TR2025-106

Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering


    •  Zhang, Q., Avraamidou, S., Paulson, J.A., Thakkar, V., Wang, Z., Chiang, L., Braun, B., Rathi, T., Chakrabarty, A., Sorouifar, F., Tang, W.-T., Guertin, F., Munoz, P., Sampat, A., "Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering", American Control Conference (ACC), July 2025.
      BibTeX TR2025-106 PDF
      • @inproceedings{Zhang2025jul2,
      • author = {Zhang, Qi and Avraamidou, Styliani and Paulson, Joel A. and Thakkar, Vyom and Wang, Zhenyu and Chiang, Leo and Braun, Birgit and Rathi, Tushar and Chakrabarty, Ankush and Sorouifar, Farshud and Tang, Wei-Ting and Guertin, France and Munoz, Paola and Sampat, Apoorva},
      • title = {{Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering}},
      • booktitle = {American Control Conference (ACC)},
      • year = 2025,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2025-106}
      • }
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  • Research Areas:

    Control, Machine Learning, Optimization

Abstract:

This paper provides an overview of recent re- search efforts on the role of Process Systems Engineering (PSE) in advancing sustainability initiatives, particularly in achieving net-zero emissions and carbon neutrality. The paper is organized as a collection of four domains where PSE methodologies contribute to sustainability: (i) carbon monetization and low-carbon supply chains, where optimization and systems modeling help design cost-effective decarbonization strategies; (ii) circular economy and sustainable manufacturing, which leverage system-level optimization to minimize resource consumption and maximize economic viability; (iii) sustain- able land management and ecosystem services, where PSE approaches aid in quantifying trade-offs between land use, emissions, and economic feasibility; and (iv) advanced control technology, particularly in building energy management, where data-driven control strategies enhance energy optimization under significant uncertainty. In addition to reviewing relevant literature, this paper highlights common challenges across these domains and discusses future opportunities for integrating emerging technologies, such as generative AI and mixed-integer programming, into PSE-driven sustainability strategies.