Academic literature on the topic 'CRITICAL MACHINE ENERGY'
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Journal articles on the topic "CRITICAL MACHINE ENERGY"
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Chen, Chi, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, and Shyue Ping Ong. "A Critical Review of Machine Learning of Energy Materials." Advanced Energy Materials 10, no. 8 (January 29, 2020): 1903242. http://dx.doi.org/10.1002/aenm.201903242.
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Ohtani, Hisashi. "Development of Energy-Saving Machine Tool." International Journal of Automation Technology 11, no. 4 (June 29, 2017): 608–14. http://dx.doi.org/10.20965/ijat.2017.p0608.
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Environmental measures are urgently required to realize a society with a low environmental load. In response, various undertakings are being carried out in the field of machine tools, which is most critical in terms of requirement of energy-saving measures. The energy consumed when a machine tool is used to machine can be broadly divided into three categories: the “standby energy” to maintain the electrical devices operational when the machine is not operational; the “steady-state energy,” which is the fixed amount of energy required when the machine is in operation; and the “dynamic energy,” which varies with the machining conditions and other factors. Measures are necessary for each of these energy categories to reduce energy consumption. This paper describes an example of an energy-saving study undertaken for a machine tool used for machining gears; a new processing method called skiving was developed to consolidate work processes.
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Fujishima, Makoto, Hiroshi Shimanoe, and Masahiko Mori. "Reducing the Energy Consumption of Machine Tools." International Journal of Automation Technology 11, no. 4 (June 29, 2017): 601–7. http://dx.doi.org/10.20965/ijat.2017.p0601.
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Global warming is one of the most important environmental issues that the world faces today. Reducing energy consumption is critical in industrial environments. Machine tools have some of the highest energy consumption rates of all the equipment in factories. This makes it important to reduce machine tool energy consumption to protect the global environment. Some effective ways of reducing the energy consumption of machine tools are by reducing the required energy, shutting down the power to standby mode, and shortening cycle times. This paper introduces several approaches to the reduction of energy consumption.
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Yuan and Sun. "Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing." Sensors 19, no. 12 (June 17, 2019): 2724. http://dx.doi.org/10.3390/s19122724.
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High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.
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Alghamdi, Noof Awad, Israa Mohammed Budayr, Samar Mohammed Aljehani, and Majed Mohammed Aborokbah. "A Scheme for Predicting Energy Consumption in Smart Cities Using Machine Learning." Webology 19, no. 1 (January 20, 2022): 3481–99. http://dx.doi.org/10.14704/web/v19i1/web19230.
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Fluctuating result on weather condition throughout several decades became a global concern due to the direct or indirect effect on energy consumption, and that was well-defined in several sector. Research investigates the use of technology and the speed of obtaining information ، which helps in decision-making. This paper Emphasize the role of data science and their application to monitoring energy consumption, also, explain the importance used and challenges of Internet of Things (IoT). Thus, there is a global concern on data transformation from IoT devices when taking into account deferent weather variations. Cities are a critical part when of energy management, it presents the effect of urbanization and some of the success achievement in several cities around the world. Our Analysis indicate that three dissimilar types of sensors can detect massive amount of information up to four hundred thousand rows, compared to traditional methods for collecting data. The results depict the resilient of IOT performance which provide an aggregate of measures reach around 405,184 rows in a record time, with achieved accuracy up to 99% when implementing the decision tree algorithm, the outcome after applying the algorithm was vary 27.60 per-cent recorded by the first device while the other devices scored 26.14%,46.26% respectively, throughout different circumstances with continuous reading in a short period of times around 8 days.
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Kandil, Abdelrahman, Samir Khaled, and Taher Elfakharany. "Prediction of the equivalent circulation density using machine learning algorithms based on real-time data." AIMS Energy 11, no. 3 (2023): 425–53. http://dx.doi.org/10.3934/energy.2023023.
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<abstract> <p>Equivalent circulation density (ECD) is one of the most important parameters that should be considered while designing drilling programs. With increasing the wells' deep, offshore hydrocarbon extraction, the costly daily rate of downhole measurements, operating restrictions, and the fluctuations in the global market prices, it is necessary to reduce the non-productive time and costs associated with hole problems resulting from ignoring and incorrect evaluation of ECD. Therefore, optimizing ECD and selecting the best drilling parameters are curial tasks in such operations. The main objective of this work is to predict ECD using three machine learning algorithms: an artificial neural network (ANN) with a Levenberg-Marquardt backpropagation algorithm, a K neighbors regressor (knn), and a passive aggressive regressor (par). These models are based on 14 critical operation parameters that have been provided by downhole sensors during drilling operations such as annular pressure, annular temperature, and rate of penetration, etc. In the study, 4663 data points were selected and included, where 80% to 85% of the data set has been used for training and validation according to the algorithm, and the remaining data points were reserved for testing. In addition, several statistical tests were used to evaluate the accuracy of the models, including root mean square error (RMSE), correlation coefficient (R<sup>2</sup>), and mean squared error (MSE). The results of the developed models show various consistencies and accuracy, while the ANN shows a high accuracy with an R<sup>2</sup> of nearly 0.999 for the training, validation, and testing, as well as the overall of them. The RMSE is 0.000211, 0.000253, 0.00293, and 0.00315 for overall, training, validation, and testing, respectively. This work expands the use of artificial intelligence in the gas and oil industry. The developed ANN model is more flexible in response to challenges, reduces dependence on humans, and thus, reduces the chance of human omission, as well as increasing the efficiency of operations.</p> </abstract>
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RASTGOUFARD, P., and R. A. SCHLUETER. "APPLICATION OF CRITICAL MACHINE ENERGY FUNCTION IN POWER SYSTEM TRANSIENT STABILITY ANALYSIS." Electric Machines & Power Systems 16, no. 5 (January 1989): 343–61. http://dx.doi.org/10.1080/07313568908909392.
