Search results

GHG emission was calculated through Intergovernmental Panel on Climate Change (IPCC) calculator 2006 based on secondary data of 2009 for all cities.
Therefore, this paper presents the estimation GHG emission based on secondary data in 2009 by Intergovernmental Panel on Climate Change (IPCC) calculator 2006 and the results were compared between urban and rural area.
The scenarios were set with actual situation based on data in 2009 and future expectation of municipal solid waste management as following : · Actual situation (AS): the total waste is disposed to landfill ( based on data and situation of municipal solid waste in 2009) · Scenario 1 (S1): mixing of management practice with high composting 60% of total waste and less landfill 40% of total waste (except the case study of Siem Reap area, it is assumed that 60% of organic waste is used for composting and the remaining waste is sent to landfill) · Scenario 2 (S2): 50% of each plastic, paper and metal material is reduced at landfill, and the remaining waste from waste reduction at landfill (50%) is sent to landfill in order to remove the non-compostable waste for anaerobic digestion · Scenario 3 (S3): 50% of each plastic, paper and metal material is reduced at landfill while 50% of organic waste is recycled for composting and the remaining waste is sent to landfill · Scenario 4 (S4): Waste
reduction at landfill 50% and the remaining waste is disposed off in landfill with gas recovery 5.
Moreover, for S3, the GHG reductions are 70.55% for Battambang and 82.74% for Siem Reap.

Abstract Neural Networks are powerful data modeling tools that are capable of capturing and representing complex input/output relationships.
Using artificial neural network in approximating of complex data is one of easy way to save time and cost.
Then use the resulting data to train a Multi-Layer Perceptron (MLP) Neural Network which would predict –accurately enough- those quantities throughout the speeds, vibration amplitudes and frequencies, friction factors and reductions body for any given input vector.
Based on these, a complete model of the extrusion was created and validated using data from the finite element simulation.
Fig. 7 Learning diagrams of ANN from FEM data References [1] S.A.A.

Moreover, it allows for achieving global reduction instead of local zones of quiet.
These paths include electronics necessary for signal conditioning and data conversion.
The control performance is evaluated as noise reduction level observed by the room microphones.
Comparison of mean reduction measured by room microphones.
Comparison of mean reduction measured by room microphones.

Based on software GAMBIT, GT-POWER,SYSNOISE,through comprehensively using finite element and boundary element method,simulating and analyzing the acoustic performance for the basic noise reduction unit,establishing the finite element model, comparing the frequency and noise reduction of different volume ratio of cavity and throat tube.Simulation analysis and experimental results show that the resonant structure parameters have a direct effect on its resonant frequency and noise reduction amount. the relationship characteristic between them is that the ratio more bigger,the requency and noise reduction amount more bigger , too.And draw the two relation formulas through calculation.
Table 1 Data for volume ratio unit: [m] Number D d h1 h2 R V1/V2 N14 0.12 0.03 0.05 0.16 0.10 5:1 N15 0.15 0.05 0.111 0.10 0.10 10:1 N16 0.18 0.06 0.133 0.08 0.10 15:1 N17 0.20 0.08 0.16 0.05 0.10 20:1 N18 0.09 0.06 0.03 0.135 0.10 1:2 N19 0.10 0.05 0.04 0.160 0.10 1:1 Through the acoustic finite element analysis , as shown in Fig.3.
Table 2 Relationship for volume rate with frequency and noise reduction Number V1/V2 f(Hz) Noise Q(dB) N14 5:1 130 11.0897 N15 10:1 145 20.8999 N16 15:1 145 43.4252 N17 20:1 180 51.1036 120 130 140 150 160 170 180 190 0 5 10 15 20 25 The volume ratio for cavity and throat tube Frequency（Hz） Fig.4 Relationship for volume rate with frequency 10 15 20 25 30 35 40 45 50 55 0 5 10 15 20 25 Noise reduction（dB） The volume ratio for cavity and throat tube Fig.5 Relationship for volume rate with noise reduction The relationship for Cavity and pipe volume ratio with the resonance frequency ,shown as regression analysis index function Eq.3
The data may be transformation into the follow table,shown as Table 3.
Miniature car noise reduction experiment [J].

