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Design of dynamic experiments for the optimization of batch fermentation processes: The case of penicillin.

机译：优化分批发酵过程的动态实验设计：以青霉素为例。

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摘要

This work aims to investigate the use of a systematic methodology to optimize the operating conditions of batch fermentation processes, presented by Georgakis (Georgakis, 2009). This methodology is a novel model-free technique, as opposed to model-based optimization techniques. The methodology consists of designing certain experiments, obtaining a response surface model, and optimizing the response surface model. This methodology has been called Design of Dynamic Experiments (Georgakis, 2009) and is an extension of the well-studied and widely used classical Design of Experiments technique (Montgomery, 2005) (Box & Draper, 2007). The main difference is that the DoDE methodology allows for the design of experiments in which at least one of the decision variables is a time-varying one. This allows us to explore several substrate feeding strategies, and to determine the optimal one. Two different designs of interest to fed-batch fermentations are studied. One in which the substrate is fed in a systematic fashion throughout the fermentation (centralized), and one in which the fermentation is split into two segments, corresponding to the growth phase and the production phase (decentralized). The results of the two designs are compared. The production of penicillin is used as a case study for this methodology, using a well-established and widely studied model by Bajpai and Reuss (Bajpai and Reuss, 1980). Centralized designs are found to be more efficient than decentralized designs. Using four dynamic subfactors gives the optimal penicillin production when using Centralized designs. Using three dynamic subfactors gives the optimal penicillin production when using Decentralized design. However, the number of experiments required for each optimal design is the same. Centralized Design has the advantage of only needing to add one extra factor to test the significance of adding one more dynamic subfactor, whereas the Decentralized Design needs two extra factors to test the significance of adding one more dynamic subfactor to each phase.
机译：这项工作旨在调查由Georgakis（Georgakis，2009）提出的使用系统方法优化分批发酵过程的操作条件的方法。与基于模型的优化技术相反，该方法是一种新颖的无模型技术。该方法包括设计某些实验，获得响应表面模型以及优化响应表面模型。这种方法被称为动态实验设计（Georgakis，2009），是对经过广泛研究和广泛使用的经典实验设计技术（Montgomery，2005）（Box＆Draper，2007）的扩展。主要区别在于，DoDE方法允许设计实验，其中至少一个决策变量是随时间变化的。这使我们能够探索几种基板进纸策略，并确定最佳的进纸策略。研究了分批补料发酵感兴趣的两种不同设计。一种是在整个发酵过程中以系统方式添加底物（集中式），另一种是将发酵分为两个部分，分别对应于生长期和生产期（分散式）。比较了两种设计的结果。青霉素的生产用作该方法的案例研究，使用的是Bajpai和Reuss建立的，经过广泛研究的模型（Bajpai和Reuss，1980年）。发现集中式设计比分散式设计更有效。使用集中式设计时，使用四个动态子因子可产生最佳的青霉素产量。当使用分散设计时，使用三个动态亚因子可产生最佳的青霉素产量。但是，每个最佳设计所需的实验次数是相同的。集中式设计的优点是只需要添加一个额外的因子来测试添加一个动态子因子的重要性，而分散式设计则需要两个额外的因子来测试向每个阶段添加一个动态子因子的重要性。

著录项

  • 作者

    Afif, Fredy.;

  • 作者单位

    Tufts University.;

  • 授予单位 Tufts University.;
  • 学科 Engineering Chemical.
  • 学位 M.S.
  • 年度 2011
  • 页码 85 p.
  • 总页数 85
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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