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Vijayapakavan, P., D. S. Robinson Smart, Kurinjimalar Ramu, and M. Ramachandran. "Superconducting Electromagnetic Launch Machine System for Aerospace Applications." Journal on Applied and Chemical Physics 2, no. 1 (June 1, 2023): 40–47. http://dx.doi.org/10.46632/jacp/2/1/5.
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The aerospace industry is constantly experimenting with innovative technologies to improve efficiency, effectiveness and sustainability. The use of superconducting machines emerged as a promising solution to address the growing demands of Aerospace applications. Superconducting machines offer significant advantages such as higher power density, reduced weight and improved efficiency compared to conventional electrical machines. However, efficient cooling methods are critical to maintain superconducting materials at low-temperature operating conditions. This abstract provides a comprehensive overview of superconducting machines and their associated cooling systems designed for space applications. A superconducting machine uses high-temperature superconductors to achieve near-zero electrical resistance, enabling high currents to be transmitted with low energy losses. This feature allows development of lightweight and compact electric propulsion systems contribute to improved fuel efficiency and extended mission capabilities in space vehicles. A cooling system is an important component of a superconducting machine because it ensures that the superconducting materials remain below their critical temperature. Various cooling techniques are being explored, including cryogenic cooling, liquid nitrogen cooling, and cryocoolers. These cooling systems effectively extract the heat generated during engine operation, maintaining the superconducting components in their superconducting state.
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Cristina Castejon, Cristina, Marıa Jesus Gomez, Juan Carlos Garcia-Prada, and Eduardo Corral. "Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft." Volume 24, No 3, September 2019 24, no. 3 (September 2019): 418–25. http://dx.doi.org/10.20855/ijav.2019.24.31190.
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Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.
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Trontl, Krešimir, Dubravko Pevec, and Tomislav Šmuc. "Machine Learning of the Reactor Core Loading Pattern Critical Parameters." Science and Technology of Nuclear Installations 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/695153.
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The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.
Dissertations / Theses on the topic "CRITICAL MACHINE ENERGY"
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Al, Marhoon Hussain Hassan. "Adaptive Online Transient Stability Assessment of Power Systems for Operational Purposes." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2036.
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Online stability assessment is an important problem that has not been solved completely yet. The purpose of this research is to tackle online transient stability assessment. Currently, most utility companies use step-by-step integration in order to set protective equipment so that they effectively work for critical contingencies. However, there are times an unforeseen contingency may occur which may cause the system to transit and the protective equipment to misoperate and does not isolate the disturbed part of the system. This research introduces a method that automatically determines a group of generators that participate in system separation and hence transient instability. The method consists of four phases: modeling and simulation, critical machines identification, online transient stability assessment, and critical clearing time calculation. In the modeling and simulation phase, the power system is built and the generators’ rotor angles and speeds are captured. In the critical machines identification phase, the average instantaneous rotor accelerating powers, coherency measures, the during-fault rotor angles and speeds characteristics, and the pre- and post-fault rotor angles are used to identify the Severely Disturbed Group (SDG) of machines. The results of this phase are used to calculate the kinetic energy of the SDG and potential energy of another (or possibly the same) group of generators. Utilization and success of the proposed method will be documented using results from the IEEE 39-Bus test system. Each step of each phase will be demonstrated as needed. The proposed method is compared to step-by-step integration and two direct methods. The suitability of the proposed method for operation will be shown in cases where the Y-Bus matrix and rotor angles and speeds are given. The proof of concept of the proposed method was used in simulating the test system and encouraging results of the simulation were published in [1] and [2]. The proof of concept is the foundation of the method proposed in this dissertation to determine transient stability of large-scale power systems.
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Al, Marhoon Hussain Hassan. "A Practical Method for Power Systems Transient Stability and Security." ScholarWorks@UNO, 2011. http://scholarworks.uno.edu/td/114.
