CAREER: An objective reduction framework for sustainable process systems

决策发生在人类经验的方方面面，几乎每一种情况下都需要考虑不能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：例如，向客户提供负担得起的可靠产品流，为社区提供高薪工作，维持所有生产单位的安全性能，以及减少对环境的有害影响。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多个(多于四个)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，该框架通过识别相互关联的目标集或在单独考虑时给出类似的解决方案，并将它们分组为一个目标，来系统地减少具有多个标准的决策情况下的目标数量。还将开发工具，有效地确定在相互竞争的目标之间提供高质量折衷的单一决定。通过拟议的综合教育活动，该项目还将为下一代科学家和工程师提供多准则决策方面的培训。本研究的目标是开发许多目标优化问题(MAOP)的目标归约的通用方法，这些方法保留了最大限度的权衡信息，并提供了所需求解时间的数量级减少，并将这些方法应用于化工过程工业中的典型决策问题。具体地说，该项目旨在开发以下方法：(1)提供第一种方法来系统地减少高维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来发现在具有动态变化或不确定参数的用例中工作良好的目标分组，例如实时操作和战略规划；以及(3)使用新的健壮的单目标优化方法来先验识别许多目标权衡曲线上的拐点。实施上述方法的公开可用的软件将被开发出来，并与更广泛的学术和工业化学过程系统社区共享，用于进一步促进这一领域的研究和教育，以及改善工业过程成果。开发的方法将通过适时的化学系统应用进行测试，例如在电气化化学品生产、绿色化肥生产以及氯制造和分销中。然而，该项目中开发的方法将具有高度的通用性，并适用于其他领域，包括但不限于人工智能、金融、医学和机器人。此外，项目团队将继续开展综合教育活动，例如开发一套用于多标准决策的教育模块，将其纳入整个本科化学工程课程，通过游戏向未被充分代表的K-12学生教授多标准决策，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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.