**Author details**

Luo Xianxi, Yuan Mingzhe and Wang Hong *Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China* 

Luo Xianxi and Li Yuezhong *East China Institute of Technology, Nanchang, China* 

Luo Xianxi *Graduate School of the Chinese Academy of Sciences, Beijing, China* 

#### Yuan Mingzhe and Wang Hong

*Shenyang Institute of Automation, Guangzhou, Chinese Academy of Science, China* 

#### **Acknowledgement**

268 Energy Efficiency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities

fault data, locate and remove the gross errors and reduce the random errors.

**Table 3.** The Result of Data Rectification

show the effectiveness of the approaches.

Luo Xianxi, Yuan Mingzhe and Wang Hong

*Chinese Academy of Science, Shenyang, China* 

*East China Institute of Technology, Nanchang, China* 

Luo Xianxi and Li Yuezhong

**5. Conclusion** 

**Author details** 

Luo Xianxi

X01 X02 X1 X11 X12 X2 X21 X22 X3

variance 0.0056 0.0056 0.0400 0.0100 0.0100 0.0100 0.0156 0.0156 0.0225 rectified value 15.582 15.592 42.306 21.340 21.566 20.946 26.086 26.236 31.375

In the chapter, three data processing approaches to improve data quality are demonstrated. For the importance of properly controlling the steam system performance normally, The data obtained from EMS should be accurate and reliable. However, the data may be influenced many outer factors. The approaches proposed in the chapter are to detect the

Four main reasons induce the low accuracy of the mass flow rate measurement. Combining the principle of "3σ" and empirical distribution function to determine control limit is proposed for single variable monitoring, and applying PCA to determine the control limits for the multivariate process. With the limits, most of fault data can be identified easily. For the fault data of flow rates, the approach to setup the mathematical model of the steam network and calculate the flow rates is proposed. The simulation and experimental results

Two approaches, MT and MP, to detect the gross errors are demonstrated. Both are preceded by selecting the statistical variables, which follow standard normal distribution,

The constrained least-squares problem applied for present application is discussed. The four assumptions are approximately satisfied when the steam network is normally function and the state is nearly static. The pipe network loss can be considered to add to the constraint equations for more accurate results. The weighed parameter matrix has influence on the results of data reconciliation. To estimate the deviations of the instruments online and apply

and applying hypothesis test. Some notations for the two algorithms are stated.

several instruments with high precision will improve the quality of reconciled data.

*Key Laboratory of Industrial Informatics, Shenyang Institute of Automation,* 

*Graduate School of the Chinese Academy of Sciences, Beijing, China* 

This work is supported by the Knowledge Innovation Project of Chinese Academy of Science (No.KGCX2-EW-104-3), National Nature Science Foundation of China (No.61064013) and Natural Science Foundation of Jiangxi Province,China (No. 20114BAB201024).

#### **6. References**

	- [16] Mei, C., H. Su, and J. Chu, *An NT-MT Combined Method for Gross Error Detection and Data Reconciliation.* Chinese Journal of Chemical Engineering, 2006. 14(5): p. 592-596.

**Chapter 13** 

© 2012 Mraihi, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Transport Intensity and Energy Efficiency:** 

Economic growth based on use of non renewable energy constitutes a serious problem because of, especially, negative environmental externalities. Growth of energy consumption and gas emissions are the principal negative impacts of these modes of development. However, the sustainable development requires modes of development which demand few of energy and produce few of pollutant gas. Literature has interested of this problematic in an aggregate or disaggregate contexts. The first is concerning the relationship between economic growth, domestic energy consumption and gas emissions. The second is corresponding to the same relationship but per economic sector. Industry and transport sectors are more studied because of their important link with economic and environmental spheres. They have an important contribution in economic growth but they are responsible

The transport is one of the major activities which consume more energy and produce gas pollutants. Majority of freight and passengers is transported by road mode which is considered an important source of fossil fuels consumption and CO2 emissions. In order to make transport sector more sustainable, some strategies should be elaborated to reduce its energy consumption and gas emissions. In other terms, governments should apply a set of instruments, such as economic, fiscal, regulatory and technological instruments, to control

Before any strategy, it's necessary to evaluate the sustainability degree of transport sector. Sustainable transport literature give us several indicators through them it's possible to measure energy demand and gas pollutants production associated to transport activity. Examples include transport intensity, transport energy intensity, transport energy emission

and reproduction in any medium, provided the original work is properly cited.

driving factors of transport-related energy consumption and gas emissions.

**Analysis of Policy Implications** 

**of Coupling and Decoupling** 

Additional information is available at the end of the chapter

Rafaa Mraihi

http://dx.doi.org/10.5772/50808

of several environmental externalities.

**1. Introduction** 

