GOALI: Optimal Design and Operation of Reliable Process Systems

空气分离设备将大气分离成其主要气体组分：氮气、氧气、氩气和其他含量较低的气体。 空气分离厂也是许多重要行业的主要来源供应商，例如医院、钢铁米尔斯厂和化学精炼厂。 空气分离厂面临的一个主要问题是由于设备故障而导致产品交付中断的可能性。提高这些工厂和其他相关化学工艺的可靠性的现有技术是使用模拟工具来评估设备冗余、额外库存和预防性维护方面的不同设计方案。考虑到设计方案的数量非常大，显然需要开发基于优化的系统工具来解决这些问题。此外，如果最终目标是设计可持续的过程系统，则必须研究与其他目标的相互作用，例如最低成本，最小环境影响和最大安全性。学术研究小组和工业合作伙伴普莱克斯之间的拟议合作旨在解决如何将可靠性纳入过程系统的优化设计和操作的问题，这是一个在文献中很少受到关注的问题。该研究项目将涉及普莱克斯参与研究生的暑期实习。该项目的基本方法和调查结果正在通过卡内基梅隆大学先进工艺决策中心的网站向石油、化学和工程/软件公司传播。本研究课题以大学生为对象，以高中生为对象，开展以工程学为基础的外展活动。本研究课题的概念是以成本最小化和设备利用率最大化为主要目标的多目标优化框架。 由于可靠性是该项目的主要关注点，因此本研究项目的重点是基于混合整数非线性规划的数学建模，以实现可用性目标的最大化。模型依赖于概率为基础的方法，主要是为了确定最佳水平的过程设备的冗余正在解决的第一。这些模型对于过程系统的综合阶段是有用的。其次，正在考虑发展基于马尔可夫过程的模型。这些模型更通用，非常适合详细的设计和改造，因为它们处理时间相关的概率和库存以及流程（尽管它们在计算上是昂贵的，因为它们明确地与系统的离散状态一起工作）。这些模型的维护调度和备件库存的扩展也被认为是与目标，如环境影响，安全性，灵活性和弹性。拟议的多目标优化框架将允许考虑其他目标，如安全，环境影响，灵活性和弹性。

An air separation plant separates atmospheric air into its primary gas components: nitrogen, oxygen, argon and other less abundant gases. Air separation plants are also primary source providers for a number of important industries, such as hospitals, steel mills, and chemical refineries. A major issue that air separation plants face is the potential for disruption in the deliveries of their products due to equipment breakdown. The current state of the art for increasing reliability of these plants and other related chemical processes is to use simulation tools to assess different design alternatives in terms of equipment redundancy, additional inventory and preventive maintenance. Given the very large number of design alternatives, there is a clear need for developing systematic tools based on optimization that can address these problems. Furthermore, the study of interactions with other objectives, such as minimum cost, minimum environmental impact, and maximum safety, is essential if the ultimate goal is to design sustainable process systems. The proposed collaboration between an academic research group and an industrial partner, Praxair, aims to address the problem of how to incorporate reliability in the optimal design and operation of process systems, a problem that has received little attention in the literature. This research project will involve summer internships at Praxair for participating graduate students. The basic methodologies and findings from this project are being disseminated to petroleum, chemical and engineering/software companies through the website of the Center for Advanced Process Decision-making at Carnegie Mellon. Undergraduate students are involved in this research project, and the research team is pursuing engineering-based outreach activities targeted towards high school students.The concept behind this research project is based on a general multi-objective optimization framework in which the minimization of cost and maximization of availability, defined as the percentage of plant uptime, are included as major objectives. This research project focuses on mathematical modeling based on mixed-integer nonlinear programming for the maximization of the availability objective, since reliability is the major concern in this project. Models that rely on a probability based approach and are mostly aimed at determining the optimal level of redundancy of process equipment are being addressed first. These models are useful for the synthesis stage of a process system. Next, the development of models based on Markov processes are being considered. These models are more general and well suited for detailed designs and retrofits, because they handle time dependent probabilities and inventories and process flows (although they are computationally expensive, since they work explicitly with discrete states of the system). The extension of these models for maintenance scheduling and spare parts inventory is also considered, with objectives such as environmental impact, safety, flexibility and resiliency. The proposed multi-objective optimization framework will allow consideration of other objectives such as safety, environmental impact, flexibility and resiliency.