Handbook of Polymer Reaction Engineering 2005
DOI: 10.1002/9783527619870.ch18
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Removal of Monomers and VOCs from Polymers

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Cited by 9 publications
(14 citation statements)
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“…The resulting model equations are simple enough to be solved online or used in process optimization studies to provide information for process engineers. Roman letters D = diffusivity, m 2 /s E(t) = residence time distribution for CFST F crumb = mass flow rate of EPDM crumb F dif = molar flow rate diffusin [11][12][13]29 g out of the EPDM particles in the tank F in = molar flow rate entering the tank within the crumb F out = molar flow rate in the crumb exiting the tank F pre = molar flow rate flowing into the headspace F crumb = mass flow rate EPDM crumb through the tank F dif,j = molar flow rate of species j flowing out of the EPDM particles into the headspace F pre,j = molar flow rate of species j flowing into the headspace F in,j,i = molar flow rate of species j entering tank i within the crumb F out,j,i = molar Flow rate of species j exiting tank i H = Henry's law constant I j,0 = adjustable factor to tune the inlet concentration of species j in the crumb J = objective function to minimize parameter estimation L = thickness of the sample m = average concentration (g small molecule per kg of polymer free of small molecule) m = average concentration, taking into account the residence time distribution M = fraction of the way for the small molecule/EPDM system to reach equilibrium MW = molecular weight p = adjustable factor to tune the pressure for the three-and fourtank process models p 34 = adjustable factor to tune the pressure in Tanks 3 and 4 for the four-tank process model P = total pressure P j = partial pressure for small molecule j P sat W = saturation pressure of water, calculated using the Antoine equation R = particle radius or effective particle radius, m Dashed lines between the model predictions are used to guide the eye. s mj;i = uncertainty associated with the industrial m j,i data t = time t bi = average time spent in tank i by particles in the bith bin t biL,R = time at the left edge and right edge of the bith bin in tank i t 0 = equivalent time T = temperature x = diffusion distance in an EPDM plaque y = mole fractions in gaseous headspace Y i = adjustable factor to tune the residence time in tank i…”
Section: Discussionmentioning
confidence: 99%
“…The resulting model equations are simple enough to be solved online or used in process optimization studies to provide information for process engineers. Roman letters D = diffusivity, m 2 /s E(t) = residence time distribution for CFST F crumb = mass flow rate of EPDM crumb F dif = molar flow rate diffusin [11][12][13]29 g out of the EPDM particles in the tank F in = molar flow rate entering the tank within the crumb F out = molar flow rate in the crumb exiting the tank F pre = molar flow rate flowing into the headspace F crumb = mass flow rate EPDM crumb through the tank F dif,j = molar flow rate of species j flowing out of the EPDM particles into the headspace F pre,j = molar flow rate of species j flowing into the headspace F in,j,i = molar flow rate of species j entering tank i within the crumb F out,j,i = molar Flow rate of species j exiting tank i H = Henry's law constant I j,0 = adjustable factor to tune the inlet concentration of species j in the crumb J = objective function to minimize parameter estimation L = thickness of the sample m = average concentration (g small molecule per kg of polymer free of small molecule) m = average concentration, taking into account the residence time distribution M = fraction of the way for the small molecule/EPDM system to reach equilibrium MW = molecular weight p = adjustable factor to tune the pressure for the three-and fourtank process models p 34 = adjustable factor to tune the pressure in Tanks 3 and 4 for the four-tank process model P = total pressure P j = partial pressure for small molecule j P sat W = saturation pressure of water, calculated using the Antoine equation R = particle radius or effective particle radius, m Dashed lines between the model predictions are used to guide the eye. s mj;i = uncertainty associated with the industrial m j,i data t = time t bi = average time spent in tank i by particles in the bith bin t biL,R = time at the left edge and right edge of the bith bin in tank i t 0 = equivalent time T = temperature x = diffusion distance in an EPDM plaque y = mole fractions in gaseous headspace Y i = adjustable factor to tune the residence time in tank i…”
Section: Discussionmentioning
confidence: 99%
“…In the devolatilization process, the removal of VOCs is based on the diffusion of VOCs from the polymer to the polymer/vapor interface for their posterior transport out of the devolatilization equipment. Therefore, the diffusion coefficient, the thermodynamic equilibrium, and the interfacial area are crucial parameters that can be tuned in order to implement the technique and achieve good purification results. The devolatilization mechanisms, the parameters influence in terms of purification efficiency and the main equipment will be discussed in detail in the following subsections.…”
Section: Purification Of Polymers In Bulkmentioning
confidence: 99%
“…Furthermore, foam is usually created by reduction of the pressure of the system, as it happens, for example, in flash evaporation. In the case of devolatilization using nonrotating equipment, three successive regimes can be defined for a boiling–foaming mechanism. , The first one, called free boiling, takes place when the ratio between the equilibrium partial pressure of the volatile in equilibrium with the melt and the total pressure of the chamber is high. It is based on the creation and fast growth of the bubbles enhancing the mass transfer by creating a convective mixture.…”
Section: Purification Of Polymers In Bulkmentioning
confidence: 99%
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