoir producing area and displacement time. The final water content of water injection huff and puff is about 25% lower than that of conventional water injection, and advanced water injection can increase the production of conventional elastic development by about 30% in the early stage. It can be seen that these two new development methods are conducive to suppressing water flooding and improving oil recovery.

Optimizing The Waste Biomass Of Peanut Shells And Water Hyacinth As An Environmentally Friendly Power Plant

According to the Ministry of Energy and Mineral Resources of the Republic of Indonesia, shows that coal deposit in Indonesia about 7,3 - 8,3 billion ton are exhausted in 2036, while cruel oil around 4,7 billion barrels are exhausted in 2028, and fuel gas is estimated to run out more quickly in 2027. So it needs alternative energy as a solution in dealing with energy problems in the world, one of them the solution is the utilization of biomass. The cellulose in the biomass is potentially to use as alternative energy, for example, the peanut shell and water hyacinth. The material goes through the process of converting from a solid mass to gas using a gasification method. The gasification method with a high temperature is one way of converting carbon-based organic materials into synthetic gases namely carbon monoxide (CO), Hydrogen (H2), and methane (CH4). Synthetic gas is used as motion energy to rotate the generator until it generates electricity. Thus an eco-friendly power plant will be formed. Some research showed that the level of cellulose in the peanut shell is 63.5 %, which is capable of producing a heat engine of 14.34 MJ/Kg equivalent to 3425,05 cal/gr. Moreover, the cellulose in the water hyacinth of 64.51 % produced values the heat engine reached 4341,67 cal / g. The higher value heat engine shows that the ingredient in producing energy is bigger and it has the potential to become sources of electrical energy . In conclusion, the optimization of the cellulose in the peanut shell and water hyacinth is made as a source of environmentally friendly power plants that is expected to addressing the problem of energy depletion.

Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

Wireless sensor networks consist of sensor nodes that can sense the environment and forward the sensed data to the destination. It takes multiple hops for the data to get transmitted from the source to the destination node. Mobile Data Patron is a high energy mobile data collector that collects data from the sensors within sparse networks. In sparse networks, the sensors detect the physical phenomenon, and the MDP is used to collect data generated by the sensors. In existing models, a single MDP is used to collect data from sensors within sparse networks. It requires high amounts of energy for long-range data transmission to the base station. The proposed work uses multiple MDPs to solve the energy depletion problem. The research also focuses on improving the data transfer rate so that the MDPs lose minimal amounts of energy.

DHCFL: Dual Head Clustering Based On Fuzzy Logic for Wireless Sensor Networks

Wireless Sensor Networks (WSN) are constructed by interconnecting miniature sensor nodes for monitoring the environment uninterrupted. These miniature nodes are having the sensing, processing and communication capability in a smaller scale powered by a battery unit. Proper energy conservation is required for the entire system. Clustering mechanism in WSN advances the lifetime and stability in the network. It achieves data aggregation and reduces the number of data transmission to the Base station (BS). But the Cluster Head (CH) nodes are affected by rapid energy depletion problem due to overload. A CH node spends its energy for receiving data from its member nodes, aggregation and transmission to the BS. In CH election, multiple overlapping factors makes it difficult and inefficient which costs the lifetime of the network. In recent years, Fuzzy Logic is widely used for CH election mechanism for WSN. But the underlying problem of the CHs node continues. In this research work, a new clustering algorithm DHCFL is proposed which elects two CHs for a cluster which shares the load of a conventional CH node. Data reception and aggregation will be done by CH aggregator (CH-A) node and data transmission to the BS will be carried over by CH relay (CH-R) node. Both CH-A and CH-R nodes are elected through fuzzy logic which addresses the uncertainty in the network too. The proposed algorithm DHCFL is compared and tested in different network scenarios with existing clustering algorithms and it is observed that DHCFL outperforms other algorithms in all the network scenarios

Green communication in sensor enabled IoT: integrated physics inspired meta-heuristic optimization based approach

Green communication plays a significant role in sensor enabled IoT. Energy is at the focal point in the smart application of the IoT which enables the sensors to be work. The faster energy depletion causes the hindrance in efficient functioning of the sensors. An energy-efficient scheme is required to prevent the faster reduction of energy from sensors in IoT. Metaheuristic techniques are most effective to solve such problems with the near-optimal solution as heuristic techniques are not suitable for such problem, they may turn into NP-hard problem in dense area sensor networks. Most of the optimization-based energy-efficient techniques suffer from unstable energy depletion problem because nodes near to the base station (BS) have more traffic load. In this paper, an energy balanced integrated atom swarm and electromagnetic force optimization (iASEF) scheme is proposed to overcome the energy depletion problem. The iASEF consists of (a) A linear programing problem of clustering that consisting of an objective function and constraints based on node degree, intra-cluster distance, residual energy of node and inter-cluster distance, (b) optimal routing problem. Atom search optimization has been employed to solve linear problem to find optimal cluster head (CH) among sensors. Electromagnetic force optimization has been used to solve routing problem to find next hop for data forwarding between the CH and BS. The simulation results demonstrate that the proposed iASEF scheme achieves substantial enhancement over the state-of-art algorithms.