Data preprocessing.
For the experimental data having a characteristic of smaller-the-better should be normalized as following: (3) Based on the data sequences after data preprocessing calculated by equation 2 and equation 3, the gray relational coefficient can be defined as the following: (4) where and are reference and comparability sequences respectively; ρ, ranging from 0 to 1, is distinguishing coefficient and 0.5 is generally used [7].
So the data preprocessing was preformed by using equation 2 and equation 3.
Based on the preprocessed data, the gray relational coefficients and grade values for each experiment were calculated by using equation 4.
The preprocessed data and calculated result were presented in Table 2.

Regression Analysis of Q- Value Data for Shale Rock in Tunnels: Implications for Support System Design Mahananda Dutta1,a*, Sanket Tirpude1,b, Sandeep Potnis1,c 1Dr.
- To validate the developed model using case study data. 1.5 Scope of Study The scope of this study is limited to tunnelling in weak shale formations.
The case study involves the collection of data on the rock mass quality, stress conditions, and water inflow.
If this data is not available then SRF have to be estimated from general experience, from what is to be observed in the tunnel or cavern, or on the surface.
(iii) Field validation and calibration: Validate and calibrate the model using field data from actual tunnel projects to ensure its practical applicability.

The as-cast block was cold rolled with different reduction levels up to 98%.
The influence of strain mode on the evolution of the recrystallization textures was discussed based on experimental data and results of crystal plasticity calculations. 2.
The materials A and B were subjected to the thickness reductions of ~88% and 98%, respectively.
The orientation data were post-processed with the MTM-FHM software [3].
To the purpose of obtaining a reliable comparison between deformation and recrystallization textures, all orientation data were post-processed with the MTM-FHM software [3].

Second order and first order kinetic models were used for the fitting of the debromination data of BDE-47.
Results show that the debromination data of BDE-47 by the sunlight, ZVI, ZVI/AC and ZVI/IER in the current study are generally best described by the pseudo first order equation.
Meanwhile, the debromination data of BDE-47 by the ZVI and ZVI/IER can also be described by the pseudo second order equation.
Results show that the debromination data of BDE-47 by the sunlight, ZVI, ZVI/AC and ZVI/IER in the current study are generally best described by the first order equation, which is the pesudo first order equation because of the excessive amount of ZVI used.
Meanwhile, the debromination data of BDE-47 by the ZVI and ZVI/IER can also be described by the pseudo second order equation.

The results show that wavelet envelope spectrum analysis of the acoustic emission signals can be effectively used in fault diagnosis of reduction gearbox.
The reduction gearbox is as an essential mechanical equipment connection and transmission of power general component, and its working conditions affect directly the normal operation of the entire unit.
The reduction gearbox is a complex system which contains gears, drive shafts, bearings and box structure etc.
Then the operating state of the key components of the gearbox is judged by the wavelet envelope spectrum analysis, so as to find the failure of the reduction gearbox .
The discrete inverse wavelet transform formula is: (3) In the formula, is the discrete collecting data of signals; is the discrete sampling point; is the decomposition layer, when it decomposes once, the frequency of the signal will be half reduced; are the impulse responses of the filter, which are used to decompose signals; denotes scale coefficient; denotes wavelet coefficient[4].

This paper introduced TPM implementation in Shandong Lingong (SDLG) with two years of data collection and practice.
The company conducted equipment classification, production plan improvement, failure rate reduction, spare parts management and staff training practices based on MTTR, MTBF and OEE figures.
Data of breakdown and total up time were collected monthly to calculate MTTR and MTBF complying with the formula of Eq. 1 and Eq. 2.
Then our department will prepare all the excavator parts besides the traditional spare parts stock based on the marketing forcast. 3.3 Reduction of failure rate Two factors involved in reduction of failure rate.
Conclusion Data of MTTR, MTBF and OEE were collected and calculated monthly in SDLG, which then make the basement of plan adjustment.