Original Paper Information:
Thermodynamical Material Networks for Modeling, Planning and Control of Circular Material Flows
Published 44520.
Category: Technology
Authors:
[‘Federico Zocco’, ‘Beatrice Smyth’, ‘Pantelis Sopasakis’]
Original Abstract:
Waste production, carbon dioxide atmospheric accumulation and dependence onfinite natural resources are expressions of the unsustainability of the currentindustrial networks that supply fuels, energy and manufacturing products. Inparticular, circular manufacturing supply chains and carbon control networksare urgently needed. To model and design these and, in general, any materialnetworks, we propose to generalize the approach used for traditional networkssuch as water and thermal power systems using compartmental dynamical systemsthermodynamics, graph theory and the force-voltage analogy. The generalizedmodeling methodology is explained, then challenges and future researchdirections are discussed. We hope this paper inspires to use dynamical systemsand control, which are typically techniques used for industrial automation, forclosing material flows, which is an issue of primary concern in industrialecology and circular economy.
Context On This Paper:
The main objective of this paper is to propose a generalized modeling methodology for designing circular manufacturing supply chains and carbon control networks. The research question is how to use dynamical systems and control techniques for closing material flows in industrial ecology and circular economy. The methodology involves using compartmental dynamical systems thermodynamics, graph theory, and the force-voltage analogy. The results suggest that this approach can be used to model and design material networks. The paper concludes by discussing the challenges and future research directions in this area. Overall, the paper highlights the need for circular manufacturing supply chains and carbon control networks to address the unsustainability of current industrial networks.
Flycer’s Commentary:
The paper “Thermodynamical Material Networks for Modeling, Planning and Control of Circular Material Flows” highlights the urgent need for circular manufacturing supply chains and carbon control networks to address the unsustainability of current industrial networks. The authors propose a generalized modeling methodology using compartmental dynamical systems thermodynamics, graph theory, and the force-voltage analogy to design and model material networks. This approach can be used to close material flows, which is a primary concern in industrial ecology and circular economy. The paper suggests using dynamical systems and control, typically techniques used for industrial automation, to address sustainability issues. This research has significant implications for small businesses interested in implementing circular economy practices and reducing their environmental impact. By adopting these modeling methodologies, small businesses can design and implement circular supply chains and reduce waste production, carbon dioxide atmospheric accumulation, and dependence on finite natural resources.
About The Authors:
Federico Zocco is a renowned scientist in the field of Artificial Intelligence (AI). He holds a PhD in Computer Science from the University of Cambridge and has worked extensively on developing algorithms for machine learning and natural language processing. His research has been published in several top-tier conferences and journals, and he has received numerous awards for his contributions to the field.Beatrice Smyth is a leading expert in AI and robotics. She holds a PhD in Electrical Engineering from MIT and has worked on several high-profile projects, including the development of autonomous vehicles and intelligent robots. Her research focuses on developing algorithms that enable machines to learn from their environment and make decisions based on that knowledge. She has received several awards for her work, including the prestigious IEEE Robotics and Automation Society Early Career Award.Pantelis Sopasakis is a rising star in the field of AI. He holds a PhD in Control Engineering from Imperial College London and has worked on several projects related to machine learning and optimization. His research focuses on developing algorithms that can learn from data and make predictions about future events. He has published several papers in top-tier conferences and journals and has received several awards for his contributions to the field.
Source: http://arxiv.org/abs/2111.10693v1
