ACOMP data

Any detector that has a flow cell can, in principle, be used in ACOMP. Conductivity and pH probes are simple, inexpensive examples of this. In facf these latter probes can often be inserted into the reactor, so that they need not be part of the ACOMP detector train and hence do not require flow cells. In such cases, it can be very valuable to add in situ data from these probes to the ACOMP data for determining other reaction characteristics. 

For example, in situ conductivity probes were used to monitor incorporation of ionic comonomers in aqueous copolymerization reactions [73,74], and to directly monitor counterion condensation during copolymerization [75]. Conductivity probes immersed in flow-coupled mixing chambers were used on ACOMP-extracted streams during organic phase polymerization, and for aqueous copolymerization where the reactor ionic strength was too high for direct conductivity measurements. In situ pH probes have been coupled with ACOMP data to monitor hydrolysis during postpolymerization modifications. 

The dotted line MWD in Figure 12.15 [94] is obtained from the continuous ACOMP data, by binning the data logarithmically. Again, the bimodality is very clear via continuous detection. 

At high starting percentage of VB drift is not significant, but becomes extremely large as starting VB drops below 50%, and begins to lead to the type of blend of copolyelectrolyte and homopolymers PAM below 25% VB. Reactivity ratios were determined from ACOMP data to be r =2. A and... 

Monitor ACOMP system health Monitor and model process using ACOMP data Control process using ACOMP and modeling ... 

Improved product quality and consistency It is a big focus for many manufacturers, especially in specialty polymers where there can be high variance in production lots. Even within a company, different plants can produce the same product with very different end properties which then creates negative feedback from customers. Using feedback control, predictive models, and historical databases, manufacturers will be able to share reaction data between their plants and will be able to build correlations to polymer properties based on process and ACOMP data, thereby creating the most efficient process to manufacture a uniform product. 

Determination of [p] hence requires that of the sample solvent and the total viscosity of the fluid containing the macromolecules 7] be measured. In some analytical techniques, such as SEC and ACOMP, the concentration is usually low enough that to a good approximation [p] = where is measured by combining viscometer and concentration detector data. Importantly, [ )] is a direct measure of the ratio of a polymer s hydrodynamic volume to its molar mass M. 

It is important to emphasize that the development of fiber optics technology is a fundamental cornerstone that allowed for the development of real in-line and in-situ monitoring spectroscopic techniques, as the sampling device can be placed at very harmful environments, while the spectrometer still sits in a process control room. Without the support of fiber optics technology, samples have to be prepared and placed inside the illuminated chambers (as performed in the lab since the nineteenth century) or pumped through sampling windows (as performed in advanced systems intended for process and product development, such as automatic continuous online monitoring of polymerization reactions (ACOMP) [37- 1] in order for spectral data to be obtained. 

Advantages of ACOMP include its versatility as a generalized approach, its ability to make fundamental measurements without recourse to empirical models and calibration, its capacity for providing a data-rich stream of complementary information from multiple independent detectors, yielding multifaceted characteristics of polymerization reactions, and its use of the front end to extract, dilute, and condition a sample stream that allows sensitive detectors to provide reliable data without exposing them to harsh reactor or sample conditions. Disadvantages include the mechanical complexity of the front end, the delay time between a continuous fluid element s extraction from the reactor and downstream measurement by the detector train, and a small but continuous waste stream. ACOMP is more invasive than probes that can be placed at an outside reactor window, but are no more invasive than in situ probes, in that in either case access to the reactor contents is required. 

Obtaining high-quality data with model-free primary quantities allows the richness of the ACOMP results to be used for building chemical, physical, and mechanistic models to any degree of elaboration desired, and for potential full feedback control of reactions. 

The background and principles of ACOMP have been discussed in this chapter, with a special focus on how important polymerization reaction characteristics are obtained from the rich data stream furnished by the ACOMP detector stream. Chapters 12 and 13 give examples of the very wide range of specific applications ACOMP has already been adapted to. Chapter 15 gives perspective on the outlook of transforming ACOMP from laboratory R D instrumentation to a robust platform for monitoring and controlling industrial scale reactions. 

