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Energy Reduction of Refined Sugar Drying Process in Rotary Dryer and Conditioning Silo Consolidate Mr.Chamlong Phoosodsoong, Assit.Prof.Dr.Chanoknun Sookkumnerd Energy Engineering Faculty, Khon Kaen University,Thailand Energy Engineering Faculty, Khon Kaen University,Thailand Author Email: Chamlongp@mitrphol.com Advisor Email: chasoo@kku.ac.th Keywords: Conditioning, Rotary dryer, Conditioning silo, Refined sugar, Dehumidifier, Energy saving Abstract.
Therefore, in this research energy reduction of refined sugar drying and conditioning process study of these phenomena was conducted both experimentally and theoretically.
Evaluation of these results showed that the low humidity of fresh air are well suited for applied of refined sugar drying process, giving the perfect performance in which quality of refined sugar impacted can be eliminated and reduction of energy can thereby improve, with the aim of enhancing energy saving.
Conclusion In this paper the research topics were fitting a set of data collected during in February to May 2013 in rotary dryer and conditioning silo in sugar factory located in Thailand.

One of the significant problems is the identification of the material data and the characteristics of the impact of the tools on frictional materials.
This allowed for the reduction of task and for the acceleration calculations.
The adopted data should be verified.
You must specify the data as an increasing function of pressure and overclosure.
Tested models were also differed in the data used to describe the elasto-plastic characteristics.

The results show a systematic variation in the deformation texture with changes in Q (the ratio of the reduction in thickness to reduction in diameter).
Initially, data from each section were averaged to determine the hardness increase towards the heavily deformed end of the tube.
Results and Discussion Texture evolution of pilgered tube The ratio of reduction of wall thickness (RW) to reduction of diameter (RD) is a key characteristic of the pilgering process and is defined as Q.
RW/RD)>1, the reduction in wall thickness dominates.
It can be seen that the Q values for each section of tube vary from less than 1 for small reductions, to slightly greater than 1 as the reduction is increased.

The starting microstructures for the simulations were generated from experimental data taken using electron backscatter pattern analysis.
Although the EBSP data is already discretized in a form suitable for input into a Monte Carlo Potts model, as a result of noise in the experimental data some pre-processing of the data is also necessary.
For starting microstructures with higher cube-fractions ( ≈ 65%) , the evolution more closely matches the experimental data.
In accordance with a large amount of experimental data, the energy of the grain boundaries in the system was defined in each case according to the Read-Shockley equation, with a minimum value set at 0.1 [7]: ( ) ( ) [ ]    −θθ =γ 1 ]ln1[/,1.0max m m θ/θ m m θ>θ θ≤θ : : (3) Less experimental data exists for the variation of boundary mobility with misorientation.
Only a small advantage (Λ=1.02, or cube grain surface energies ≈ 2% lower those of other grains) is necessary to give a noticable cube-texture enhancement to levels comparable with the experimental data.

The alumina/nickel composite powder following reduction in hydrogen atmosphere was sintered using a pulse electric current sintering method.
The powder is soaked in metallic salt solution in vacuum, followed by filtering, calcination, and reduction in hydrogen.
Fig. 3 TEM observation of nanosized nickel particles within γ-alumina after reduction.
The modified relation between the averaged data for the monolithic alumina, as-sintered nanocomposites, and annealed nanocomposites is shown in Fig. 7, where the data for as-sintered nanocomposites sintered at 1550 ºC and the data for annealed nanocomposites annealed for 10 min were omitted because these data were affected by over-annealing conditions.
Table 1 shows the average values of the experimental results of monolithic alumina, as-sintered nanocomposites, and annealed nanocomposites for the alumina/nickel system, where data of the specimens sintered at 1350ºC were selected.

Flue gas denitration includes selective noncatalytic reduction (SNCR) and selective catalytic reduction (SCR) while denitration of combustion process in furnace includes advanced reburn, shade combustion and low oxygen combustion.
Fig.1 Artificial neural network diagram Based on the neural network into bayesian normalization method and LM algorithm,this paper improves the BP neural network, so as to speed up the convergence speed and improve the generalization ability.So it achieves better prediction effect.This paper divided the collected data into the training sample data and validation sample data.
Using the training sample data,this paper trains the best neural network model, and then verifies sample data to validate it. 2 Calculation A 600 MW boiler which is subcritical pressure intermediate a reheat control cycle furnace, single furnace ∏ type half open layout.The width of the furnace is 19558mm,the depth is 16940.5mm,the top level of the furnace is 73000mm,the drum centerline level is 74000mm,the Roof plate beam bottom elevation is 81500mm.
The data is divided into training samples and validation samples,uses the artificial neural network model estiblished in the front to train the training samples, and then use the data in the validation sample to verify the predicted results of the artificial neural network model.
The veryfication data are shown in Table 3.

In order to cancel effects of noise as much as possible some measures have to be taken for data manipulation noise cancellation, such as averaging, inverse filtering and so on.
In order to minimize spurious peaks, which widths are less than 2j samples, the sampled data are made smooth by performing the moving average of the data sequence: [ ] [ ] [ ] [ ]1 1 32 1 +++�= nycnycnycny , 1321 =++ ccc . (5) This is equivalent to operate the Hanning spectral window.
To perform the subtraction, first data are taken from the test object without presence of any target.
Another data are taken with the presence of target.
Let’s consider the model of received discrete noisy signal ][][][ nnynz
+= , Ni ,...,1= . (9) To reconstruct the original data, a wavelet representation is used [4].

When the data is analyzed, the gradient of load-strain is applied.
Equipment for wheelset the axle box measurement and save of data Title of Publication (to be inserted by the publisher) We measured and analyzed the data only in case of leading axle running.
Fig. 14 shows the measuring data according to the running distance.
Data according to the movement Fig. 15.
Speed and derailment coefficient distance The result data was shows speed and derailment coefficient by polynomial regression and gaussian(see Fig. 15) in case of the speed was over 224km/h.

This paper is focusing on the study of SRB growth using pressure sensor to measure the pressure changes in bacterial reactor [6].The whole experiment is divided into two parts, the first part is culture of SRB, and the second part is the data processing and analysis.
Diagram of sensor data acquisition Figure 7.
Temperature Compensation Circuit In the data acquisition module of the pressure sensor, in order to reduce error we take temperature compensation circuit for error.
Analysis of Experimental Results Through the test analysis, we get table 1 after data statistics.
The experimental results are consisting with computer data.

Electric vehicle transmission system using single reduction gear or multi-speed transmission directly affects electric vehicle drivability and economy.
The comparative analysis between single reduction gear and two-speed transmission for electric vehicle was carried out.
In a UDDS driving cycles, the energy consumption is 2.12 kW.h. 3.3 Comparative Performance Analysis of EV with Single Gear and Two Speed Transmission (1) Contrastive analysis of dynamic performance ① Acceleration time By running 0 to 50 km/h acceleration and 0-80 km/h acceleration simulation calculation, acceleration time - speed data are obtained, as shown in table 6.
It played a key role in cost reduction for electric vehicle. ②Maximum speed simulation analysis By applying Maximum speed simulation calculation, Maximum speed curve are obtained.
Table7 Maximum Speed Simulation Analysis Simulation Analysis Unit Single gear Ratio Two-speed Ratio Maximum speed km/h 102.9 107.7 As can be seen from the table, whether using fixed speed ratio transmission system or two speed ratio transmission system, the car's maximum speed is also determined by the maximum speed of the motor. ③Gradability simulation analysis By applying Maximum speed simulation calculation with 20% grade road conditions, data was obtained, as shown in table 8.