Reduced Energy Utilization Research Articles

On the contact and vibration characteristics of TBM cutter with abnormal damage under hard rock conditions

Tunnel Boring Machines (TBM) play a pivotal role in the construction of modern tunnels, with their performance directly determining the progress, economic benefits, and safety levels of the construction. As the core component interfacing with the rock, the condition of the TBM cutter significantly impacts the machine's boring performance. Faced with increasingly complex geological conditions, the cutters frequently suffer from abnormal damage, including partial wear, polygon wear, and chipping. Such damages are typically accompanied by reduced rock-breaking ability and increased vibration intensity, leading to severe performance degradation. To propose effective monitoring and maintenance strategies to prevent serious consequences of abnormal damage, such as cutterhead damage or personnel injuries, it is necessary to understand the changes in the operational characteristics of the cutter in such conditions and to accurately assess the specific impacts of abnormal damage on cutter performance. This study uses bench tests and discrete element-dynamics coupled simulations to explore the differences in load-bearing, cutter-rock contact, vibration, and noise characteristics between the normal wear cutter and those with abnormal damages. The results show that abnormal damages affect the cutter's load-bearing and cutter-rock contact properties, thereby increasing the proportion of cutting resistance and reducing energy utilization and rock-breaking efficiency. Additionally, partial wear and polygon wear not only significantly increase the noise/vibration levels of the cutter but also cause the mode of noise and vibration to shift from random to periodic, thus increasing the likelihood of forced vibrations in the system. By combining simulation and experimental results, this study elucidates the mechanisms by which different types of abnormal damage affect cutter performance by altering the motion form and cutter-rock contact mode. The operational characteristics of cutters with abnormal damages, as revealed in this study, in terms of load, vibration, and noise, provide theoretical support for the state monitoring and maintenance strategies of TBM cutters.

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

Impacts of Low-Carbon City Pilot Policy on Urban Land Green Use Efficiency: Evidence from 283 Cities in China

On the global scale, the low-carbon city pilot policy (LCCPP) has important significance for and influence on the study of urban land green use efficiency (ULGUE). Based on the panel data of 283 cities in China from 2007 to 2019, this study uses the super-SBM model, multi-period DID model, spatial econometric model, intermediary effect model, and heterogeneity analysis methods to deeply explore the specific impact mechanism of LCCPP on ULGUE. The results show the following: (1) During the study period, the average ULGUE of the selected samples increased by 11.71 percentage points overall and showed a certain spatial agglomeration effect. (2) LCCPP has a significant promoting effect on the improvement of ULGUE, and there is a positive spatial spillover effect. (3) The impact of LCCPP on ULGUE is mainly achieved through two paths: reducing energy utilization intensity and improving urban innovation level. (4) In cities with different levels of land green use efficiency, geographical location, and resource endowment, there are significant differences in policy effects. This paper puts forward countermeasures and suggestions to comprehensively promote the sustainable development of global cities and the improvement of land green use efficiency.

A voltammetric method coupled with chemometrics for determination of a ternary antiparkinson mixture in its dosage form: greenness assessment

An electroanalytical methodology was developed by direct differential pulse voltammetric (DPV) measurement of Levodopa (LD), Carbidopa (CD) and Entacapone (ENT) mixture using bare glassy carbon electrode (GCE) in Britton Robinson (BR) buffer (pH = 2.0). A multivariate calibration model was then applied to the exported preprocessed voltammetric data using partial least square (PLS) as a chemometric tool. Additionally, the model was cross-validated and the number of latent variables (LVs) were determined to produce a reliable model for simultaneous quantitation of the three drugs either in their synthetic mixtures or in their marketed pharmaceutical formulation with high accuracy and precision. Data preprocessing was used to tackle the problem of lacking bi-linearity which is commonly found in electrochemical data. The proposed chemometric model was able to provide fast and reliable technique for quantitative determination of antiparkinson drugs in their dosage forms. This was successfully achieved by utilizing sixteen mixtures as calibration set and nine mixtures as validation set. The percent recoveries for LD, CD and ENT were found to be 100.05% ± 1.28%, 100.04% ± 0.53% and 99.99% ± 1.25%, respectively. The obtained results of the proposed method were statistically compared to those of a previously reported High Performance Liquid Chromatography (HPLC) methodology. Finally, the presented analytical method strongly supports green analytical chemistry regarding the minimization of potentially dangerous chemicals and solvents, as well as reducing energy utilization and waste generation.