Performance Analysis of Modified AODV Routing Protocol With Lifetime Extension of Wireless Sensor Networks

Amalgamation of Re-Charging and Load Balancing are introduced here to overcome the problem of quick battery energy depletion and jamming in Wireless Sensor Networks. The primary approach (Re-Charging) is to maintain the desired energy level at each node in the network. The second method guarantees jamming-free communication in the network. To maintain the required energy level of the nodes, the SenCar visits the chosen anchor point along the pre-defined trajectory. At the chosen point, SenCar tests all the nodes within its coverage and re-energize all the nodes that have the energy level below the threshold value (3 Joules). Then, the SenCar moves on to the next anchor point and so on until the pre-defined trajectory completes. Finally, the implementation of the proposed mechanism proves the performance enhancement of Wireless Sensor Networks in terms of Throughput, Energy Level, and Packet Delivery Ratio.

Piezo/triboelectric nanogenerators based on 2-dimensional layered structure materials

Recently, research on energy harvesting has attracted great attention as a solution to energy depletion and environmental problems due to the use of fossil fuels such as coal, natural gas, and oil. To be precise, harvesting technology converts the energy sources around us such as solar, heat, and mechanical energy into electrical energy. It has the advantage of being able to supply and sustain energy on a permanent basis, rather than being non-renewable, and it is also eco-friendly. Among the various energy harvesting techniques, nanogenerators based on piezoelectric and triboelectric phenomena can generate electrical energy based on mechanical energy sources, which are usually ubiquitous, there are no restrictions due to weather, time, or space, and this technology is also user-friendly. Recently, two-dimensional (2D) materials have been chosen for implementing piezo/triboelectric nanogenerators. The 2D materials have transparency, flexibility, and a high surface-to-volume ratio. Owing to the very low thickness of the atomic unit, a stacking structure using 2D materials can be also made to form a very thin device, which is applicable for insertion into the body or wearable electronic devices. In this review, we summarize the characteristics and research results on piezo/triboelectric energy harvesters based on 2D layered structure materials.

Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks

Recent breakthrough progress of wireless energy transfer technology and rechargeable lithium battery technology emerge the Wireless Rechargeable Sensor Networks (WRSNs). To avoid sensor node failure, how to schedule the Mobile Charger (MC) to recharge sensor nodes in WRSNs is very challenging. Previous works that predetermining the charging path of MC cannot adapt to the diversity and dynamic energy consumption of sensors in actual environment and may lead to problematic schedules. Many online charging schemes are proposed to overcome the challenges, but they still get performance limitation due to leaving out of consideration about the energy depletion issue resulted from not timely and/or unfair charging response. Particularly, the node failure problem will be worse when there are a large number of charging requirements in the network. In this paper, we address the node failure avoidance mobile charing for WRSNs, which aims to minimize the number of invalid nodes due to sensor node energy depletion in the charging process. We first consider the dynamic energy consumption rate of the node based on both its history statistics and real time energy consumption. Then we propose two efficient online charging algorithms named PA and INMA, respectively. PA selects the next charging node according to the charging probability of the requesting nodes, whereas INMA always chooses the nodes which make the least number of other requesting nodes suffer from energy depletion as the charging candidates. Furthermore, to achieve high charging efficiency, the node with the shortest time to finish the charging will be selected as the next charging node if there are multiple nodes in the candidate set. Simulation results demonstrate that the proposed algorithms can effectively solve the node energy depletion problem with lower charging latency and charging cost in comparison with other current existing online charging schemes.

Multi-Objective Optimal Design of Permanent Magnet Synchronous Motor for High Efficiency and High Dynamic Performance

There is a strong demand for the research of electric vehicles (EVs) in automotive industry, because of an increased concern of the energy depletion and environmental pollution problems caused by oil-fueled automotive. The traction motor drive system is one of the core components of EVs, and a motor with superior dynamic performance and high efficiency could significantly reduce energy consumption and improve riding comfort of EVs. Therefore, in order to achieve high dynamic performance and high efficiency of permanent magnet synchronous motor (PMSM), a multi-objective optimization design method for PMSM based on the artificial bee colony algorithm was proposed in this paper. First, based on the magnetic field analytical model of PMSM, the analytical expressions of the key parameters were deduced, namely, mechanical time constant and electrical time constant. Second, the efficiency and electrical and mechanical time constant were defined as optimization objectives. Third, the efficiency and dynamic performance of the original motor and optimized motor were compared applying the finite-element analysis. Furthermore, one prototype machine was manufactured according to the results of optimization. The dynamic performance and efficiency of the prototype had been tested. The experiments show confident results that the efficiency increased about 1% and the mechanical time constant reduced to 31.4% of initial value.

Distributed Energy Efficient Clustering Algorithm to Optimal Cluster Head by Using Biogeography Based Optimization

Wireless sensor network consist of hundred to thousand of sensor nodes with limited energy capacity. It is generally difficult to recharge or replace these sensor nodes. Energy efficiency is thus a primary issue to maintain a wireless network. The problem of energy depletion of nodes is common for all data collection scenarios in which cluster head have a heavy burden of gathering and relaying information. In this paper we propose an energy efficient clustering algorithm “Distributed energy efficient clustering biogeography based optimization algorithm” to elect optimal cluster head based on highest residual energy and appropriate packet forwarding to the sink with respect to sensor nodes. This algorithm gives the better simulation results in comparison to DEEC algorithm.

The Association of Energy Depletion Problems With Retention of Daily Life Activities in People With Parkinson’s Disease

Abstract Date Presented 3/31/2017 The study proposes the construct of energy depletion in people with Parkinson’s disease (PwPD) and evaluates its relationship with activity retention. PwPD with more fatigue and sleepiness retained fewer activities. Important to occupational therapy practice is that the direction of causality may be bidirectional. Primary Author and Speaker: Cailin D. Stewart Contributing Authors: Marie Saint-Hilaire, Cathi A. Thomas, Linda Tickle-Degnen