ε-SVR-Based Predictive Models of Energy Consumption and Performance for Sintering

2014 ◽  
Vol 548-549 ◽  
pp. 1905-1910
Author(s):  
Jun Kai Wang ◽  
Fei Qiao ◽  
Guo Chen Li ◽  
Xue Chu Zhu
Keyword(s):  
Energy Consumption ◽  
Support Vector ◽  
Chemical Compositions ◽  
Sintering Process ◽  
Iron And Steel ◽  
System A ◽  
Study Results ◽  
And Performance ◽  
Linear Regressions

For realizing energy conservation and burdening optimization of sintering process in iron and steel enterprises, as to the predictive issues of energy consumption and performance indices, the Support Vector Machine for Regression (ε-SVR) was introduced into sintering production system. A general modeling mode was proposed and the predictive model of energy consumption and several performances like chemical compositions was established by history data of sintering. Then, this model was compared with several other methods such as multiple linear regressions, ELM, BPNN and RBFN in a case study. Results show that the ε-SVR method can achieve qualified prediction results rapidly with the best accuracy and time efficiency.

scholarly journals Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

2018 ◽  
Author(s):  
Hadi Abbas ◽  
Youngki Kim ◽  
Jason B. Siegel ◽  
Denise M. Rizzo
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Efficient Operation ◽  
Operating Modes ◽  
Minimum Energy Consumption ◽  
System A ◽  
Trip Time ◽  
Route Information

This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.

A New Model for Energy-Saving in Cast Irons Production Introduced Technology Contribution

2011 ◽  
Vol 71-78 ◽  
pp. 2420-2423
Author(s):  
Dan Huang ◽  
Wu Zhao ◽  
Wei Ping Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Analysis Model ◽  
Cast Irons ◽  
New Model ◽  
Yield Increase ◽  
Study Results ◽  
Technological Level ◽  
Lost Foam

A new model for energy-saving in cast irons production introduced technology contribution has been developed. According to the analysis model, in case of keeping same energy efficiency of device, the higher technological level increases, the easier the R increases; even if keep the same melting and heat treatment devices, significant reduction of production energy consumption would be implemented just depending on the production yield increase. A case study results show that technology measurements which has no direct effect on energy consumption play an important role in energy conservation, where the contribution rates of lost-foam casting and computer technology are 20% and 17%. The technological measurements play an important role in cast irons production which cannot be ignored.

scholarly journals Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1722
Author(s):  
Yu-Sheng Kao ◽  
Kazumitsu Nawata ◽  
Chi-Yo Huang
Keyword(s):  
Energy Consumption ◽  
Empirical Study ◽  
Moving Average ◽  
Primary Energy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Primary Energy Consumption ◽  
Mode Decomposition ◽  
Study Results ◽  
Hybrid Forecasting

Forecasting energy consumption is not easy because of the nonlinear nature of the time series for energy consumptions, which cannot be accurately predicted by traditional forecasting methods. Therefore, a novel hybrid forecasting framework based on the ensemble empirical mode decomposition (EEMD) approach and a combination of individual forecasting models is proposed. The hybrid models include the autoregressive integrated moving average (ARIMA), the support vector regression (SVR), and the genetic algorithm (GA). The integrated framework, the so-called EEMD-ARIMA-GA-SVR, will be used to predict the primary energy consumption of an economy. An empirical study case based on the Taiwanese consumption of energy will be used to verify the feasibility of the proposed forecast framework. According to the empirical study results, the proposed hybrid framework is feasible. Compared with prediction results derived from other forecasting mechanisms, the proposed framework demonstrates better precisions, but such a hybrid system can also be seen as a basis for energy management and policy definition.

A model for measuring the performance of the meat supply chain

2013 ◽  
Vol 115 (8) ◽  
pp. 1090-1111 ◽  
Author(s):  
Faranak Fattahi ◽  
Ali S. Nookabadi ◽  
Mahdi Kadivar
Keyword(s):  
Supply Chain ◽  
Case Study Research ◽  
Meat Industry ◽  
Performance Function ◽  
Content Type ◽  
Delphi Approach ◽  
Study Results ◽  
Overall Performance ◽  
And Performance

PurposeThe purpose of this paper is to analyze the characteristics and performance of the meat supply chain by focusing on developing a model for measuring the meat supply chain's performance in the province of Isfahan, Iran.Design/methodology/approachUsing a combination of literature review, Delphi approach and case study research, the paper examines part of the meat supply chain that consists of three industrial slaughterhouses, two cold rooms, three factories and more than 20 supermarkets and it then presents a framework to assess the performance of the industry in the region.FindingsThe methodology suggests indices for strategic and tactical levels in a meat industry as a case study. The proposed framework for the performance measurement of the chain was applied in strategic and tactical levels in which the ranking of indices are also among the achievements of this study. Results show that there are six main criteria required to measure the meat industry's performance.Originality/valueLiterature shows no record of an integrated measurement system for the entire food supply chain where indicators are combined into a performance function to assess the overall performance of the industry.

Designing and performance analysis of solar tracker system: a case study of Çukurova region

2021 ◽  
pp. 165-184
Author(s):  
Hakan Alici ◽  
Burak Esenboga ◽  
Irfan Oktem ◽  
Tugce Demirdelen ◽  
Mehmet Tumay
Keyword(s):  
Performance Analysis ◽  
System A ◽  
Solar Tracker ◽  
And Performance

Balancing Power And Performance In HPC Clouds

2020 ◽  
Vol 63 (6) ◽  
pp. 880-899
Author(s):  
Lixia Chen ◽  
Jian Li ◽  
Ruhui Ma ◽  
Haibing Guan ◽  
Hans-Arno Jacobsen
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Virtual Machines ◽  
Support Vector ◽  
Frequency Scaling ◽  
Vm Placement ◽  
Placement Problem ◽  
Dynamic Voltage ◽  
And Performance ◽  
Save Energy

Abstract With energy consumption in high-performance computing clouds growing rapidly, energy saving has become an important topic. Virtualization provides opportunities to save energy by enabling one physical machine (PM) to host multiple virtual machines (VMs). Dynamic voltage and frequency scaling (DVFS) is another technology to reduce energy consumption. However, in heterogeneous cloud environments where DVFS may be applied at the chip level or the core level, it is a great challenge to combine these two technologies efficiently. On per-core DVFS servers, cloud managers should carefully determine VM placements to minimize performance interference. On full-chip DVFS servers, cloud managers further face the choice of whether to combine VMs with different characteristics to reduce performance interference or to combine VMs with similar characteristics to take better advantage of DVFS. This paper presents a novel mechanism combining a VM placement algorithm and a frequency scaling method. We formulate this VM placement problem as an integer programming (IP) to find appropriate placement configurations, and we utilize support vector machines to select suitable frequencies. We conduct detailed experiments and simulations, showing that our scheme effectively reduces energy consumption with modest impact on performance. Particularly, the total energy delay product is reduced by up to 60%.

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