CAREER: Machine-Learning Assisted Process Systems Engineering: Hybrid modeling for process optimization, design and synthesis

过程系统工程（PSE）通过使用基于物理的数学模型和计算机算法来设计、优化和控制复杂系统，在历史上取得了进步。数据科学和人工智能广泛领域的最新进展导致了机器学习（ML）工具开发的一系列突破，这些工具可用于从大型数据集中导出数学模型。然而，基于机器学习的模型在深入了解系统行为的物理根源方面可能受到限制，并且可能导致在用于创建模型的原始数据集范围之外的糟糕预测。这个CAREER项目旨在开发混合建模方法，保留我们已经知道的系统行为，使数据驱动的ML模型更加可靠，从而实现更准确的医疗诊断、更智能的自动驾驶汽车和更安全的化工厂。课程开发活动旨在将数据科学概念引入化学工程课程和高中统计学课程。拟议的外联活动的目的是增加PSE领域的女工程师人数。所提出的方法旨在开发算法，使现代ML模型（即神经网络和高斯过程模型）能够同时训练具有物理约束的模型（这些约束来自基于第一原理的模型的离散化）。该研究将系统地研究数据的预处理和集成、低维描述性特征空间的识别、ML模型与基于第一性原理的模型的杂交以及混合模型预测的不确定性的量化。具体的研究目标是：(1)从理论上推进训练非参数机器学习模型的数学技术，以满足基于第一性原理的模型预测（混合建模）；(2)在存在噪声和不完整数据集的情况下，对混合模型的不确定性进行量化；(3)嵌入式混合模型设计与综合的混合整数非线性优化问题的算法开发。包括药品、聚合物和化学品生产在内的一系列案例研究将用于开发一个基准库，用于测试各种混合建模架构。生物过程的设计和优化将使用混合模型来研究，将基因水平的控制与宏观尺度的过程优化联系起来。将开发并广泛传播一套适合纳入现有化学工程课程核心课程的混合建模和优化教学模块。建议开展外联活动，目的是通过统计学课程向高中学生介绍数据驱动的决策，并增加PSE领域的性别多样性。一名高中教师将由首席研究员主持，与佐治亚理工学院的科学、数学和计算综合教育中心（CEISMC）和佐治亚实习教师奖学金（GIFT）项目合作，该项目为K-12科学、数学和技术教师提供在工业工作场所和大学实验室的带薪暑期STEM实习。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Process Systems Engineering (PSE) has historically been advanced by using physics-based mathematical models and computer algorithms to design, optimize and control complex systems. Recent advances in the broad field of Data Science and Artificial Intelligence have led to a series of breakthroughs in the development of Machine Learning (ML) tools that can be used to derive mathematical models from large data sets. However, ML-based models can be limited in their ability to give insight into the physical origins of the system’s behavior and can result in poor predictions outside of the range of the original data set used to create the model. This CAREER project aims to develop hybrid modeling approaches that preserve what we already know about system behavior to make data-driven ML models more reliable leading to more accurate medical diagnoses, smarter autonomous vehicles, and safer chemical plants. Curriculum development activities are proposed aimed at introducing data science concepts into Chemical Engineering courses and high school statistics classes. Proposed outreach activities are aimed at increasing the number of female engineers in the field of PSE.The proposed methodology aims at developing algorithms that will enable the simultaneous training of modern ML models (i.e., Neural Networks and Gaussian Process Models) with physical constraints that are derived from discretization of first-principles based models. The proposed research will involve a systematic study of pre-processing and integration of data, identification of low-dimensional descriptive feature spaces, hybridization of ML models with first-principles based models and quantification of the uncertainty of hybrid model predictions. The specific research aims are: (1) Theoretically advancing mathematical techniques for training nonparametric ML models to satisfy first-principles based model predictions (hybrid modeling); (2) Quantification of the uncertainty of hybrid models in the presence of noisy and incomplete data sets; (3) Algorithmic development for mixed-integer nonlinear optimization problems for design and synthesis with embedded hybrid models. A series of case studies that include production of pharmaceuticals, polymers and chemicals will be used to develop a benchmarking library for testing various hybrid modeling architectures. The design and optimization of bioprocesses will be studied using hybrid modeling to connect gene-level control to macroscale process optimization. A set of hybrid modeling and optimization teaching modules, suitable for incorporation within existing core courses of the Chemical Engineering curriculum, will be developed and broadly disseminated. Outreach activities are proposed that are aimed at introducing data-driven decision-making to high-school students through statistics classes and increasing gender diversity in the field of PSE. A high-school teacher will be hosted by the Principal Investigator in collaboration with the Center for Education Integrating Science, Mathematics, and Computing (CEISMC)at Georgia Tech and the Georgia Intern Fellowships for Teachers (GIFT) program, which provides paid summer STEM internships in industry workplaces and University laboratories for K-12 science, mathematics, and technology teachers.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.