کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
13433314 1842788 2019 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimal Antibody Purification Strategies Using Data-Driven Models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Optimal Antibody Purification Strategies Using Data-Driven Models
چکیده انگلیسی
This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering - Volume 5, Issue 6, December 2019, Pages 1077-1092
نویسندگان
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