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Stability analysis methods may be categorized by two major stability analysis methods: small-signal stability and transient stability analyses. Transient stability methods are further categorized into two major categories: numerical methods based on numerical integration, and direct methods. The purpose of this thesis is to study and investigate transient stability analysis using a combination of step-by-step and direct methods using Equal Area Criterion. The proposed method is extended for transient stability analysis of multi machine power systems. The proposed method calculates the potential and kinetic energies for all machines in a power system and then compares the largest group of kinetic energies to the smallest groups of potential energies. A decision based on the comparison can be made to determine stability of the power system. The proposed method is used to simulate the IEEE 39 Bus system to verify its effectiveness by comparison to the results obtained by pure numerical methods.
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.
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Castro, González Jesús. "Simulation of heat and mass transfer phenomena in the critical elements of H2O-LiBr absorption cooling machines. Experimental validation and application to design." Doctoral thesis, Universitat Politècnica de Catalunya, 2005. http://hdl.handle.net/10803/6692.
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Degut a la tendència a l'increment del preu de la energia, i el seu ús cada cop més estès per aire condicionat en els paisos desenvolupats, els sistemes de refrigeració basats en energia solar tenen cada cop més atractiu. El objectiu final d'aquesta tesi és el desenvolupament d'eines de simulació numèrica pel disseny de màquines de refrigeració per absorció que tinguin la possibilitat de funcionar amb energia solar. Malgrat existeixen en el mercat màquines d'absorció d'aquestes característiques des de fa anys, hi ha una deficiència en el desenvolupament de sistemes de petita capacitat. Els sistemes de petita capacitat impliquen problemes addicionals en el seu disseny (sistemes refrigerats per aire, compacitat ...) que només es poden abordar fent ús d'eines de disseny adequades, tant pel sistema com pels seus components. Tanmateix, hi ha també certa deficiència en la literatura especialitzada en el desenvolupament de models matemàtics adequats per la descripció dels processos de transferència de calor i de massa en les màquines de refrigeració per absorció: àrea mullada en les superfícies d'intercanvi de calor i de massa, paper dels additius, etc. Per aquestes raons aquest treball ha estat enfocat en aquests objectius:
- Estudi de processos bàsics de transferència de calor i de massa juntament amb els fenomens fluid-dinàmics implicats en absorbidors de màquines d'absorció. Aquest estudi ha estat fet mitjançant simulacions detallades resolent les equacions de Navier-Stokes sota ertes hipòtesis.
- Desenvolupament d'eines de simulació numèrica pel disseny i predicció de sistemes de refrigeració per absorció, aprofitant la informació donada per models més detallats.
- Desenvolupament d'eines de simulació numèrica pel disseny dels elements crítics d'intercanvi de calor i de massa de sistemes de refrigeració per absorció (absorbidor, generador, evaporador, condensador) mantenint el càlcul en un raonable temps de CPU. Aquest model recolza el mencionat en el punt anterior.
- Desenvolupament de un prototipus de màquina d'absorció, refrigerada per aire, fent servir H2O-LiBr com a fluid de treball, amb les eines numèriques desenvolupades.
- Contrastació experimental dels models desenvolupats.
- Estudi del funcionament de la màquina d'absorció anteriorment mencionada.
- Avaluació dels resultats per millorar els criteris de disseny i optimització del mateix de cara a prototipus de segona generació.
Després del desenvolupament d'aquestes eines de simulació numèrica que s'han fet servir per problemes específics sortits en el procés d'estudi d'una màquina en concret, un marc de treball ha estat creat per l'estudi d'altres sistemes de refrigeració per absorció.
Due to the increasing trend of the price of the energy, mainly obtained from fossil combustibles, and its also increasing use for air-conditioning in developed countries, solar cooling has been becoming more attractive from the point of view of economics and environment conservation. The final aim of this thesis is the development of numerical simulation tools for the design of absorption machines with the possibility of being driven by solar energy. Although there are available in the market absorption chillers of such characteristics for years, there is a lack in development of small capacity systems. Small capacity systems imply additional problems of design (air-cooled systems, compactness ...) that only can be afford with adequate design tools for system and components. Moreover, there is also a lack in the specialised literature in the development of adequate mathematical models for the description of the heat and mass transfer processes in absorption machines: wetted area of the heat and mass transfer surfaces, role of additives, complex geometries etc.
For these reasons this work has been focused on the following detailed objectives:
- Study of basic heat and mass transfer processes together with the fluid-dynamic phenomena implied in absorbers of absorption chillers. This study has beencarried out by means of detailed simulations solving the Navier-Stokes equations under certain hypotheses.
- Development of numerical simulation tools for design and prediction of absorption systems, taking advantage of information given by more detailed models.
- Development of numerical simulation tools for design of the heat and mass exchange components of absorption systems keeping the calculation in a reasonable CPU time. This model provides of the necessary information for the model mentioned in the previous point.
- Development of a prototype of an air cooled absorption machine based on the numerical results obtained from the models.
- Validation of the models developed by means of comparison of numerical results and experimental data obtained from the prototypes developed.
- Study of the performance of the above mentioned absorption system.
- Evaluation of the results in order to improve the design criteria for a second generation of prototypes.
After the development of these numerical simulation tools and their applicationin specific problems, a framework has been created for the study of other type of absorption systems.
