Circular Economy (CE); an economy that is restorative and regenerative by design, is currently promoted by several national governments and businesses around the world. This concept has been highly developed by practitioners, the business community, and policymakers, while a scientific basis is clearly needed to effectively navigate the transition towards CE. CE can contribute to all dimensions of sustainable development: environmental, economic, and social, making it an emerging research field in Chemical and Process Systems Engineering.
Research Group Goal: Accelerate the transition towards a Circular Economy.
Approach: Our research lab focuses on expanding the limits of Chemical and Process Systems Engineering by developing tools for the understanding, analysis, and optimization of the interconnected CE supply chains, focusing on the supply chains of chemicals, plastics, and food. Our research considers the transition to renewable energy, CE supply chain modeling, resiliency, and the development of multi-agent modeling & optimization approaches.
The focus of our research group lies on 2 interconnected strands. The first strand focuses on the fundamentals of CE, where we perform research to answer key questions and explore different CE case studies. The second strand focus on the development of modeling and computational tools for i) multi-agent modeling and optimization, and ii) enhancing the resilience of CE supply chains.
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Future Manufacturing of Grid-Connected Chemical Production Technologies
The power sector that controls grid-connected electricity movement and the chemical sector that produces commodity chemicals have become increasingly coupled by a shared interest in decarbonization. Emerging technology that sits at the interface of these sectors will play a critical role in the multi-scale, clean energy transition. Because of this, a wholistic analysis and planning approach that characterizes tradeoffs between environmental impacts and profit from participation in multiple electricity markets will be developed to recommend chemical production technologies and pathways that most sustainably aid in nation-wide decarbonization.
Group members working in this area: Ashley McCullough, Javiera Vergara Zambrano, Parth Brahmbhatt
Collaborators: Prof. Victor Zavala (UW-Madison,CBE), Prof. Song Jin (UW-Madison, Chemistry)
Optimal Supply Chain Design for Circular Economy
With the rising population and increasing demand to meet social needs, Supply Chains demand more and more attention given their significant contributions to global emissions, waterway pollution, and resource depletion. Incorporating circular economy practices throughout the supply chain will reduce the environmental impact and increase resource efficiency. For that reason, we are developing a circular economy (CE) framework based on multi-objective optimization, techno-economic analysis (TEA), and life-cycle assessment (LCA). By considering both economic and environmental impacts in the analysis, our goal is to identify the optimal supply chain design for sustainable infrastructure. We are currently working on the food packaging supply chain considering multiple waste management technologies.
Group members working in this area: Paola Munoz Briones
Collaborators: Prof. Victor Zavala (UW-Madison,CBE), Aurora Munguia-Lopez
Research Papers in the area:
Munoz-Briones, P.; Munguia-Lopez, A.C.; Aguirre-Villegas, H.; Huber, G.W; Zavala, V.; Avraamidou, S. Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy. FOCAPD 2024. [Link]
Advanced Applications of Multiparametric Programming in Process Systems Engineering
Research focuses on leveraging multiparametric programming to enhance various applications within Process Systems Engineering. This includes the integration of ReLU neural network models for explicit model predictive control (MPC), the development of distributed MPC frameworks, and the application of demand response-based scheduling for electrochemical ammonia production. Additionally, efforts are directed towards integrating multiparametric solution spaces into the constraints of large-scale infrastructure optimization problems. The aim is to advance the understanding, analysis, and optimization of interconnected Circular Economy supply chains.
Group Members working in this area: Parth Brahmbhatt
Collaborators: Prof. Victor Zavala (UW-Madison,CBE), Prof. Hari Ganesh (IIT- Gandhinagar)
Optimizing the Transition to Zero Carbon Transportation: A Decision-Making Framework
The urgent need to mitigate the environmental impact of the transportation sector, which accounts for a significant portion of global greenhouse gas emissions, compels the exploration of sustainable fuel alternatives. This research proposes a comprehensive decision-making framework (DMF) integrating techno-economic, environmental, and social aspects to evaluate and compare the viability of various powertrain technologies, including hydrogen (H2), methanol (CH3OH), and ammonia (NH3). A circular economy (CE) impact assessment identifies the most viable fuel production, transportation, and usage pathways. The DMF will be further enhanced by optimization algorithms and mathematical modeling that integrate these CE assessments to create a robust tool for policymakers and industry stakeholders, guiding the transition to zero-carbon transportation.
