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Online since: July 2012
Authors: Lin Yao, Li Li Yu
Adsorption equilibrium data were analyzed by Langmuir and Freundlich adsorption isotherms.
Fig. 1 Adsorption isotherms of Cu(II), Cr(III), Cd(II) and Pb(II) on CSM Table1 Freundlich and Langmuir Parameters of Cu(II), Cr(III), Cd(II) and Pb(II) on CSM Freundlich Langmuir lnQe n R2 Ce/Qe R2 Pb -0.0570+1.1469lnCe 0.8719 0.9814 1.6135-0.7373Ce 0.3058 Cd -0.0956+0.9423lnCe 1.0612 0.9818 1.0372-0.42711Ce 0.3341 Cr 0.2351+0.6386lnCe 1.5659 0.9409 0.1565+0.7643Ce 0.9624 Cu 0.2184+0.3689lnCe 2.7108 0.8253 0.0538+0.7862Ce 0.9873 It is observed from Table1 that the data of Cd(II) and Pb(II) fits well with the classical Freundlich equation, while the data of Cu(II), Cr(III) fits well with Langmuir equation.
Fig. 3 The Impact Bring by other Ions to CSM adsorption of Cu(II) From Fig.3, a marked reduction could be produced when the concentration of HCl increases from 0mmol/g to 5mmol/g.
Fig. 1 Adsorption isotherms of Cu(II), Cr(III), Cd(II) and Pb(II) on CSM Table1 Freundlich and Langmuir Parameters of Cu(II), Cr(III), Cd(II) and Pb(II) on CSM Freundlich Langmuir lnQe n R2 Ce/Qe R2 Pb -0.0570+1.1469lnCe 0.8719 0.9814 1.6135-0.7373Ce 0.3058 Cd -0.0956+0.9423lnCe 1.0612 0.9818 1.0372-0.42711Ce 0.3341 Cr 0.2351+0.6386lnCe 1.5659 0.9409 0.1565+0.7643Ce 0.9624 Cu 0.2184+0.3689lnCe 2.7108 0.8253 0.0538+0.7862Ce 0.9873 It is observed from Table1 that the data of Cd(II) and Pb(II) fits well with the classical Freundlich equation, while the data of Cu(II), Cr(III) fits well with Langmuir equation.
Fig. 3 The Impact Bring by other Ions to CSM adsorption of Cu(II) From Fig.3, a marked reduction could be produced when the concentration of HCl increases from 0mmol/g to 5mmol/g.
Online since: October 2011
Authors: K.S. Sim, K. Y. Low, C. P. Tso, H. Y. Ting, C.T. Lee
A common rule of thumb is every 10oC reduction in the junction of temperature, will double the life expectancy of a semiconductor.
Fig. 7 shows the flow chart of function to obtain data from position encoder.
Flow chart of function to obtain data from position encoder.
The four speed data is modified by using pointers/indirect addressing mode.
Fig. 7 shows the flow chart of function to obtain data from position encoder.
Flow chart of function to obtain data from position encoder.
The four speed data is modified by using pointers/indirect addressing mode.
Online since: April 2013
Authors: Sulaiman Hasan, Mohd Hilmi Othman, Suriati Rasli
The disadvantages are reduced melt strength and stiffness, and less reduction in viscosity at high shear rates [1].
Then the data was analyzed by analysis of variance (ANOVA) to find which parameters significantly affect the quality characteristic of samples.
For this study, the verification test was carried out by mean of the optimum levels of process parameters the data has showed in Table 4.
Based on verification test as stated in Table 7, the minimum warpage for this research was obtained based from response table data.
Then the data was analyzed by analysis of variance (ANOVA) to find which parameters significantly affect the quality characteristic of samples.
For this study, the verification test was carried out by mean of the optimum levels of process parameters the data has showed in Table 4.
Based on verification test as stated in Table 7, the minimum warpage for this research was obtained based from response table data.
