Date Approved

12-20-2023

Embargo Period

12-20-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Chemical Engineering

College

Henry M. Rowan College of Engineering

Advisor

Kirti Yenkie, Ph.D.

Committee Member 1

Gerardo J. Ruiz-Mercado, Ph.D.

Committee Member 2

Kauser Jahan, Ph.D., P.E.

Committee Member 3

Joseph F. Stanzione, III, Ph.D.

Committee Member 4

Kevin D. Dahm, Ph.D.

Keywords

Environmental Impact Assessment; Machine Learning; MINLP; Optimization; Process Design; Sustainable Design

Subject(s)

Manufacturing processes; Sustainable engineering

Disciplines

Chemical Engineering | Operations Research, Systems Engineering and Industrial Engineering

Abstract

Historically, process design prioritized efficiency and profitability, often overlooking environmental and societal implications. However, given the global challenges like climate change and resource scarcity, there is a growing emphasis on embedding sustainability into process design. Adopting a systems-oriented approach provides a comprehensive view, spanning from raw material acquisition to end-of-life product management. Such an approach not only identifies potential sustainability challenges but ensures that solutions foster both environmental responsibility and economic viability. In this study, a comprehensive framework for designing industrial systems is introduced, aiming to encompass the entire lifecycle impacts of chemical processes. The research initially delves into two end-of-life scenarios: solvent recovery (as a pollution reduction intervention) and wastewater treatment systems (as a pollution control intervention). Employing graph-theoretical methods and multi-objective optimization, a thorough systems analysis which incorporates Ecological footprint and Emergy analysis, coupled with economic assessment is presented. Furthermore, a Machine Learning (ML) model (as a source reduction option) is developed to predict the cradle-to-gate impacts of chemicals. Merging the insights from this ML model with the end-of-life scenarios offers a comprehensive systems strategy, advocating for a sustainability-focused approach during the early stages of process design.

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