Model-Free Quality Optimization Strategy for a Batch Process with Short Cycle Time and Low Operational Cost
Model-Free Quality Optimization Strategy for a Batch Process with Short Cycle Time and Low Operational Cost
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DOI：
10.1021/ie5017199
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发表时间：
2014-10
影响因子：
4.2
通讯作者：
Sheng Zhu;Yi Yang;Bo Yang;Zhijiang Shao;X. Chen
中科院分区：
文献类型：
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作者：
Sheng Zhu;Yi Yang;Bo Yang;Zhijiang Shao;X. Chen
批处理过程是现代工业的一个重要因素。批量工艺的质量优化，以确定关键工艺变量的适当设置是成功生产的关键。传统的质量优化方法是基于模型的优化（MBO），这在很多情况下是一项具有挑战性的任务。模型的准确性可能会下降，特别是当工艺条件经常改变时，这种情况在一些批处理过程中经常发生。针对周期短、运行成本低的批量生产过程，提出了一种系统的无模型优化策略。MFO不是建立一个模型来关联过程及其质量变量之间的关系，而是基于在线实验而不是功能评估来优化过程。将单纯形搜索与多目标优化策略相结合，提出了一种直接搜索算法。提出了一种自适应实验数（AEN）策略来提高搜索效率。
Batch processes are an important factor in modern industries. Quality optimization of batch processes to determine the proper settings of key process variables is critical to successful production. The traditional method for quality optimization is model-based optimization (MBO), which is a challenging task in many cases. The accuracy of the model can deteriorate, particularly when process conditions are frequently changed, a situation that commonly occurs in some batch processes. In this study, a systematic, model-free optimization (MFO) strategy is proposed for a batch process with short cycle time and low operational cost. Instead of developing a model to correlate the relationship between the process and its quality variables, MFO optimizes the process on the basis of online experimentation rather than function evaluation. A direct search algorithm is proposed by integration of simplex search and the MFO strategy. An adaptive experiment number (AEN) strategy is also presented to enhance the search met...