Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Subject: search.filters.subject.polymerization

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    State Estimation Model Based Algorithm for On-line Optimization and Control of Batch Processes
    (1994) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    Batch/semi-batch processes are highly nonlinear and involve complex reaction mechanisms. Model-plant mismatch always exists. The lack of rapid direct or indirect measurements of the properties to be controlled makes the control task difficult. It is the usual practice to follow the prespecified setpoint profiles for process variables for which measurements are available, in order to obtain desired product properties. Modeling error can be the cause of bad performance when optimal profiles computed for the model, are implemented on the actual plant. In this paper, a state estimation model based algorithm is presented for on-line modification of the optimal profile and control with the goal of obtaining the desired properties at the minimum batch time. The effectiveness of the algorithm is demonstrated by its application to bulk polymerization of styrene.
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    Modeling and Control of Continuous Free Radical Polymerization Reactors
    (1991) Kim, Kee J.; Choi, K.Y.; ISR
    Nonlinear dynamics of continuous stirred tank reactors for free radical polymerization of styrene have been studied with complex initiator systems such as a binary mixture of monofunctional initiators and bifunctional initiators. The regions of reactor operating conditions which give rise to steady state multiplicity, multiple Hopf bifurcation points, isolas, period doubling bifurcations leading to period-doubling cascades and homoclinics have been identified. The parametric sensitivity of the reactor has also been investigated during the start-up and the steady state operations. Emphasis of the reactor analysis has been placed on the elucidation of the effect of initiator characteristics on the reactor dynamics and resulting polymer molecular weight properties. It has been observed that the presence of more than one monofunctional initiator or dual initiator functionalities makes the reactor dynamics more complex than a single monofunctional initiator. When the heat transfer coefficient of the reactor wall changes during the polymerization because of viscosity increase, the reactor exhibited simpler dynamic behavior than the case of constant heat transfer coefficient. The presence of reactive impurities such as inhibitors in the feed steams also influenced reactor dynamics significantly, depending on their reactivities and concentrations. A two-time scale extended Kalman filter has been used for on-line estimation and control of polymer molecular weight in continuous and batch polymerization reactors. The effects of model uncertainty and measurement time delay on the filter performance have been investigated through numerical simulations. In the presence of moderate model errors and unknown process disturbances in the continuous polymerization process, the filter showed robust estimation performance in predicting the polymer molecular weight properties when frequent molecular weight measurements are provided. It has been illustrated that the polymer grade change policy can be obtained effectively by using the filter and the steady state process model in the continuous process. The overall filter performance in the batch process was quite similar to that of the continuous process. With relatively large model errors or long measurement time delays, the filter converges slowly. Since the batch processes are operated in finite reaction time, more frequent molecular weight measurements than in continuous processes are required for fast filter convergence.
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    Nonlinear Quadratic Dynamic Matrix Control with State Estimation
    (1991) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    Quadratic Dynamic Matrix Control (QDMC) with state estimation is presented for use with nonlinear process models. This formulation extends Garcia's nonlinear version of QDMC to open- loop unstable nonlinear processes and allows for better disturbance rejection. It also extends Ricker's linear state space formulation with state estimation to nonlinear systems. Stability and better performance is observed when compared to the algorithm without state estimation in rejecting disturbances for processes operating at unstable steady state setpoints, as illustrated with two simple examples. The algorithm requires that only a Quadratic Program be solved on-line. The modest computational requirements make it attractive for industrial implementation. the effectiveness of the approach is demonstrated by its successful application to the temperature control of a semibatch polymerization reactor. A model and related control requirements for this problem were presented at the 1990 AIChE Annual Meeting in a session on "Industrial Challenge Problems in Process Control."