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CAREER: An objective reduction framework for sustainable process systems

职业：可持续过程系统的客观减排框架

基本信息

批准号：
2237284
负责人：
William Allman
金额：
$ 55.94万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-15 至 2027-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237284&HistoricalAwards=false
关键词：
CAREER objective reduction framework sustainable

项目摘要

Decision making occurs in all facets of the human experience, and in almost every case requires the consideration of tradeoffs between multiple goals that cannot be fully satisfied simultaneously. For critical chemical manufacturing infrastructure, system design and operation decisions require a balance between, for example, supplying an affordable and reliable stream of products to customers, providing well-paying jobs to the community, maintaining safe performance of all production units, and reducing detrimental environmental impacts. Mathematical tools that allow for the identification of decision-making tradeoffs are essential to ensuring that US industries can meet these varied goals while remaining economically competitive. Unfortunately, existing rigorous methods for doing so do not scale well to problems with many (greater than four) objectives. The proposed research program aims to address this challenge by developing a computational framework that systematically reduces the number of objectives in decision making situations with many criteria by identifying sets of objectives that are correlated, or give similar solutions when considered individually, and grouping them into a single objective. Tools also will be developed that efficiently identify a single decision that provides a high-quality compromise between competing objectives. Through the proposed integrated educational activities, this program also will provide training to the next generation of scientists and engineers in multi-criteria decision-making.The goals of this research are to develop generalizable methods for objective reduction in many objective optimization problems (MaOPs) which preserve maximum tradeoff information and provide orders of magnitude reduction in the required solve time, and to apply these methods to representative decision-making problems in the chemical process industries. In particular, this project aims to develop methods that (1) provide a first of its kind approach for systematically reducing high dimensional MaOPs a priori to solving the problem, (2) apply machine learning methods and develop a stochastic community detection approach for finding objective groupings that work well in use cases with dynamically evolving or uncertain parameters, such as real time operation and strategic planning, and (3) use a novel robust single objective optimization approach for a priori identification of knee points on many objective tradeoff curves. Publicly available software to implement the above methods will be developed and shared with the broader academic and industrial chemical process systems community for use in furthering research and education in this area, as well as improving industrial process outcomes. The methods developed will be tested using timely chemical systems applications, such as in electrified chemical production, green fertilizer production, and chlorine manufacture and distribution. However, the methods developed in this project will be highly generalizable and applicable to other fields, including but not limited to artificial intelligence, finance, medicine, and robotics. Furthermore, the project team will pursue integrated educational activities such as the developing a set of educational modules for multi-criteria decision making for inclusion throughout the undergraduate chemical engineering curriculum, teaching underrepresented K-12 students about multi-criteria decision making through games, and mentoring K-12 students for academic competition programs in STEM areas.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

决策发生在人类经历的各个方面，几乎在每种情况下都需要考虑无法同时完全满足的多个目标之间的权衡。对于关键的化学品制造基础设施，系统设计和运营决策需要在以下方面取得平衡：向客户提供负担得起且可靠的产品流、为社区提供高薪工作、维持所有生产单元的安全性能以及减少有害的环境影响。能够识别决策权衡的数学工具对于确保美国工业能够在保持经济竞争力的同时实现这些不同的目标至关重要。不幸的是，现有的严格方法不能很好地解决具有许多（超过四个）目标的问题。拟议的研究计划旨在通过开发一个计算框架来应对这一挑战，该框架通过识别相关的目标集，或者在单独考虑时给出类似的解决方案，并将它们分组为单个目标，系统地减少具有许多标准的决策情况下的目标数量。还将开发工具来有效地识别单个决策，从而在竞争目标之间提供高质量的折衷方案。通过拟议的综合教育活动，该计划还将为下一代科学家和工程师提供多标准决策方面的培训。这项研究的目标是开发许多目标优化问题（MaOP）中目标减少的通用方法，保留最大的权衡信息并在所需的解决时间中提供数量级的减少，并将这些方法应用于化学过程工业中的代表性决策问题。特别是，该项目旨在开发以下方法：（1）提供首个方法，用于系统地减少先验高维 MaOP 以解决问题，（2）应用机器学习方法并开发随机社区检测方法，以查找在具有动态演变或不确定参数的用例（例如实时操作和战略规划）中运行良好的目标分组，以及（3）使用新颖的鲁棒单目标优化方法来先验识别许多客观权衡曲线上的拐点。将开发用于实施上述方法的公开软件，并与更广泛的学术和工业化学过程系统社区共享，以用于进一步推进该领域的研究和教育，以及改善工业过程成果。所开发的方法将通过及时的化学系统应用进行测试，例如电气化化学生产、绿色肥料生产以及氯的制造和分配。然而，该项目开发的方法将具有高度通用性，适用于其他领域，包括但不限于人工智能、金融、医学和机器人技术。此外，项目团队还将开展综合教育活动，例如开发一套多标准决策教育模块，纳入整个本科化学工程课程，通过游戏向代表性不足的 K-12 学生传授多标准决策知识，以及指导 K-12 学生参加 STEM 领域的学术竞赛项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。