ANGPTL3 Downregulation Increases Intracellular Lipids by Reducing Energy Utilization.

ANGPTL3 (angiopoietin-like protein 3) is a circulating protein with a key role in maintaining lipoprotein homeostasis. A monoclonal antibody against ANGPTL3 is an approved and well-tolerated treatment to reduce lipoproteins in familial hypercholesterolemia homozygotes. However, the reduction of hepatic ANGPTL3 synthesis using an antisense oligonucleotide unexpectedly resulted in a dose-dependent increase in liver lipid content and circulating transaminases, resulting in the termination of the clinical trial. Meanwhile, the use of silencing RNAs remains an area of active investigation. Our study sought to investigate whether intracellular downregulation of ANGPTL3 may lead to a primary increase in neutral lipids within the hepatocyte. We downregulated ANGPTL3 by silencing RNA in primary human hepatocytes 3-dimensional spheroids, HepG2/LX-2 3-dimensional spheroids, and in HepG2, Hep3B2, and Huh7 cultured in 2 dimensions. ANGPTL3 downregulation increased neutral lipids in all models investigated. Interestingly, ANGPTL3 induced lower intracellular deiodinase type 1 protein levels resulting in a reduction in beta-oxidation and causing an increase in triglycerides stored in lipid droplets. In conclusion, intracellular ANGPTL3 downregulation by silencing RNA led to an increase in triglycerides content due to a reduction in energy substrate utilization resembling a primary intracellular hepatocyte hypothyroidism.

How do information and communication technology, human capital and renewable energy affect CO2 emission; New insights from BRI countries

If nations want to attain sustainable development with the exponential growth of information and communication technology (ICT) around the world, they must understand the connection between ICT and carbon emissions. Therefore, this study has used panel data from 64 ‘‘Belt and Road Initiative economies between 2000 and 2021 while finding the impact of ICT, renewable energy consumption (REC), human capital (HC) and economic growth (EG) on CO2 emissions. This study employs the Augmented Mean Group (AMG) estimator, Mean Group (MG) estimator and the Dumitrescu-Hurlin panel causality. The findings indicate that the use of ICT, HC and the REC are inversely related to CO2 emissions, whereas EG is positively associated to CO2 emissions and hence poses a danger to environmental sustainability. In addition, the interaction term of EG with ICT, REC and HC has negative impact on CO2 emissions in BRI economies. Intriguingly, the results reveal that ICT and CO2 emissions has inverted U-shape relationship in BRI economies. Furthermore, the causality results show that ICT, REC, and human capital are all cause and effect linkages that affect CO2 emissions in both directions. In order to reduce energy utilization and boost economic growth, the findings stress the importance of implementing cutting-edge ICT and REC in the industrial sector.

Photovoltaic-wind-battery and diesel generator-based hybrid energy system for residential buildings in smart city Coimbatore.

The building consumes almost 40% of the energy generated in the building. Investigating the photovoltaic system, wind, battery, and diesel generators for residential buildings can reduce energy utilization. In this work, various energy sources are combined to form hybrid energy sources, which are designed based on the load of the residential building. The Hybrid Optimization of Multiple Energy Resources tool optimizes various energy sources such as photovoltaic (PV), wind, diesel generator (DG), and battery. An investigation on the residential load in the smart city of Coimbatore, Tamil Nadu, India, is being carried out. This article examined the technological and economic feasibility of solar photovoltaic, wind, diesel generators, and batteries combined to form a hybrid energy source (HES). With 2kW of photovoltaic, 1kW wind, 1kW of DG, 1kW of the power converter, and five batteries, system case 1 (photovoltaic/wind/diesel generator/battery model) had the best results in the simulation and was recommended for use in the proposed residential building. As a result, it has a minimum net present value of $14,568 and an energy cost of $0.312/kWh, which is about 39% cheaper than system base cases. The sensitivity and environmental analysis are carried out to analyze the system's feasibility.

