Industrial polymerization plants experience frequent changes of products, driven by end-use properties to meet various market requirements. Efficient grade transition policies are essential to save ...time and materials. In this study, the gas-phase catalytic polymerization is modeled in a fluidized bed reactor by a single-phase model, and dynamic optimization is implemented to determine optimal operating sequences for grade changes. Two optimization formulations, a single-stage and a multi-stage formulation, are introduced and compared. The superiority of the multi-stage formulation is demonstrated owing to a better control on each stage during the transition and a further reduction of off-grade time. Subsequently, an on-line optimal control framework is established by incorporating shrinking horizon nonlinear model predictive control with an expanding horizon weighted least-square estimator for process states and unknown parameters. The results of a case study indicate the designed framework is able to handle process uncertainty, while reducing the transition time.
To satisfy the diverse product quality specifications required by the broad range of polyolefin applications, polymerization plants are forced to operate under frequent grade transition policies. ...Commonly, the optimal solution to this problem is based on the minimization of a suitable objective function defined in terms of the changeover time, product quality specifications, process safety constraints and the amount of off-spec polymer, using dynamic optimization methods. However, considering the great impact that a given control structure configuration can have on the process operability and product quality optimization, the time optimal grade transition problem needs to be solved in parallel with the optimal selection of the closed-loop control pairings between the controlled and manipulated variables. In the present study, a mixed integer dynamic optimization approach is applied to a catalytic gas-phase ethylene-1-butene copolymerization fluidized bed reactor (FBR) to calculate both the “best” closed-loop control configuration and the time optimal grade transition policies. The gPROMS/gOPT computational tools for modelling and dynamic optimization, and the GAMS/CPLEX MILP solver are employed for the solution of the combined optimization problem. Simulation results are presented showing the significant quality and economic benefits that can be achieved through the application of the proposed integrated approach to the optimal grade transition problem for a gas-phase polyolefin FBR.
A nonlinear control system integrating an off-line optimizer and a nonlinear MPC controller is developed to perform optimal grade transition operations at the industrial polyolefin reactors. In this, ...paper, the details of the optimizer are given: The sequential nonlinear programming is performed in the optimizer by employing control vector parameterization method together with sensitivity analysis. The switching times (i.e. times of chaning in the input actions) can be also optimized by new formulation and a modification in trial functions. The simulation result illustrates the capability of the control system with the proposed optimizer.
Industrial polymerization plants experience frequent changes of products, driven by end-use properties to meet various market requirements. Good transition policies are essential to save time and ...materials. In this study, the gas-phase catalytic polymerization is modeled in a fluidized bed reactor by a single-phase model and dynamic optimization is implemented to determine optimal operating sequences for grade changes. Two optimization formulations, a single-stage and a multi-stage formulation, are introduced and compared. The superiority of the multi-stage formulation is concluded owing to better control at each stage during the transition and a further reduction of off-grade time. Subsequently, an on-line optimal control framework is established by incorporating a shrinking horizon nonlinear model predictive control with an expanding horizon weighted least-squares estimator for process states and unknown parameters. The results of a case study indicate the designed framework is able to overcome certain levels of uncertainty, while reducing the transition time.
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