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SEBASTIAN, BETSY. "TRANSIENT STABILITY ANALYSIS OF MULTIMACHINE POWER SYSTEM USING CRITICAL MACHINE ENERGY FUNCTIONS AND FIRST SWING STABILITY ANALYSIS OF SVC." Thesis, 2013. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15776.
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Transient stability analysis is an indispensible part of utility system planning. The industry
standard for transient stability performance varies from region to region, but it requires the
ability of the system to withstand severe disturbances such as faults or switching of lines.
Under this event, system linearization is not possible and the Differential Algebraic Equation
model of the power system need to be solved. Even though several direct stability methods
have been developed since the inception and identification of stability problem in multimachine
system, numerical method of solving the stability problem is still carried out
exploiting the features of digital computation. The result of this method is still being used as
the benchmark for determining the precision of direct methods of stability analysis. Keeping
in hand with the convention, Numerical method of solving stability problem has been carried
out in this project.
Stability analysis of multi-machine power system using energy functions of individual
machine and group of machines has been the main subject matter of this research study. The
simulations carried out reaffirm the concept of critical machines in stability analysis. These
are the machines which contribute to instability, and their transient energy separate them
from the rest of the system. Critical clearing time, an important parameter in design of circuit
breakers and various other protection equipments has been determined using energy functions
of individual machines, and critical group of machines. The values obtained using critical
machine energy function are found to be more accurate.
Power systems worldwide are undergoing transformation into complex entities with the
introduction of various fast controllers such as FACTS devices. Stability analysis
incorporating FACTS devices is the need of the hour. Some preliminary effort in this regard
has been carried out in this project. Initially the analysis have been carried out on SMIB
system and later on expanded to multi-machine power system by utilising the advantages of
MATLAB and DIGSILENT POWER FACTORY software.
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Le, Ha Thu. "Increasing wind power penetration and voltage stability limits using energy storage systems." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-05-864.
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The research is motivated by the need to address two major challenges in wind power integration: how to mitigate wind power fluctuation and how to ensure stability of the farm and host grid. It is envisaged that wind farm power output fluctuation can be reduced by using a specific type of buffer, such as an energy storage system (ESS), to absorb its negative impact. The proposed solution, therefore, employs ESS to solve the problems. The key research findings include a new technique for calculating the desired power output profile, an ESS charge-discharge scheme, a novel direct-calculation (optimization-based) method for determining ESS optimal rating, and an ESS operation scheme for improving wind farm transient stability. Analysis with 14 wind farms and a compressed-air energy storage system (CAES) shows that the charge-discharge scheme and the desired output calculation technique are appropriate for ESS operation. The optimal ESSs for the 14 wind farms perform four or less switching operations daily (73.2%-85.5% of the 365 days) while regulating the farms output variation. On average, the ESSs carry out 2.5 to 3.1 switching operations per day. By using the direct-calculation method, an optimal ESS rating can be found for any wind farm with a high degree of accuracy. The method has a considerable advantage over traditional differential-based methods because it does not require knowledge of the analytical form of the objective function. For ESSs optimal rating, the improvement in wind energy integration is between 1.7% and 8%. In addition, a net increase in grid steady-state voltage stability of 8.3%-18.3% is achieved by 13 of the 14 evaluated ESSs. For improving wind farm transient stability, the proposed ESS operation scheme is effective. It exploits the use of a synchronous-machine-based ESS as a synchronous condenser to dynamically supply a wind farm with reactive power during faults. Analysis with an ESS and a 60-MW wind farm consisting of stall-regulated wind turbines shows that the ESS increases the farm critical clearing time (CCT) by 1 cycle for worst-case bolted three-phase-to-ground faults. For bolted single-phase-to-ground faults, the CCT is improved by 23.1%-52.2%.text
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Makgoale, Dineo Mokganyetji. "Effects of mill rotational speed on the batch grinding kinetics of a UG2 platinum ore." Diss., 2019. http://hdl.handle.net/10500/26498.
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In this study, the effect of speed was investigated on the breakage rate of UG2
platinum ore in a batch mill of 5 dm3 and 175 mm internal diameter. One size fraction
method was carried out to perform the experiment. Five mono-sized fractions in the
range of 1.180 mm to 0.212 mm separated by √2 series interval were prepared. The
fractions were milled at different grinding times (0.5, 2, 4, 15 and 30 min) and three
fractions of mill critical speed were considered (20%, 30%, and 40%). The target of
critical speed below 50% was due to the need of lower energy consumption in milling
processes. The selection and breakage function parameters were determined and
compared for fractions of critical speed.