Sustainable supply chain of distributed multi-product gas fields based on skid-mounted equipment to dynamically respond to upstream and market fluctuations

Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative design and operation optimization mixed integer nonlinear programming (MILP) method for distributed supply chain based on skid equipment. Unlike the traditional methods, the proposed MILP method can simultaneously obtain upstream production planning, midstream modular equipment and processing capacity allocation, and downstream transportation allocation schemes for various natural gas products such as liquefied natural gas (LNG), compressed natural gas (CNG) and pipeline natural gas (PNG) of each time periods by making the maximum net present value (NPV) of the full development cycle as the target. In order to prove the superiority and usability of the proposed method, four operation scheduling modes are compared through two comprehensive case analysis. Additionally, the effects of gas well productivity, market demand and product price are investigated through sensitivity analysis. The results show that comparing with the traditional method, the proposed method can effectively improve the loading rate of the processing equipment, increase the overall revenue of gas field development, integrate the operation in the upstream, midstream and downstream of the supply chain, dynamically adapt to the gas production and marketing fluctuations. This study provides a creative way to obtain better profit, reduce energy utilization and promote cleaner production for sustainable supplies management in gas industry.

Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.

Chicken swarm optimization modelling for cognitive radio networks using deep belief network-enabled spectrum sensing technique.

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques.

Sustainable Manufacturing Practices: Evaluating the Environmental Benefits of Biodegradable Polymers and Recycled Metals

Among all industries looking for economic development and environmental responsibility, sustainable manufacturing has gained popularity worldwide. This study reviews how biodegradable polymers and recycled metals can be utilized to encourage sustainable manufacturing. In reality, biodegradable polymers are made from renewable materials such as starch or cellulose; hence they can be utilized rather than common plastics and they can avoid contamination caused by non-disposable polymers while at the same time improving resource productivity. Biodegradable polymers have found various applications in packaging, agriculture, and pharm sectors where they contribute towards sustainable product advancement through their potential to break down under certain conditions ensuring that no waste remains behind after usage. On the other hand, recycled metals are significant for accomplishing circularity in manufacturing systems and bringing down the natural impacts related to different stages of manufacturing processes. Recycled metals offer assistance to save natural resources, reduce energy utilization, and lower the amount of GHGs produced by mining and refining virgin ore. For sustainable manufacturing, this paper highlights participation as well as development and technology integration among other things. For instance; making eco-friendly materials may be one such activity while digitalizing production processes can be another great thought for improving environmental sustainability in the manufacturing sector. Moreover, this paper suggests a few areas that can foster greater natural protection amid production activities like switching over renewable sources of power supply for energy usage. These actions will not only guarantee economic development but also protect our environment because somehow they are interrelated to each other.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

A Combined Dual Leader and Relay Node Selection for Markov Cluster Based WSN Routing Protocol

The major challenge in Wireless Sensor Networks (WSNs) is to increase the node’s lifespan and decrease energy utilization. To avoid this issue, many Clustering Routing Protocols (CRPs) have been developed, where Cluster Head (CH) in each cluster accumulates the data from each other node and transfers it to the sink through Relay Nodes (RNs). But both CHs and RNs dissipate more energy to aggregate and transfer data. As a result, it is vital to choose the appropriate CHs and RNs concurrently to reduce energy utilization. Hence, this article proposes a Weighted Markov Clustering with Dual Leader and Relay node Selection based CRP (WMCL-DLRS-CRP) in WSNs. This protocol aims to lessen energy dissipation during inter- and intra-cluster communication. Initially, a Markov Clustering (MCL) algorithm is applied by the sink to create nodes into clusters based on a threshold distance. Then, a dual leader selection scheme is proposed to elect dual CHs in each cluster according to the node weighting factor that considers the node’s remaining energy, the distance between CHs and sink, the distance among all nodes, and abundance. Also, an RN selection scheme is proposed to choose the appropriate RNs based on a new Predicted Transmission Rate (PTR) factor. Moreover, the elected RNs transfer the data from the CHs to the sink, resulting in a tradeoff between the node’s energy utilization and lifetime. At last, extensive simulations illustrate that the WMCL-DLRS-CRP achieves better network performance compared to the existing protocols.

Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.18$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 2.35$</tex-math></inline-formula> compared to the baseline methods while maintaining similar performance.

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

EEGT: Energy Efficient Grid-Based Routing Protocol in Wireless Sensor Networks for IoT Applications

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network.

