Surrogate-based optimization of capture chromatography platforms for the improvement of computational efficiency
Surrogate-based optimization of capture chromatography platforms for the improvement of computational efficiency
复制标题
DOI：
10.1016/j.compchemeng.2023.108225
复制
发表时间：
2023-03
影响因子：
4.3
通讯作者：
J. J. Romero-J.;E. Jenkins;S. Husson
中科院分区：
文献类型：
--
作者：
J. J. Romero-J.;E. Jenkins;S. Husson
在这项工作中，我们讨论了代理函数和一个新的优化框架的使用，以创建一个高效和健壮的过程设计计算框架。我们的模型过程是单克隆抗体纯化的捕获层析单元操作，这是生物制药生产的重要步骤。模拟该单元的运行需要求解一个非线性偏微分方程组，计算成本很高。我们实现了代理函数，以减少计算时间，并使框架对工业应用程序更具吸引力。该策略产生了准确的结果，处理时间减少了93%。此外，我们开发了一个新的优化框架，以减少生成优化问题的解决方案所需的模拟次数。我们演示了我们的新框架的性能，该框架使用MATLAB内置工具，通过将其性能与单个优化算法的性能进行比较，以解决整数、连续和混合整数变量问题。
In this work, we discuss the use of surrogate functions and a new optimization framework to create an efficient and robust computational framework for process design. Our model process is the capture chromatography unit operation for monoclonal antibody purification, an important step in biopharmaceutical manufacturing. Simulating this unit operation involves solving a system of non-linear partial differential equations, which can have high computational cost. We implemented surrogate functions to reduce the computational time and make the framework more attractive for industrial applications. This strategy yielded accurate results with a 93% decrease in processing time. Additionally, we developed a new optimization framework to reduce the number of simulations needed to generate a solution to the optimization problem. We demonstrate the performance of our new framework, which uses MATLAB built-in tools, by comparing its performance against individual optimization algorithms for problems with integer, continuous, and mixed-integer variables.