First the grinding kinetics of the ore was determined and it was found that the
material breaks in non-first order manner. Thereafter, effective mean rate of
breakage was determined. It was found that the rate of breakage increased with
increase of mill speed and optimum speed was not reached in the range of chosen
mill speed fractions. Again the rate of breakage was plotted as a function of particle
size, the optimum size was 0.8 mm when milling at 30% critical speed. As for 20% and
30% optimum size was not reached. The selection function parameters estimated at
30% critical speed were 𝑎0 = 0.04 min−1
, 𝛼 = 1.36, 𝜇 = 0.9 mm, and Λ = 3. Breakage
function parameters were determined and was noticed that the material UG2
platinum ore is non-normalised, i.e. Φ value was changing from 0.25 to 0.90
depending on feed size and mill speed. The parameters 𝛽 and 𝛾 were constant at 7.3
and 1.17 respectively.College of Science, Engineering and Technology
M. Tech. (Chemical Engineering)
Books on the topic "CRITICAL MACHINE ENERGY"
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Thompson, William R., and Leila Zakhirova. The Netherlands: Not Quite the First Modern Economy. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699680.003.0006.
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In this chapter, we look at four cases: Genoa, Venice, Portugal, and the Netherlands. Genoa, Venice, and Portugal acted as transitional agents over a five- to six-hundred-year period, creating sea power and trading regimes to move Asian commodities and innovations to and from European markets. While Genoa and Venice were primarily Mediterranean-centric, Portugal led the breakthrough from the constraints of the inland sea and inaugurated Europe’s Atlantic focus. None of these actors possessed the power of China nor subsequent global actors, but for their age, they were critical technological leaders, providing a technological bridge from the eastern zone of Eurasia to the western zone. The Netherlands fits into this narrative by combining Baltic and Atlantic activities to construct a European trade regime that greatly overshadowed the earlier transitional efforts. Buttressed by the development of agrarian and industrial technology and a heavy reliance on peat and wind as energy sources, the Dutch case seems idiosyncratic. Most critically, its energy transition was only partial. Although the Netherlands made clear advances in some power-driven machinery and technological innovation , the heat and energy that were expended remained constrained by the inherent limitations of the energy sources.
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Kubek, Maria M., and Zhong Li, eds. Autonomous Systems 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783186862105.
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To meet the expectations raised by the terms Industry 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in ever-changing environments, their design becomes increasingly critical. Thus, the many topics addressed in this book range from data integration on hardware level to methods for security and safety of data and to stochastic methods, data interferences as well as machine learning and search in decentralised systems. Their validity is proven by extensive simulation results. Also, applications for methods from deep learning and neurocomputing are presented. The sustainable management of energy systems using intelligent methods of self-organisation and learning is dealt with in the second major part of this book. As in these particular settings, the assessment of network vulnerabilities plays a crucial role, respective ...
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Raju, Raghavan, and Irshad H. Chaudry. The host response to hypoxia in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0305.
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The hypoxic response of the host is complex. While the oxygen-sensing intracellular machinery attempts to restore cellular homeostasis by augmenting respiration and blood flow, events such as severe haemorrhage lead to whole body hypoxia and decreased mitochondrial function. Immunological perturbations following severe haemorrhage may result in multiple organ dysfunction and sepsis, while impaired perfusion may lead to microvascular injury and local hypoxia. Trauma-haemorrhage or hypoxic exposure in animals causes a systemic inflammatory response, decreased antigen presentation by peritoneal macrophages, hypoxaemia and initiation of endoplasmic reticulum stress. In response, the protein level of the oxygen-sensing transcription factor, hypoxia inducible factor (HIF)-1 increases; this leads to the regulation of expression of a number of genes resulting in decreased mitochondrial ATP production, but enhanced glycolytic processes, thus shifting the energy balance. In addition, sustained tissue hypoxia leads to increased free radical production and cellular apoptosis. Though the initial host response to hypoxia may be protective, sustained hypoxia becomes detrimental to the tissues and the organism as a whole.
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Thompson, William R., and Leila Zakhirova. Comparing the Four Main Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699680.003.0009.
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No two system leaders were identical in their claims to being the most innovative states in their respective zones, eras, and periods of leadership. Nonetheless, three general categories emerge: maritime commercial leadership, a pushing of agrarian boundaries, and sustained industrial economic growth. Those that made breakthroughs in the latter category, of course, redefined the modern world. Frontiers were critically important in all four cases of system leadership (China, the Netherlands, Britain, and the United States), but not exactly in the same way. Major improvements in transportation/communication facilitated economic growth by making interactions more feasible and less expensive, although the importance of trade varied considerably. Expanding populations were a hallmark of all four cases, even if the scale of increase varied. Population growth and urbanization forced agriculture to become more efficient and provided labor for nonagricultural pursuits. Urban demands stimulated regional specialization, technological innovation, and energy intensification, expanding the size of domestic markets and contributing to scalar increases in production. Just how large those scalar increases were depended on the interactions among technological innovation, power-driven machinery, and energy transition. Yet no single change led automatically to technological leadership. While lead status was never gained by default, it helped to have few rivals. As more serious rivals emerged, technological leaderships became harder to maintain.
Book chapters on the topic "CRITICAL MACHINE ENERGY"
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Rajab, Husam, and Tibor Cinkler. "Enhanced Energy Efficiency and Scalability in Cellular Networks for Massive IoT." In 5G and Beyond, 283–305. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3668-7_13.
