دانلود رایگان مقاله افزایش سرعت و بهینه سازی عدم قطعیت کروماتوگرافی SMB غیر خطی

Abstract

A parametrized reduced-order model is constructed and employed as a surrogate for the full-order model in optimization and uncertainty quantification of nonlinear simulated moving bed chromatography. The reduced-order model is obtained by the reduced basis method using an efficient error estimation. The complexity of the model is reduced by an empirical interpolation method applied to the nonlinear part of the model. Due to the reduced size and complexity of the surrogate model, the processes of optimization and uncertainty quantification are sped up by a factor of 10.

6. Conclusions

We have explored using parametric ROMs to accelerate optimization and UQ of the nonlinear SMB chromatography. The parametric ROM is constructed using the RB method, and the nonlinear coupled terms are tackled by the EIM. The order and the complexity of the full-order model are both largely reduced, so the resulting ROM is fairly efficient and globally reliable in the entire parameter domain. Using the ROM, the optimization problem considered is effi- ciently solved. The effect on the purity of the products is analyzed under flow rate uncertainty. It is shown that the optimal solution is robust in a wide range of flow rate ratios. The runtime of the UQ is significantly reduced by using the ROM. Note that the SMB model considered is nonlinear, involves a coupling structure, as well as periodic switching, which are all challenging for MOR. We have shown that the output error estimation derived in Zhang et al.(2015b)is applicable to this nontrivial model. It should also be highlighted that the ROM constructed by the proposed parametric MOR method produces much higher speedup than that using the non-parametric MOR method in Li et al.(2014a).