CAREER: OPTIMIZATION FORMULATIONS AND ALGORITHMS FOR THE ANALYSIS AND DESIGN OF HIERARCHICAL MODULAR SYSTEMS

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Modularization is a pervasive organization strategy that is used by living, socio-economic, and industrial systems to cope with complexity. Modular industrial systems are built from small-scale, standardized equipment modules, which perform well-defined tasks. Standardization and size reduction enables mass fabrication and fast deployment of equipment, which accelerates experimentation and learning and ultimately leads to technology cost reductions. Modular systems enable staged (sequential) investment strategies, which provide flexibility to mitigate market and regulatory uncertainties. They also facilitate exploitation of highly dispersed resources that are deemed too expensive to centralize. The goal of this CAREER project is to develop optimization formulations and algorithms that facilitate the analysis and design of hierarchical modular systems. These capabilities will be used to design flexible combined power fertilizer systems in rural areas that produce power, ammonia, and urea from distributed resources such as wind energy, natural gas, biomass, and organic waste. Current industrial-scale process systems are highly customized and involve logistically-complex, expensive and lengthy construction phases. Identifying technologies that are suitable for modularization and determining appropriate degrees of enterprise-wide modularity can improve operational flexibility and mitigate financial risk. Large-scale industrial process systems that benefit from the economies of scale can evolve into a hybrid state in which certain functions will be performed in small modular systems that increase flexibility. To model these systems the use of hierarchical graph abstractions is proposed to provide a natural framework for analysis and optimization of the benefits of modularity. Graph abstractions enable the use of techniques to properly organize process equipment units into tightly integrated modules and can be applied recursively at higher levels where modules represent subsystems, entire production facilities, and local/regional/global supply chain hubs. Hierarchical graph structures will be exploited by combinatorial optimization and multi-stage stochastic programming techniques to derive scalable design and investment strategies that mitigate markets and regulatory risk. The educational part of this project aims to incorporate new hierarchical decision-making concepts into the engineering curriculum, make the curriculum itself more modular and develop software tools that enable the design of complex hierarchical systems using crowd-sourcing. Planned outreach activities will provide K-12 students from schools with high enrollment of underrepresented minorities with opportunities to learn about the benefits of modular decision-making and motivate them to pursue career paths in STEM fields. 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.