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AbstractThe significant expansion of cellular networks has increased their potential to support a wide range of use cases beyond their original purpose of providing broadband access. One such development is using cellular networks to support the Internet of Things (IoT), called Cellular IoT (CIoT). The growth of CIoT is an important trend in the evolution of cellular networks, it leads to broader and more comprehensive ecosystem circumstances. The extensive IoT business evolution is transforming a diverse sector, including health, smart cities, security, and agriculture. Nevertheless, a large scale with very different characteristics and use cases struggle with connectivity challenges due to the unique traffic features of massive IoT and the tremendous density of IoT devices. This study aims to identify the critical obstacles that hinder the widespread deployment of IoT over cellular networks and suggest an innovative algorithm to mitigate them effectively. We discovered that the primary challenges revolve around three specific areas: connection setup, network resource management, and energy consumption. In this regard, we investigate the integration of massive Machine-Type Communication (mMTC) into cellular networks, focusing on the performance of Narrowband IoT (NB-IoT) in supporting mMTC.
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Kubsch, Marcus, Daniela Caballero, and Pablo Uribe. "Once More with Feeling: Emotions in Multimodal Learning Analytics." In The Multimodal Learning Analytics Handbook, 261–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08076-0_11.
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AbstractThe emotions that students experience when engaging in tasks critically influence their performance and many models of learning and competence include assumptions about affective variables and respective emotions. However, while researchers agree about the importance of emotions for learning, it remains challenging to connect momentary affect, i.e., emotions, to learning processes. Advances in automated speech recognition and natural language processing (NLP) allow real time detection of emotions in recorded language. We use NLP and machine learning techniques to automatically extract information about students’ motivational states while engaging in the construction of explanations and investigate how this information can help more accurately predict students’ learning over the course of a 10-week energy unit. Our results show how NLP and ML techniques allow the use of different modalities of the same data in order to better understand individual differences in students’ performances. However, in realistic settings, this task remains far from trivial and requires extensive preprocessing of the data and the results need to be interpreted with care and caution. Thus, future research is needed before these methods can be deployed at scale.
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Dhawale, Chitra A., and Kritika A. Dhawale. "Review on Reliability and Energy-Efficiency Issues in Cloud Computing." In Encyclopedia of Data Science and Machine Learning, 790–802. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch045.
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In the current era of information technology, data is increasing exponentially due to the large number of devices in the network. This leads to the rise of a concept called cloud computing, which becomes the need of time and also results in the rise in popularity of cloud computing. It becomes critical to provide on-demand services that adapt to the needs of the customer. Two important factors that trigger cloud computing systems (CCS) are reliability and energy efficiency. This article provides a comprehensive overview of available strategies for ensuring reliability and energy efficiency in cloud computing, combined approaches, the current status, as well as their trade-offs.
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Ghosh, Abichal, Reddi Kamesh, Siddhartha Moulik, and Anirban Roy. "Industrial Revolution 4.0 With a Focus on Food-Energy-Water Sectors." In Encyclopedia of Data Science and Machine Learning, 2199–210. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch131.
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In this article, the authors explore how sustainable goals can be achieved with artificial intelligence (AI) and machine learning (ML) with a focus on food, energy, and water sectors. This article explores the possibilities of intervention in the above three sectors by exploring the production, processing, storage, and distribution. The challenge lies in the rapid screening of materials, processes, optimized usage of resources, minimization of wastage, and availability of the same to the users. All these challenges can be achieved by AI-ML interventions in the food, energy, and water sectors and can be an expected norm during this Industrial Revolution 4.0 era. From recent reported studies, it is clear that for achieving sustainable goals in the food, energy, and water sectors application of AI-ML methods plays a critical role in modelling, prediction, design, optimization, monitoring, and control of processes in the above sectors. In this article, the authors focus on all these techniques for better understanding and reaching sustainable goals across the three sectors.
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Sharma, Nidhi, and Rajeev Mohan Sharma. "5G." In Advances in Wireless Technologies and Telecommunication, 519–43. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7335-7.ch022.
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The tactile internet works on opportunities, critical services, and skill-set transfer instead of data. The global scenario is how realistically a machine/device is going to communicate with the other machine/device. Machine/device connectivity in IoT architecture relies on scalability, signal simplification, low cost and long-term sensors for energy efficiency, and improved battery lifetime. While 5G designs are guided by increased user networking demands in the field of industrial automation, precision agriculture, and augmented reality, researchers are forced to consider the unison of new technologies instead of incremental additions to the LTE specifications.
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Namitha, Kavitha, and Mr. Srinivas B.L. "A STUDY ON HOST MACHINE OVERLOAD DETECTION ALGORITHM IN CLOUD DATA CENTER." In INFORMATION TECHNOLOGY & BIOINFORMATICS: INTERNATIONAL CONFERENCE ON ADVANCE IT, ENGINEERING AND MANAGEMENT - SACAIM-2022 (VOL 1). REDSHINE India, 2020. http://dx.doi.org/10.25215/8119070682.07.
