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Cell voltage was measured at different amperages by lowering amperage in steps and then plotting the voltage versus current data.
At every stage a waiting time of about 2 minutes was provided for acquisition of considerable amount of data by the system.
Figure 1: Cell voltage data indicating metal rolling with frequency < 0.1 Hz and amplitude 0.5 Figures 2 shows cell voltage and current data plotted with time during current sinking measurement.
Extrapolation of the voltage data to zero current for estimating Ve is plotted in Figure 3.
Theoretical value of counter electromotive force was calculated based on the plant data and given in Tables 2 and 3.

Electrochemical reduction of 2,4-DCBA.
To acquire in situ FTIR data, two hundred interferograms were collected at each potential at a resolution 8 cm-1.
Another reduction peak was observed clearly in the negative-going potential scan at around -0.9 V.
The two reduction peaks were assigned to electrochemical reduction of 2,4-DCBA on Pd/Ti electrode.
On the basis of in situ FTIR data, electrochemical hydrodehalogenation reaction of 2,4-DCBA on Pd/Ti electrode might be represented as a sequence of electron additions and chlorine expulsions.

Ltd. coupled to a PC, and the system was controlled and data recorded using the CorrTest software package.
On the scan towards more negative potentials, a reduction wave is observed at Epc -438 mV.
When and , the equation 2 is approximated to express as (3) There is a linear relationship between the net cathode current density and overpotential, and the ratio of overpotential and net cathode current density is charge transfer resistance Rct, that is (4) According to the test data of cathode polarization curves of graphite felt electrode in electrolyte with soluble lead(II), Fig. 2 showed that the linear relationship between net cathode current density and overpotential.
The oxidation and reduction peak potentials were basically unchanged with the increase of cycle number, and the oxidation and reduction peak potentials were 55 mV and 440 mV, respectively.
(2) The oxidation and reduction peak potentials were basically unchanged with the increase of cycle number, and the oxidation and reduction peak potentials were 55 mV and 440 mV, respectively.

In this paper, we study the energy-saving in optical data center networks.
Traditional methods of energy-saving have been geared towards reduction of energy consumption in the network [2–4].
However, server utilization is quite low in data centers.
In practical applications, VMs in data centers are dynamically changing.
Since new data centers emerge in different geographic locations, interconnecting these data centers in a way that allows seamless VM migration has become a necessity.

In order to achieve the objective of this research, the following steps were taken: (1) Preparation/administration of questionnaires; (2) Data collection for a 5-year period (2002-2006); (3) Identification of performance measures from the information produced; (4) Plotting of Graphs and utility curves of the performance measures.
Data Classification and Analysis Data on production and maintenance activities obtained from 2002-2006 are: (1) Production Output; (2) Operating Time; (3) Downtime; (4) Total Maintenance Cost; (5) Planned Maintenance Hours; (6) Total Maintenance Hours.
The following performance measures were identified on the basis of available data: equipment availability, routine service worked, cost of maintenance hour, maintenance cost component, and cost of reduction.
Computation of Performance Measures The performance measures that were computed on the basis of available data obtained from the manufacturing company are (1) Equipment Availability (EA); (2) Emergency Failure Intensity Ratio (EFIR); (3) Cost of Maintenance Hour (CMH); (4) Routine Service Worked (RSW); (5) Maintenance Cost Component (MCC); (6) Cost of Reduction (COR).
Cost of Reduction (COR): The year 2005 shows a relatively good performance of COR.

Under the constraint of CO2 reduction policy, power utilities trade carbon allowance through carbon market according to its CO2 emissions.
CO2 Reduction Constraint.
Basic Data.
The basic data is as follow [7-10]: There are 8 types of candidate power plants, including thermal power, gas power, nuclear power, large hydroelectric power, small hydroelectric power, wind farm, photovoltaic power and biomass power.
(2) The CO2 reduction policy and the carbon market greatly promote the adjustment of power structure.

Therefore, setting up green, low carbon development concept, focusing on energy-saving and emission reduction,developing the energy consumption carbon footprint research have special practical significance on energy saving and emission reduction and the development of low carbon economy in China.
Research methods and data sources Carbon footprint calculation.
As there is no direct detection carbon emissions data of China at present, most of the studies are based on the energy consumption calculation.
Data source.
In this paper, relevant data derived from " Guangdong Statistical Yearbook ", " China Environment Yearbook ", " China energy statistical yearbook".We selected main fossil fuel (coal, oil, natural gas ) consumption volume data for the carbon footprint calculation of Guangdong Province,and also collected the population, The per capita GDP, total energy consumption per RMB10,000 turnover and the per capita forest land area ( including forest and grassland ) of Guangdong Province in recent years to analyse it.

Solutions responsible for data acquisition for management purposes should constitute integrated system, combining data sources located in the lowest layers of the production system, middle-layer sub-systems responsible for the initial interpretation, data pre-processing and reduction of the amount of data, as well as communication interfaces and industrial database responsible for robust data archiving [4].
MIAS data sources.
The process of data integration can be divided into two phases – the pre-processing (basic data manipulation, such as formal verification of correctness, analog-to-digital conversion, digitization and reduction) and aggregation of information [11].
It allows significant reduction of data amount and facilitate further analysis due to initial interpretation of dispersed information [13].
Data archiving in MIAS.

For instance, data collection and transmission etc may introduce noise.
Therefore in practice we must carry out denoising before analyzing measurement data.
For general 0≤a≤1, The size of data estimated by this method is between soft, hard methods, so it is called soft and hard thresholds compromise methods.
Example analysis This section adopts a set of RTK dynamic deformation monitoring data observed by GPS continuously to carry out denoising experiment analysis.
GPS and GLONASS combined measurement and research on deformation monitoring data processing.

In the dry category, the selective catalytic reduction (SCR) and selective non catalytic reduction (SNCR) were the most widely used [2-3].
This paper presented a new method of “absorption-oxidation-reduction” which used ferrous sulfate solution as absorbent, oxygen as oxidizer and urea as reducer to remove NOX from flue.
This method could provide important reference data for wet denitration, Moreover, it has good application prospects. 2 Experimental Sections 2.1 Experimental apparatus and instruments Experimental apparatus and flow diagram of “absorption–oxidation-reduction” coupling method in Fig.1.
Low-temperature selective catalytic reduction of NO with NH3 over Ce-Mn/TiO2 catalyst [ J].
NO removal by reducing agents and additives in the selective non-catalytic reduction(SNCR) process [J].