权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Towards Reliable and Efficient Real-Time Optimization of Processing Plants

实现加工厂可靠、高效的实时优化

基本信息

批准号：
271280750
负责人：
Professor Dr.-Ing. Sebastian Engell
金额：
--
依托单位：
Laboratoire dAutomatique
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/271280750?language=en
关键词：
Towards Reliable Efficient Real Time

项目摘要

With increasing global competition, the process industry faces intense pressure to improve production efficiency, product quality and process safety. As a result, real-time optimization (RTO) is used extensively for the operational optimization of the plant (either a single unit or part of a larger plant). RTO is a model-based upper-level control system that repeatedly provides set points to the lower-level control system with the objective to maintain process operation as close as possible to the economic optimum. The RTO system provides a link between high-level planning and scheduling and regulatory control. RTO is usually performed on the basis of a rigorous, nonlinear process model. However, the model will never represent the true behavior of the process exactly, and so the optimization, which typically converges to the model optimum, will not be optimal for the real plant; in addition, the computed operating point may violate the constraints. Several approaches have been developed in the past decade to cope with this problem, which include parameter adaptation, gradient correction (called modifier adaptation) or direct search using only the observed plant behavior. These approaches all have certain drawbacks and limitations, in particular it cannot be guaranteed that the constraints are met at each iteration or convergence is slow. This project investigates ways of modifying and combining different approaches to come up with an improved data- and model-based RTO scheme. The goal is the development of an RTO scheme that implements fast convergence to the true plant optimum through the use of a model of realistic accuracy (i.e. without excessive effort for model building) and measured data.

随着全球竞争的加剧，过程工业面临着提高生产效率、产品质量和过程安全的巨大压力。因此，实时优化（RTO）被广泛用于工厂（单个单元或大型工厂的一部分）的操作优化。RTO是一种基于模型的上层控制系统，它反复向下层控制系统提供设定点，目的是保持过程操作尽可能接近经济最优。RTO系统提供了高级别计划和调度与监管控制之间的联系。RTO通常在严格的非线性过程模型的基础上执行。然而，该模型永远不会准确地表示过程的真实行为，因此通常收敛到模型最优值的优化对于真实的工厂将不是最优的;此外，计算的操作点可能违反约束。在过去的十年中，已经开发了几种方法来科普这个问题，其中包括参数自适应，梯度校正（称为修改器自适应）或直接搜索只使用观察到的植物行为。这些方法都有一定的缺点和局限性，特别是不能保证在每次迭代时满足约束或收敛缓慢。该项目研究了修改和组合不同方法的方法，以提出一种改进的基于数据和模型的RTO方案。我们的目标是一个RTO计划，实现快速收敛到真正的工厂最佳通过使用一个模型的现实精度（即，没有过多的努力模型建设）和测量数据的发展。