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A critical step in the motion of virtual machine consolidation (VMs)process is host-overloading detection. A particularly intriguing method of avoiding host overload is to use machine learning to forecast future host demand. Host overflow detection, which attempts to foretell whether or not a physical server will become overloaded by VMs, is a crucial stage when consolidating VMs. Major cloud management difficulties in relation to clouds computing include energy utilisation and service level agreements (SLAs). The utilisation inside cloud computing data centres is rapidly growing to meet the large growth in demand for high-performance computing (HPC), storage, and networking resources for commercial and academic purposes. In this research, we attempt to study the performance of the detection of host overload algorithm in cloud datacenters.
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Verma, Mohak, Sukriti Jaitly, and Jaisakthi S M. "Prediction of Water Portability using Machine Learning Methods." In New Frontiers in Communication and Intelligent Systems, 415–24. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-44.
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Water covers around 3/4th of our planet’s surface and is one of the most significant sources of energy for the continuation of life on the planet. In the wake of rapid urbanization and industrialization, water quality has declined at an alarming rate, leading to the spread of life-threatening illnesses and diseases. The consequences of polluted water are far-reaching, affecting every area of human existence. As a result, effective management of water is critical to ensuring that the water's quality is optimized. When data is evaluated and water quality predictions are made in advance, the consequences of water pollution may be dealt with more effectively. There have been many prior studies that have addressed this problem; nevertheless, there is still more work that needs to be done to improve the efficacy, dependability, accuracy, and usefulness of the existing water quality management methods. The goal of this research is to predict water portability by comparing the accuracy of six different machine learning models on a dataset containing water quality metrics for 3276 different water bodies and 10 features.
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Zakur, Yahya Asmar, Biswadip Basu Mallik, Yousif Asmar Zakoor, and Digvijay Pandey. "Survey on the Artificial Intelligence and Machine Learning Techniques on the Applications of Wastewater Treatment for Sustainable Environment." In Handbook of Research on Safe Disposal Methods of Municipal Solid Wastes for a Sustainable Environment, 241–48. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8117-2.ch017.
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Critical water and wastewater treatment applications have been optimized, modelled, and automated using artificial intelligence (AI) techniques and machine-learning models. Also, it describes the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Wastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of samplings, energy, and cost. The study reviews machine learning, deep learning, and smart technologies used in wastewater treatment for generation, prediction enhancement, and classification tasks, providing a guide for future water resources challenges. These models can be used to make decisions in water resources management and governance, but ethics and future directions need to be addressed and focused.
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Bhuyan, Bikram Pratim. "Artificial Intelligence-Based Approaches in Vehicular Power Energy Application." In Advances in Civil and Industrial Engineering, 200–219. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8816-4.ch012.
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According to government officials, automakers, and academics, vehicular ad hoc networks (VANET) may be an effective tool for improving safety and efficiency on the road. For safety-related information to be disseminated, VANET uses cars and infrastructure nodes to interact with each other. Over the years, interest in vehicular communications has developed and is now acknowledged as a pillar of the intelligent transportation systems (ITSs). Nodes in vehicular networks have a lot of electricity and computational power (storage and processing) as a requirement. Electrification and renewable energy initiatives are relocating workforces. Controlling and regulating power flow from several sources and converters to various vehicle loads is critical in electric vehicle technology (EVT) and VANET. In this chapter, the authors put forward an extensive study over the power controllers and the use of artificial intelligence and machine learning in this field. Neural network systems for power optimization are explored. Intelligent power management systems developed are also a part of the focus.
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Mohammad, Ashraf, Jieh-Ren Chang, and Tien-Tai Chang. "Machine Learning Algorithm for Sorting of Battery Packs at Smart Manufacturing Industries." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220716.
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Energy conversion, high efficiency, and longer product life batteries are required to allow the strong enabling and efficient integration of battery technology into our society. Lithium-ion cells are used for the formation of battery packs due to their good characteristics provided for a long time. In manufacturing industries, battery packs are produced at a massive rate. Pouch cell capacity inside the pack has to be balanced to make the output of the battery stable and efficient. Sorting pouch cells became a critical task to build a battery pack. So, the pouch cells have to be sorted into groups based on their capacity. A methodology was proposed in this paper for sorting pouch cells using the hybrid clustering algorithm. Results were compared on various clustering algorithms. Optimal clustering was selected by analyzing the results of the machine learning model.
Conference papers on the topic "CRITICAL MACHINE ENERGY"
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Weckesser, Tilman, Hjortur Johannsson, and Jacob Ostergaard. "Critical machine cluster identification using the equal area criterion." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7285937.
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Lu, Fang, and Ji-Lai Yu. "Using Critical Machine Couple Equal Area Criterion to Assess Multi-Machine System Stability." In 2009 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2009. http://dx.doi.org/10.1109/appeec.2009.4918955.
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Li, Qingyan, Tao Lin, Shuqin Sun, Song Ke, and Hui Du. "Critical Clearing Time Prediction of Power System Fault Based on Machine Learning." In 2020 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2020. http://dx.doi.org/10.1109/ispec50848.2020.9351275.