Keeping in mind that polymer characteristics provided by ACOMP M, are determined from direct measurements, whereas NIR furnishes monomer conversion data via empirical calibration, an advantage of the in situ NIR is that it furnishes immediate information on the conversion in the reactor, whereas ACOMP relies on continuous withdrawal and dilution of a small stream of reactor fluid, so that there... 

To illustrate the effect of the CTA on the polymer mass, data from ACOMP and size exclusion chromatography (SEC) experiments are combined in Rgure 12.2 [7]. Thus, MJfi data for reactions in which different amounts of CTA were used, computed based on Equation 12.1, are shown in the top portion of the figure whereas molecular weight distribution (MWD) from SEC experiments on reaction end products are shown in the bottom part. Both methods used show similar trends in the evolution of the polymer mass, that is, the higher the concentration of CTA involved in the reaction, the lower the is. 

The agreement among data computed by various approaches proves the feasibility of ACOMP as a powerful tool to follow reaction kinetics and to offer various options in determining chain transfer parameters. It is hoped that the method will be applicable to a wider range of polymerization reactions. 

A new advance with regard to the instrumentation and methods available for online monitoring of heterogeneous polymerization reactions was made by using ACOMP for monitoring the evolution of multiple characteristics during polymerization. The information-rich data collected simultaneously by multiple detectors provide absolute, model-independent determination of quantities such as conversion, composition, and molar mass distribution and avoid potentially damaging effects of the reactor environment. 

While 5(0 is an important parameter to be monitored, since it allows running the reaction to any degree of conversion desired, there was no model-independent route to obtaining conversion, in contrast to typical ACOMP measurements of polymerization reactions, where model-independent conversion is directly obtained from collected data. The authors proposed the use of a chemically specific detector, such as FTIR, for the direct measurement of conversion in future studies. 

The computed concentrations from either one of the methods described earlier allowed the comonomer conversion, instantaneous composition drift, and copolymer molecular weight to be determined. It is important to point out the unique advantages ACOMP offers by providing continuously collected data that allow more detailed information about reaction kinetics to be obtained. Monitoring the evolution of molecular weight during reactions offered further means to assess deviations from ideal behavior. 

Continuous UV data at 305 nm provided by ACOMP, shown in Figure 12.14 [23], follow the evolution of the TTC during several copolymerization reactions with different initial composition and indicate that the degradation process is slower for higher amount of styrene. Shown in the inset... 

FIGURE 12.14 Continuous UV data at 305 nm from ACOMP foUow the evolution of the TTC during 2-(dimethylamino)ethyl acrylate (DMAEA)/styrene (sty) copolymerization reactions by RAFT with different initial composition and indicate that the degradation process is slower for higher amount of styrene. Shown in the inset to figure are the plotted TTC decomposition rate constants versus styrene %. The rates were from exponential fits used as first-order approximations. Reprinted from Li Z, Serelis AK, Reed WF, Alb AM. Online monitoring of the copolymerization of 2-(dimethylamino(ethyl acrylate with styrene by RAFT deviations from reaction control. Polymer 2010 51 4726-4734. 2010 with permission from Elsevier. 

The ACOMP continuously computed conversion data for experiments at 90 °C with different concentration of Cu Br (0,0.025, and 0.05 equiv vs. Cu Br) made possible to observe the persistent radical effect (PRE) kinetics at the early polymerization stages. 

Direct comparison of kinetic and molecular weight from the continuous nonchromatographic and SEC approaches showed that addition of multidetector SEC enhances data gathering power of ACOMP by bringing valuable complementary information. 

FIGURE 13.3 Raw data for several ACOMP signals during the free radical copolymerization of Am and VB. Instantaneous molar fraction of VB, F versus conversion for batch and two feed rates during VB/AM free radical copolymerization reactions. Reprinted (adapted) with permission from Alb AM, Paril A, CatalgU-Giz H, Giz A, Reed WF. Evolution of composition, molar mass, and conductivity during the free radical copolymerization of polyelectrolytes. J Phys Chem B 2007 111 8560-8566. 2007 American Chemical Society. 

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