替代品杂交和适应性采样对基于模拟的优化效应
Suryateja Ravutla1, Andrew Bai1, Matthew J Realff1
1Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
概括
优化复杂的过程模拟具有挑战性. 混合替代品和自适应采样提高了稳定性和效率,减少了变化,提高了工艺设计的融合.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
- 优化优化 优化优化
背景情况:
- 过程模拟器对于复杂的建模至关重要,但优化受到高成本,缺乏方程和融合问题的阻碍.
- 代理建模和基于代理的优化提供解决方案,黑子和混合代理是常见的方法.
研究的目的:
- 评估和比较两个主要的优化方法:用确定性解决器固定先验抽样和基于自适应性抽样的优化.
- 系统地评估黑盒与混合替代品对优化性能的影响.
- 分析抽样数量,维度,配方和混合化对解决方案融合,可靠性和CPU效率的影响.
主要方法:
- 使用在固定样本上训练的代用人与适应性采样策略的优化比较.
- 对黑子替代品与采用模型校正架构的混合替代品进行系统评估.
- 在数学基准 (最多十个维度) 和工程案例研究 (提取蒸,吸附) 中进行测试.
主要成果:
- 混合建模提高了替代品的稳定性,降低了解决方案的可变性,尽管优化成本增加了.
- 与固定采样策略相比,自适应采样方法显示出更高的效率和一致性.
- 这项研究量化了采样,维度,配方和杂交对优化结果的影响.
结论:
- 混合替代品在流程模拟优化中提供了更高的可靠性和更低的可变性.
- 适应性采样是一种比固定采样方法更有效和更一致的替代品优化方法.
- 这些发现为优化昂贵和复杂的过程模拟提供了有价值的见解.
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