Online since: January 2013
Authors: Peng Gao, En Yu Guo, Juan Huang
The general requirements are as follows: (FMS 8) the hospital formulates and carries out plans, checking, examining and maintaining medical instruments and recording the results of the implementation; (FMS 8.1) the hospital collects the monitoring data of medical equipment management item used for the long-term needs for planning the upgrading of hospital medical equipment; (FMS 8.2) the hospital establishes product/device recall system.
Measuring element seven: Collect monitoring data of medical instrument management plan and record and document.
Measuring element eight: The monitoring data are used for plans and improvements.
Replacement of wearing parts: Replace the components having reached the end of life, with performance reduction or undesirable and replace accessories required periodic replacement according to operation instruction in time.
Measuring element seven: Collect monitoring data of medical instrument management plan and record and document.
Measuring element eight: The monitoring data are used for plans and improvements.
Replacement of wearing parts: Replace the components having reached the end of life, with performance reduction or undesirable and replace accessories required periodic replacement according to operation instruction in time.
Online since: August 2014
Authors: Zhong Zhong, Chi Zhang, Yi Jian Feng, Lu Sun, Lu Qi
Conventional biological phosphorous removal technologies based on activated sludge processes were widely used in industrial and domestic wastewater treatment plants, however the activated sludge processes were mostly focused on reduction of chemical oxygen demand (COD) and biological oxygen demand (BOD), while tertiary wastewater treatment with reasonable phosphorus removal effectiveness was expensive and complicated.
Absorption equilibrium data for 96h of each treatments fit well with both Langmuir equation and Freundlich equation.
The data calculated by the Freundlich equation fitted better than the data calculated by the Langmuir equation.
Absorption equilibrium data for 96h of each treatments fit well with both Langmuir equation and Freundlich equation.
The data calculated by the Freundlich equation fitted better than the data calculated by the Langmuir equation.
Online since: June 2020
Authors: Alifatul Haziqah Abu Hanipah, Zhenren Guo
However, most of these previous studies of natural wetlands [4-7] have focused on pollutant mass removal rates and tend to summarise wetland treatment performance data into average concentration percent reductions, while the kinetics of the removal process have received little attention.
Once all the test results were obtained, data analysis was performed to obtain the degradation coefficient, k, of BOD5 based on the degradation kinetic model (Eq. 1).
QinC0 – QoutC = kCV (2) Q/V (C0 – C) = kC (3) k= Q/V [(C0 / C) – 1] (4) Results and Discussions The measured input BOD5 concentrations (C0), output BOD5 concentrations (C), and the estimated k calculated using the measured data based on Eq. 1 to Eq. 4 are shown in Table 2.
Once all the test results were obtained, data analysis was performed to obtain the degradation coefficient, k, of BOD5 based on the degradation kinetic model (Eq. 1).
QinC0 – QoutC = kCV (2) Q/V (C0 – C) = kC (3) k= Q/V [(C0 / C) – 1] (4) Results and Discussions The measured input BOD5 concentrations (C0), output BOD5 concentrations (C), and the estimated k calculated using the measured data based on Eq. 1 to Eq. 4 are shown in Table 2.
Online since: February 2020
Authors: Ru Xiao, Yu Lei Zheng, Shuang Chen, Jia Hui Wang
Sample
Relative viscosity
Mn(g/mol)
PDI
PA66
FRPA66-1
FRPA66-2
FRPA66-3
FRPA66-4
2.7
2.3
1.9
2.3
1.9
3.3×104
2.4×104
1.9×104
2.4×104
2.0×104
1.4
2.4
3.5
2.4
3.4
Thermal properties
DSC results of PA66 and FRPA66s are shown in Fig. 1, and the corresponding data are summarized in Table 2.
Furthermore, the data of all FRPA66s were comparable, which indicated that flame retardant PA66s could be successfully synthesized by the two synthetic methods.
DCS data of PA66 and FRPA66s.
Meanwhile, inclusion of FR-B led to destruction of the regularity of FRPA66’s macromolecular chains, which resulted in reduction of molecular weight, tensile strength, and thermal stability.