Carbon Nanotube/Hygroscopic Salt Nanocomposites with Dual‐Functionality of Effective Cooling and Fire Resistance for Safe and Ultrahigh‐Rate Operation of Practical Lithium‐Ion Batteries

AbstractFire and explosion accidents and reduced energy utilization due to poor cycling stability of lithium‐ion batteries (LIBs) caused by inevitable internal temperature rise during high‐rate operations have become a growing concern. Herein, a dual‐functional carbon nanotube/hygroscopic salt (DFCNT/HS) film with effective passive cooling performance and fire insulation for the safe usage of practical LIBs under extremely fast discharging conditions is reported. The DFCNT/HS film based on the cooling mechanism of self‐adaptive moisture absorption/desorption delivers a high cooling power of 32.9 W m−2 K−1, which can reduce the maximum temperature of a 18650–3.6 V/2.0 Ah LIB by 11.2 and 17.4 °C at discharging rates of 10 and 15 C, respectively. Covering the cooling film, the battery discharges 23.6 Ah more total capacity at 10 within 500 cycles. What is challenging, almost three‐fold extended lifetime of 425 cycles is achieved at 15 C with an extra total capacity of 467.2 Ah. Meanwhile, the developed film also shows an excellent high‐temperature resistance up to 540 °C, which can alleviate the devastating fire propagation. The fast heat dissipation and excellent fire insulation as well as the mechanical flexibility and manufacturing scalability make this new material promising for safe usage of high‐rate LIBs with zero energy consumption.

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

Renewable integration and energy reduction in multiple stage evaporator

• Development of steady state energy model for a hybrid MSE. • Formulating the constrained optimization problem to maximize energy efficiency. • Implementation of a metaheuristic approach named Flow Direction Algorithm. • Incorporation of FTs and LFR to reuse waste heat and meet required thermal demand. Multiple Effect Evaporator (MEE) is an imperative part of the paper industry. It is used to process the waste byproduct named Black Liquor to enhance its solid concentration that may be further utilized to produce biomass-based energy. This process accounts very high energy consumption which leads to the energy intensiveness of this unit. A substantial amount of energy-saving may be accumulated by integrating various Energy Reduction Schemes (ERSs) with the MEE. Steam splitting, feed splitting, feed liquor preheating, thermo-vapor compressor, etc. are the well-known ERSs that may be incorporated with the MEE to reduce energy utilization. Hence, this study investigates the hybrid ERSs incorporated with MEE to observe their energy efficiency in an optimal manner. The prime focus of this investigation is to achieve enhanced energy efficiency in teams of maximum steam efficiency which is computed by formulating the nonlinear mathematical models of the MEE. To achieve the maximized energy efficiency, an optimization function has been developed under a constraint environment and solved by using metaheuristic-based approach named Flow Direction Algorithm. Another ERSs named flash tanks can also be integrated with this system to encourage waste heat re-utilization and ensures an increase in energy efficiency with constant steam consumption. This work also deals with the incorporation of the Linear Fresnel Reflector with ERSs integrated MEE which is capable of reducing the conventional energy load. The overall integrated MEE model achieves an energy-efficient and renewably operated MEE unit that may enhance the self-sustainability of the industry.

Practical Evaluation of Loss Reduction in Isolated Series Resonant Converter with Fixed Frequency Modulation

Nowadays, power converters with reduced cost, compact size and high efficiency are evolving to overcome the emergent challenges of renewable energy integrations. In this context, there is an increased demand for well-designed power converters in renewable energy applications to reduce energy utilization and handle a variety of loads. This paper proposes a center-tapped bridge cascaded series-resonant LC dual active bridge (DAB) converter for DC-DC conversion. The low part count of the proposed converter enables a high-power density design with reduced cost. The proposed converter offers reduced conduction losses as the reverse current is eliminated by adopting current blocking characteristics. Reverse current blocking also enables zero voltage switching (ZVS) and zero current switching (ZCS) over a wide operating range. Therefore, using a simple fixed frequency modulation (FFM) scheme offers a wide operating range compared to a conventional DAB converter. A thorough comparison of the proposed converter and a conventional DAB converter is provided based on conduction losses and switching losses to illustrate the performance improvement. Lastly, the effectiveness of the proposed converter is validated through simulation and experimental results.