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In an embodiment, a polymerization process comprises circulating, with a pump, a reaction mixture slurry in a polymerization loop reactor during a polymerization process, detecting a pressure change in the reaction mixture slurry downstream of the pump, generating, by a pressure controller, a takeof

In an embodiment, a polymerization process comprises circulating, with a pump, a reaction mixture slurry in a polymerization loop reactor during a polymerization process, detecting a pressure change in the reaction mixture slurry downstream of the pump, generating, by a pressure controller, a takeoff valve actuation signal for a takeoff valve based on the pressure change, generating, by the pressure controller, a correction to the takeoff valve actuation signal, generating, by the pressure controller, a time delay for the correction, applying the correction to the takeoff valve actuation signal to generate a corrected takeoff valve actuation signal, providing the corrected takeoff valve actuation signal to the takeoff valve after the time delay, and adjusting a position of the takeoff valve in response to providing the corrected takeoff valve actuation signal. The reactor pressure is based on the takeoff valve position.

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1. A method for characterizing a polymerization reactor comprising a plurality of reaction zones, the method comprising: providing measurements of one or more operational parameters within the polymerization reactor, wherein the one or more operational parameters comprises a plurality of production

1. A method for characterizing a polymerization reactor comprising a plurality of reaction zones, the method comprising: providing measurements of one or more operational parameters within the polymerization reactor, wherein the one or more operational parameters comprises a plurality of production variables and corresponding production variable weights;obtaining production data measurements of the plurality of production variables from the plurality of reaction zones within the polymerization reactor during operation of the polymerization reactor;transferring the production data measurements to a probability network, wherein the probability network is a Bayesian network; anddetermining a probability that each production variable of the plurality of production variables has an effect on the operation of the polymerization reactor. 2. The method of claim 1, further comprising: removing one or more production variables of the plurality of production variables to provide a second plurality of production variables, wherein the probability that each production variable removed is below a threshold; and operating the polymerization reactor using the one or more operational parameters including the second plurality of production variables. 3. The method of claim 2, further comprising: updating one or more of the production variable weights of the second plurality of production variables; wherein operating the polymerization reactor using the one or more operational parameters including the second plurality of production variables comprises operating the polymerization reactor using the second plurality of production variables and the updated one or more production variable weights. 4. The method of claim 1, wherein the plurality of production variables comprise two or more of: a pump power, a melt index of a polymer product, or a molecular weight of the polymer product. 5. The method of claim 4, wherein the plurality of production variables further one or more of: a concentration of an olefin in a reaction mixture slurry, a concentration of a catalyst in the reaction mixture slurry, a concentration of an alpha olefin reaction product in the reaction mixture slurry, a composition of the catalyst, a composition of the olefin, a composition of the alpha olefin reaction product, a density of the reaction mixture slurry, a composition of a diluent, a pressure within the polymerization reactor, an average temperature of the reaction mixture slurry, a flowrate of the reaction mixture slurry, a temperature of a coolant inlet in a heat transfer portion of the polymerization reactor, or any combination thereof. 6. The method of claim 1, further comprising: determining one or more transformations of the production data measurements;transferring the one or more transformations of the production data measurements to the probability network; anddetermining the probability that one or more production variables of the plurality of production variables has an effect on the operation of the polymerization reactor based on the one or more transformations of the production data measurements. 7. The method of claim 6, wherein the one or more transformations comprise a time based rate of change of one or more of the production data measurements. 8. The method of claim 1, wherein the polymerization reactor is a loop slurry reactor, a continuous stirred tank reactor, or a plug flow reactor. 9. The method of claim 1, wherein the Bayesian network is defined by a set of the plurality of production variables and a set of probabilistic relationship between the set of the production variables. 10. A method for monitoring the status of a polymerization reactor comprises: measuring one or more operational parameters within a polymerization reactor during a polymerization process;transferring the one or more operational parameters to a probability network, wherein the probability network is developed using historical data for a polymerization system,wherein the historical data comprises data for the one or more operational parameters and corresponding event data,wherein the one or more operational parameters comprise a plurality of production variables, andwherein the probability network is a Bayesian network; anddetermining a probability of a state of the polymerization reactor using the probability network with the one or more operational parameters within the polymerization reactor. 11. The method of claim 10, further comprising: determining one or more transformations of the one or more operational parameters, wherein the probability network is further developed based on the one or more transformations; and transferring the one or more transformations to the probability network, wherein determining the probability of the state comprises using the probability network with the one or more transformations. 12. The method of claim 10, wherein the one or more operational parameters comprise at least one of: a pump power, a melt index of a polymer product, a molecular weight of polymer product, a concentration of an olefin in a reaction mixture slurry, a concentration of a catalyst in the reaction mixture slurry, a concentration of an alpha olefin reaction product in the reaction mixture slurry, a composition of the catalyst, a composition of the olefin, a composition of the alpha olefin reaction product, a density of the reaction mixture slurry, a composition of a diluent, a pressure within the polymerization reactor, an average temperature of the reaction mixture slurry, a flowrate of the reaction mixture slurry, a temperature of a coolant inlet in a heat transfer portion of the polymerization reactor, or any combination thereof. 13. The method of claim 10, wherein the state comprises at least one of: an operational state, an upset condition state, a runaway reactor state, a near plugged state, or a plugged state. 14. The method of claim 10, wherein the Bayesian network is defined by a set of the plurality of production variables and a set of probabilistic relationship between the set of the plurality of production variables. 15. A system for monitoring a polymerization production facility comprising at least one polymerization reactor having a plurality of reaction zones disposed therein, the system comprises: a memory;a processor; anda monitoring application stored in the memory, wherein the monitoring application, when executed on the processor, configures the processor to: obtain production data measurements of a plurality of production variables from the plurality of reaction zones within the at least one polymerization reactor during operation of the polymerization reactor; determine a probability of one or more events within the polymerization reactor using the production data measurements with a probability network, wherein the probability network is a Bayesian network; and output an indication of a probability of the one or more events. 16. The system of claim 15, wherein the monitoring application further configures the processor to: determine one or more control signals based on the production data measurements; and send the one or more control signals to one or more components within the polymerization reactor. 17. The system of claim 16, wherein the monitoring application further configures the processor to: determine a probability that each production variable of the plurality of production variables has an effect on the operation of the polymerization reactor; and remove one or more production variables of the plurality of production variables to provide a second plurality of production variables, wherein the probability that each production variable removed is below a threshold. 18. The system of claim 17, wherein the monitoring application further configures the processor to: determine one or more control signals based on production data measurements for the second plurality of production variables. 19. The system of claim 15, wherein the Bayesian network is defined by a set of the plurality of production variables and a set of probabilistic relationship between the set of the plurality of production variables. 20. The method of claim 10, wherein the at least one polymerization reactor is a loop slurry reactor, a continuous stirred tank reactor, or a plug flow reactor.