کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7005770 | 1455135 | 2018 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust state estimation of feeding-blending systems in continuous pharmaceutical manufacturing
ترجمه فارسی عنوان
برآورد دولت قوی در سیستم های تغذیه-مخلوط در تولید داروهای مداوم
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کلمات کلیدی
تخمین وضعیت افق حرکتی، برآوردگر قوی، تولید دارویی مداوم، سیستم تغذیه تغذیه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تصفیه و جداسازی
چکیده انگلیسی
State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. To improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding-blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemical Engineering Research and Design - Volume 134, June 2018, Pages 140-153
Journal: Chemical Engineering Research and Design - Volume 134, June 2018, Pages 140-153
نویسندگان
Jianfeng Liu, Qinglin Su, Mariana Moreno, Carl Laird, Zoltan Nagy, Gintaras Reklaitis,