Environmentally Conscious Manufacturing: Process Systems Tools for the Development and Management of Environmentally and Economically Conscious Manufacturing

化学加工业（CPI）构成了美国制造业的主要竞争优势之一，但它也是美国工业危险废物总产量的大部分来源。为了保持和提高CPI的全球竞争力，同时确保一个安全、清洁和可持续的环境，废物最小化和处理措施需要在未来化学制造设施的整体设计和运营中充分整合。这项工作的目标是开发和演示基于计算的建模、优化和智能控制方法，这些方法将促进这种集成设计和操作。这项工作是针对关键问题，这是批量推力的特点，PI计划调查模型和优化为基础的工艺设计方法，其中考虑到各种相关问题。在持续过程的推动下，他们将开发工具和方法，以促进联合考虑设计和操作决定以及监测和诊断控制系统。技术工作包括开发高效的方法来制定和解决高度结构化的混合整数线性规划（MILP）系统，控制相关指标适合包含在稳态设计方法中，基于数学规划的大规模模型预测控制（MPC）方法，以及根据层次，定性形状分析形式主义制定控制系统性能监测问题。该项目的主要成果将是在一系列工业基准问题上展示一套原型工具，希望这些工具将激励该行业追求环境可持续的设计和工厂运营。

9528675 Venkatasubramanian Purdue Univ. The chemical processing industry (CPI) constitutes one of the major competitive strengths of the US manufacturing sector, but it is also the source of the of the majority of the total US industrial output of hazardous wastes. In order to maintain and enhance the global competitiveness of the CPI while ensuring a safe, clean, and sustainable environment, waste minimization and processing measures need to be fully integrated within the overall design and operation of future chemical manufacturing facilities. The goal of this work is to develop and demonstrate computing based modeling, optimization and intelligent control methodologies which will facilitate such integrated designs and operations. The work is aimed at the key issues which are characteristic of the batch thrust, the PI's plan to investigate model and optimization based process design methodology which takes into account a variety of relevant issues. Under the continuous process thrust, they will develop tools and approaches which facilitate joint consideration of design and operational decisions and the monitoring and diagnosis of control systems. The technical work includes development of efficient methods for the formulation and solution of highly structured mixed integer linear programming (MILP) systems, control relevant metrics suitable for inclusion into steady state design methodology, mathematical programming based approaches to large scale model predictive control (MPC), and the formulation of control systems performance monitoring problems in terms of a hierarchical, qualitative shape analysis formalism. The key outcomes of this project will be a set of prototype tools demonstrated on a set of industrial benchmark problems which, it is hoped, will energize the industry to pursue environmentally sustainable designs and plant operations.