Date Approved

6-30-2022

Embargo Period

12-28-2022

Document Type

Thesis

Degree Name

M.S. Chemical Engineering

Department

Chemical Engineering

College

Henry M. Rowan College of Engineering

Advisor

Kirti M. Yenkie, Ph.D.

Committee Member 1

Thomas Meadowcroft, Ph.D.

Committee Member 2

Robert Hesketh, Ph.D.

Committee Member 3

Matthew DeNafo, M.S., MBA

Keywords

Machine Learning, Optimization, Infrastructure, Asset Management, Hazardous Waste, Wastewater

Subject(s)

Industrial engineering

Disciplines

Civil and Environmental Engineering | Operations Research, Systems Engineering and Industrial Engineering | Software Engineering

Abstract

Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges with creating sustainable infrastructure is the vast amount of information that needs to be collected and analyzed. Oftentimes, engineers are faced with the time-consuming problem of evaluating the environmental impacts of their infrastructure while trying to meet economic targets. This creates a daunting problem requiring the engineer to have in-depth knowledge of a variety of topics such as environmental metrics, harmful emissions, chemical interactions, and economics. To this end, this work focuses on developing software tools for efficient process development to give engineers and operators additional resources to improve our national infrastructure.

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