Group Members working in this area: Kenneth Martinez, Paola Munoz Briones, Riya Suthar
Collaborators: Prof. Marcel Schreier (UW- Madison, CBE)
Research Papers in the area:
Bilevel optimization
Bilevel optimization is a hierarchical problem-solving framework where one optimization problem, referred to as the upper-level problem, is nested within another, the lower-level problem. This approach is extensively utilized in various domains such as planning and scheduling, decision-making, controls, and environmental economics, where decisions at one level impact the feasible solutions and objectives at another. However, bilevel optimization becomes particularly challenging when the lower-level problem includes integer variables and non-linearities. These complexities arise due to the difficulty in solving non-linear integer problems and the nested nature of the optimization, which complicates the solution landscape and increases computational demands. Our research aims to address these challenges by developing warm-up techniques to facilitate data-driven methods and generating effective cuts for analytical methods, ultimately enhancing the efficiency and accuracy of bilevel optimization solutions.
Group Members working in this area: Meng-Lin Tsai
Collaborators: Prof. Brucu Beykal (UConn), Dr Vassilis Charitopoulos (UCL)
Modeling mineral removals for Sustainable Waste Conversion
This research focuses on simulating and modeling systems for the removal of waste biomass and mixed plastic waste, which can be converted into valuable chemical compounds through catalytic processes. However, minerals present in the biomass can poison the catalyst, reducing its efficiency. To address this, we are developing a series of processes to remove these minerals, including torrefaction, washing, and advanced catalytic treatments. Our approach emphasizes the economics, energy efficiency, and sustainability of the entire supply chain, from the initial collection of biomass to the production of chemicals, ensuring a comprehensive evaluation of the environmental and economic impacts.
Group Members working in this area: Meng-Lin Tsai
Collaborators: Prof. George Huber (UW-Madison, CBE), Prof. Reid C. Van Lehn (UW-Madison, CBE)
Urban Energy Infrastructure Planning: A Multi-Scale Optimization Framework for Sustainable Development
Given the complexity and scale of this energy transition, the development of tools for energy infrastructure planning is crucial. This research proposes a framework for the design, planning and evaluation of urban energy systems using a multi-scale and integrated approach, incorporating circular economy metrics and uncertainty analyses. By bridging the gap between short-term operational needs and long-term sustainability goals, this framework aims to optimize the efficiency, resilience, and environmental performance of urban energy systems and will consider interactions and dependencies across different energy system levels. Integrating circular economy metrics will ensure that proposed solutions reduce emissions and promote resource efficiency and sustainability. Lastly, the framework will provide flexible and adaptive solutions to withstand future challenges by addressing uncertainty.
Group Members working in this area: Javiera Vergara Zambrano
Collaborators: Office of Sustainability (UW-Madison)
Research Papers in the area:
Vergara-Zambrano, J.; Avraamidou, S. An optimization-based framework for wind farm layout design considering multi-directional wake effect. FOCAPD 2024.
Optimizing STRAP: Data-Driven Scaleup and Commercialization of Multilayer Plastic Recycling
Multilayer plastics cannot be recycled by existing technologies. Solvent Targeted Recovery And Precipitation (STRAP) is a recycling method that uses solvents to selectively isolate single polymers from a mixed polymer waste stream. We are producing large amounts of experimental data during the scaleup of this technology. We can use this data to build an accurate model that can be used to optimize design and operation of STRAP at a commercial scale.
Group Members working in this area: Charles Granger
Collaborators: Prof. George Huber (UW-Madison, CBE)