To achieve different end-use properties of polymers, an industrial plant must produce several grades of the product through the same process under different operating conditions. As molecular weight distribution (MWD) is a crucial quality index of polymers, grade transition based on MWD is of great importance. Dynamic optimization of the grade transition process using MWD is a challenging task because of its large-scale nature. After analyzing the relationships among state variables during polymerization, a novel method is proposed to conduct the optimal grade transition using dynamic optimization with a small-scale moment model, combined with a steady-state calculation of the MWD. By avoiding expensive computation in dealing with dynamic MWD optimization, this technique greatly reduces the computational complexity of the process optimization. The theoretical equivalence of this simplification is also proved. Finally, an industrial high-density polyethylene slurry process is presented to demonstrate the efficiency and accuracy of the proposed strategy.

Polymers can be used in diverse applications in daily life because of their various microstructures. Molecular weight distribution and chemical composition distribution are the two most important microstructural indices for many copolymers. Monte Carlo simulation is an efficient method to obtain those specific distributions that cannot be easily determined via traditional equation-based methods. However, this method requires long computation time. In this project, a parallel method is proposed for Monte Carlo simulation on a graphics processing unit platform. Both steady state and dynamic state cases are presented to show the accuracy and efficiency of the proposed method. The computation time of the proposed method is greatly decreased by at least 30-fold compared with the time required by CPU platform.

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