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Begli, MohammadReza, Farnaz Derakhshan, and Hadis Karimipour. "A Layered Intrusion Detection System for Critical Infrastructure Using Machine Learning." In 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2019. http://dx.doi.org/10.1109/sege.2019.8859950.
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Moraliyage, Harsha, Dilantha Haputhanthri, Chamod Samarajeewa, Nishan Mills, Daswin De Silva, Milos Manic, and Andrew Jennings. "Automated Machine Learning in Critical Energy Infrastructure for Net Zero Carbon Emissions." In 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). IEEE, 2023. http://dx.doi.org/10.1109/isie51358.2023.10227985.
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Khan, Jobaidur R. "Energy Crisis From Household Dryer Machine." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-89688.
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Energy is a very critical issue for United States, especially when it is wasted for very trivial reason but in a very big scale. Household dryer machine is used to dry cloths using valuable energy, which could be performed just by using natural heat from day sunlight. Using of dryer machine contributes to two negative impacts in energy world. Firstly, it consumes fuel from world’s fuel reservoir and secondly, it produces heat and contributes to global warming, which could be used for power production. During this ongoing countrywide energy crisis, now it is the time to audit this energy expenditure to find out how much energy is spent for dryer machine. It is also important to find out how this event contributes to the global warming.
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Scheidler, Justin J., Thomas Tallerico, Wesley A. Miller, and William Torres. "Progress Toward the Critical Design of the Superconducting Rotor for NASA's 1.4~MW High-Efficiency Electric Machine." In AIAA Propulsion and Energy 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-4496.
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Song, Ruoyu, Yanglong Lu, Cassandra Telenko, and Yan Wang. "Manufacturing Energy Consumption Estimation Using Machine Learning Approach." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67679.
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Environmental impacts of manufacturing are often significant and influenced by part and process parameters. Energy consumption is one of the most critical factors for the overall environmental impact of manufacturing. To achieve energy reduction, one must estimate the manufacturing energy consumption throughout the design stage. This paper presents an efficient data-driven approach to utilize machine learning to estimate energy consumption of a manufacturing process from a CAD model. The approach enables quick cost estimation with limited knowledge about the exact process parameters. A case study of fused deposition modeling is used to illustrate the feasibility of this framework and test potential regression methods. Lasso and elastic net regressions were compared in this study. The potential application of this framework to other manufacturing processes is also discussed.
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Joung, Byung Gun, Zhongtian Li, and John W. Sutherland. "Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85459.
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Abstract The reliability of manufacturing equipment is critical for ensuring the productivity and energy efficiency of a manufacturing facility. An unexpected machine breakdown may lead to unexpected downtime, disruption of manufacturing schedule, lower production efficiency, higher operation and maintenance cost. The recent development in machine learning and artificial intelligence enables data-driven Predictive Maintenance (PdM) by means of perceiving the dynamics of manufacturing systems and abstracting them into learnable features to provide a better interpretation of machine failures or unplanned downtimes. PdM, often translated to Prognostics and Health Management (PHM), aims to continue the optimal/normal operation of manufacturing systems. Often, vibration is used as a proxy of an early indicator of impending failure. In this study, tri-axial acceleration data collected from the two different machines are utilized. PdM-based strategies for machine condition monitoring and smart scheduling of equipment maintenance using an anomaly scoring model are discussed for two critical elements in a manufacturing system: 1) Chiller 2) Compressor. An anomaly scoring model is developed to extract meaningful information from the vibration data.
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Abdul Shathar, Sevideen, Bala Murugan Ramakrishnan, Shafiulla Abdul Jabbar, and Reem Al Mansoori. "Surge Mapping of Compressors to Enhance Energy Efficiency and Integrity." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207216-ms.
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Abstract ADNOC Gas Processing Ruwais NGL Plant carried out a field surge testing of one of its Centrifugal type Refrigeration compressor units in order to accurately evaluate the real surge points. Centrifugal compressors used in Gas processing plants are critical machineries consuming significant amount of energy. Unavailability of the compressor due to any failure will cause revenue loss and downtime to the plant operators. Often failure of the compressor system happens due to unstable operation caused by surge. Manufacturers build surge control systems to protect the machinery during the project stages through simulation. However, inaccurate surge map or shifting of surge control lines during plant operation may result in energy losses or machinery damage. Surge test establishes the baseline for machine to help understand future issues better, for machinery protection, safe operation and efficiency. Performing high risk surge testing activity in a safe and successful manner during plant operation and re-mapping resulted in optimal utilization of existing assets without the need for costly upgrades. This innovative technique can lead to impressive improvements and benefits on equipment integrity, performance, energy efficiency, emissions and profitability. This best practice has great potential for transferability across Oil & Gas industry as such compressors and control systems are common across the industry. This paper highlights the methodology of accurate mapping of surge control lines in a safe manner during plant operation to enhance energy efficiency of the machine by reduced gas recycles and to enhance machine integrity by avoiding early surge possibilities.
Reports on the topic "CRITICAL MACHINE ENERGY"
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Minz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598153.bard.
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Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report