Furthermore, the data of all FRPA66s were comparable, which indicated that flame retardant PA66s could be successfully synthesized by the two synthetic methods.
DCS data of PA66 and FRPA66s.
Meanwhile, inclusion of FR-B led to destruction of the regularity of FRPA66’s macromolecular chains, which resulted in reduction of molecular weight, tensile strength, and thermal stability.
Online since: July 2008
Authors: Kee Nam Hong, Jung Jun Park, Sung Wook Kim, Su Tae Kang
Since the
application of ultra high performance concrete in structures can contribute in reducing and lightening
the structural members owing to its remarkable material performances, noticeable reduction of
construction costs can be realized.
However, the lack of experimental data for quantitative assessment including the load sharing capacity of the fibers together with the absence of relevant methods analyzing the final state of ultra high performance concrete structural members are currently impeding the exploitation of the promising large deformation performance brought by such material.
Accordingly, this study intends to examine experimentally the tensile softening characteristics of ultra high performance concrete and to establish a tension softening model for the accurate estimation of the deformability through FEM analysis based on the corresponding experimental data and results.
The tension softening model expressed in Equations 1 to 3 was then proposed based on the regression analysis of the experimental data
However, the lack of experimental data for quantitative assessment including the load sharing capacity of the fibers together with the absence of relevant methods analyzing the final state of ultra high performance concrete structural members are currently impeding the exploitation of the promising large deformation performance brought by such material.
Accordingly, this study intends to examine experimentally the tensile softening characteristics of ultra high performance concrete and to establish a tension softening model for the accurate estimation of the deformability through FEM analysis based on the corresponding experimental data and results.
The tension softening model expressed in Equations 1 to 3 was then proposed based on the regression analysis of the experimental data
Online since: October 2006
Authors: Fritz Aldinger, S. Wildhack
.-% are obtained for bimodal AlN mixtures,
however, their viscosities at a shear rate of 100 s
-1
are very high (grey data points in Fig. 4), and the
casting procedure is complicated.
The influence on the flow characteristics was monitored (Fig. 3a)), and the viscosity data at shear rates of 100 s-1 and 200 s -1 shows a minimum at 3 wt.-% of Dolapix addition.
Figure 4: Viscosity of bimodal AlN suspensions (2 wt.-% Y2O3) depending on the solid content measured at a shear rate of 100 s-1 Fig. 4 summarizes the viscosity measurements performed, with the grey and black points demonstrating the data obtained before and after optimization of the suspension preparation parameters.
Raising the solid content, the water fraction is decreased which leads to a reduction of the total porosity.
The influence on the flow characteristics was monitored (Fig. 3a)), and the viscosity data at shear rates of 100 s-1 and 200 s -1 shows a minimum at 3 wt.-% of Dolapix addition.
Figure 4: Viscosity of bimodal AlN suspensions (2 wt.-% Y2O3) depending on the solid content measured at a shear rate of 100 s-1 Fig. 4 summarizes the viscosity measurements performed, with the grey and black points demonstrating the data obtained before and after optimization of the suspension preparation parameters.
Raising the solid content, the water fraction is decreased which leads to a reduction of the total porosity.
Online since: April 2010
Authors: Dezső L. Beke
But
fortunately. this data collection and trial to get a relation expressing at least the non-reduced values
of D as the function of T* = T/Ti(0), has already been done in [11].
Thus q* (T* ,p* ) q* (T* p), (13) and using Ti(p) and Ωoi(p) in the reduction of q one has the same universal q* function at a given p* ≠ 0 and p* = 0.
(22) This relation still awaits verification, but after having enough data for impurity diffusion coefficients in binary liquid alloys it will be easily checked and can be even used to predict data for not yet measured impurities.
Thus q* (T* ,p* ) q* (T* p), (13) and using Ti(p) and Ωoi(p) in the reduction of q one has the same universal q* function at a given p* ≠ 0 and p* = 0.
(22) This relation still awaits verification, but after having enough data for impurity diffusion coefficients in binary liquid alloys it will be easily checked and can be even used to predict data for not yet measured impurities.