• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >The development and application of a multi-objective optimization technique for chemical processes and controller design.
【24h】

The development and application of a multi-objective optimization technique for chemical processes and controller design.

机译：化学过程和控制器设计的多目标优化技术的开发和应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In recent years, the development and application of multi-objective optimization techniques have received increasing attention in the literature. Recent innovations in the fields of systems science, artificial intelligence, and operations research have led to the development of rigorous multi-objective optimization techniques to address the problem of optimizing complex processes in the presence of multiple conflicting objectives.; This thesis is a collection of three papers that focuses on the development and application of a multi-objective optimization strategy for selecting optimum operating conditions for the production of gluconic acid and for determining optimum tuning parameters for PID controllers. The optimization strategy developed is performed in two steps: the approximation of the set of feasible solutions called the Pareto domain using the Dual Population Evolutionary Algorithm and the classification of the domain using the Net Flow Method, which incorporates information on the process provided by an expert. This strategy has been proven to be robust in determining the optimal solution after studying twelve standard test cases, which have been used frequently in the literature, and two engineering problems. In addition, the Pareto domain per se provides very useful information on the quality of the optimal zone.
机译：近年来，多目标优化技术的发展和应用在文献中受到越来越多的关注。系统科学，人工智能和运筹学领域的最新创新导致了严格的多目标优化技术的发展，以解决在存在多个冲突目标的情况下优化复杂过程的问题。本文是三篇论文的集合，着重于多目标优化策略的开发和应用，该策略用于选择生产葡萄糖酸的最佳操作条件并确定PID控制器的最佳调节参数。开发的优化策略分两步执行：使用双种群进化算法逼近一组称为Pareto域的可行解决方案，并使用Net Flow方法对域进行分类，其中结合了专家提供的过程信息。在研究了十二个标准测试用例（这在文献中经常使用）和两个工程问题之后，该策略已被证明在确定最佳解决方案方面具有鲁棒性。另外，帕累托域本身提供了有关最佳区域质量的非常有用的信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号