CAREER: Integrating Graph Theory based Networks with Machine Learning for Enhanced Process Synthesis and Design

Process synthesis involves finding the optimal sequence of processing steps for a given chemical manufacturing operation based on predefined metrics such as cost, energy consumption, and environmental impact. Graph theory is a powerful mathematical tool that can enumerate all possible pathways between manufacturing steps using specific rules of connectivity and can produce a ranked list of feasible pathways given case-specific criteria.

However, the top-ranked pathways may still lead to concerns regarding overall process resilience - for example, a wastewater treatment facility faces failure risks due to infrastructure aging or extreme weather events. Thus, resilience, defined as the capability to withstand failures and recover from them, is incorporated as an additional metric. Machine learning algorithms can predict risk and failure probabilities from past operational data, which enables resilience evaluation of each feasible pathway.

This research program will integrate graph theory and machine learning to fundamentally enhance process synthesis algorithms to generate cost-effective, environmentally friendly, and robust solutions that can be easily adapted to other chemical manufacturing operations, such as plastics recycling or solvent recovery in pharmaceuticals manufacturing. In addition to training graduate and undergraduate students, this project will also contribute to the development of open-access educational modules and process systems case studies that feature environmental and social justice themes.

Interactive activities for K-12 STEM outreach and competitions will be conducted for students and teachers from underserved communities in the southern New Jersey region.

A key limitation when designing chemical process networks is that the final design is only as good as the initial structural enumeration (superstructure), a large, complex network of chemical processing path options that must currently be defined by the design engineer. Existing superstructure synthesis methods are largely based on heuristics which can sometimes lead to suboptimality since there is no guarantee that all feasible paths have been captured in the initial enumeration.

In this project, graph theory will be employed to generate an exhaustive enumeration of the overall process network (in this project a wastewater treatment plant network) through well-defined axioms and a maximal structure generation algorithm, wherein the possible process pathways are represented via the connection of materials and process technology nodes with arcs that represent stream flows. This exhaustive enumeration is followed by feasible pathway analysis to generate a ranked list of solution structures based on cost, energy use, environmental impact, and resilience metrics through a novel two-layer process synthesis algorithm that includes combinatorial, linear, and nonlinear model equations and solvers.

The integration of machine learning for regression, classification, and feature importance modeling based on available wastewater treatment plant data and asset inventory will allow for timely prediction of risk and failure probabilities due to aging and extreme events. This enhanced process synthesis methodology will lead to non-intuitive, innovative, cost-effective, sustainable, and robust solutions that will enable stakeholders, such as municipalities and water utility companies, to make informed decisions when designing new facilities or retrofitting existing facilities.

Materials developed as part of this project will include computational algorithms, codes, user manuals, tutorials, technical papers, conference presentations, educational materials, factsheets, and K-12 outreach plans.

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.