Control of monomer conversion

In the polymerization of BD by Ti-, Co- and Ni-based catalyst systems the polymerization has to be shortstopped at a specific monomer conversion in order to avoid the formation of gel. In contrast, polymerization catalysis by Nd catalysts does not need control of monomer conversion. As gel formation is particularly low with Nd catalysts full monomer conversion can be accomplished [427,428]. 

Kipaiissides et al. [36] have applied suboptimal control to the CSTR emulsion polymerization of vinyl acetate. A mathonatical model was used to develop a simulation of the polymerization process. Verification of the model was done by open-loop bench-scale polymerization. Closed-loop control of monomer conversion via manipulation of both monoma and initiator flow rates was... 

Figure 5.12 Optimizing control of monomer conversion and molecular weight (from [34]).

Tailored polymer resins are frequently required for a given application. Fontoura et al. used NIR spectroscopy for in-line and in situ monitoring and control of monomer conversion and polymer average molecular weight during styrene solution polymerization. Two process control strategies, one based on the optimal control theory and the other on model predictive control, were implemented both theoretically and experimentally [67]. 

FIGURE 6.10 Proposed scheme for control of monomer conversion and Mw in free-radical solution polymerizations. 

The available data from emulsion polymerization systems have been obtained almost exclusively through manual, off-line analysis of monomer conversion, emulsifier concentration, particle size, molecular weight, etc. For batch systems this results in a large expenditure of time in order to sample with sufficient frequency to accurately observe the system kinetics. In continuous systems a large number of samples are required to observe interesting system dynamics such as multiple steady states or limit cycles. In addition, feedback control of any process variable other than temperature or pressure is impossible without specialized on-line sensors. This note describes the initial stages of development of two such sensors, (one for the monitoring of reactor conversion and the other for the continuous measurement of surface tension), and their implementation as part of a computer data acquisition system for the emulsion polymerization of methyl methacrylate. 

The analytical predictor, as well as the other dead-time compensation techniques, requires a mathematical model of the process for implementation. The block diagram of the analytical predictor control strategy, applied to the problem of conversion control in an emulsion polymerization, is illustrated in Figure 2(a). In this application, the current measured values of monomer conversion and initiator feed rate are input into the mathematical model which then calculates the value of conversion T units of time in the future assuming no changes in initiator flow or reactor conditions occur during this time. 

The propagation reaction in free-radical polymerizations is rapid.1 One important feature of the polymerization is that high molecular weight polymer is formed even at very low levels of monomer conversion. Thus, each propagating radical or its progeny lives for well under a minute. To control molecular weights in these polymerizations, the use of chain... 

In a simulation study, Leffew and Deshpande [62] have evaluated the use of a dead-time compensation algorithm in the control of a train of CSTRs for flie emulsion polymerization of vinyl acetate. In this study, monomer conv ion was controlled by manipulating the initiator flow rate. Experiments indicate that there is a period of no response (dead-time) between the time of increase in the flow of initiator and the response of monomer conversion. Dead-time compensation attempts to correct for this dead-time by using a mathematical model of the polymerization system. Reported results indicate that if the reactor is operated at low surfoctant concentration (where oscillations are observed), the control algorithm is incapable of controlling monomer conversion by the manipulation of either initiator flow rate or reactor temperature. The inability of the controller to eliminate oscillations is most probably due to the choice of manipulated variable (initiator flow rate) rather than to the perfotmance of the control algorithm (deadtime conq)ensation). 

The second classification has been recently used in a later review article by Meijer and co-workers. This classification is mainly concerned with the mechanism of supramoiecuiar polymerization, which has been defined as the evolution of Gibbs free energy as a function of monomer conversion to polymer (p) from zero to one (p = 0 1) as the concentration, temperature, or some other environmental parameter is altered. This classification has been extremely effective in describing the vast array of examples of SPs, correlating mechanistic similarities with their covalent counterparts, which are widely understood to be classified mechanistically. In this scheme, the authors clearly identify the most fundamental difference between covalent and SPs as the difference in kinetic versus thermodynamic control. The authors argue that it is from this dramatic difference between covalent polymers and SPs, due to the reversibility of the noncovalent interactions, that SPs derive their special properties. This review did not include, however, SPs made from large macromolecular building blocks. 

An optimal predictive controller was developed and implemented to allow for maximization of monomer conversion and minimization of batch times in a styrene emulsion polymerization reactor, using calorimetric measiuements for observation and manipulation of monomer feed rates for attainment of control objectives [31]. Increase of 13% in monomer conversion and reduction of 28% in batch time were reported. On-line reoptimization of the reference temperature trajectories was performed to allow for removal of heater disturbances in batch bulk MMA polymerizations [64]. Temperature trajectories were manipulated to minimize the batch time, while keeping the final conversion and molecular weight averages at desired levels. A reoptimization procediue was implemented to remove disturbances caused by the presence of unknown amounts of inhibitors in the feed charge [196]. In this case, temperatiue trajectories were manipulated to allow for attainment of specified monomer conversion and molecular weight averages in minimum time. 

The multivariable adaptive control algorithm was applied in simulation for the PFR/CSTR reactor system described above. On-line measurement of monomer conversion (via density) and particle size (via light scattering) were assumed. White noise was added to the model inputs and outputs simulating actuator and sensor errors, respectively. 

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