Distributed decision making for intensified process systems

Daoutidis, Pródromos; Allman, Andrew; Khatib, Shaaz; Moharir, Manjiri; Palys, Matthew J.; Pourkargar, Davood Babaei; Tang, Wen

doi:10.1016/j.coche.2018.12.007

Cited by 16 publications

(5 citation statements)

References 41 publications

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“…There exist interesting synergies of the modular design principles discussed in this work with modular design principles of control architectures . This is because in both cases one is implicitly seeking to minimize the degree of interaction between modules.…”

Section: Discussionmentioning

confidence: 99%

Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

AIChE Journal

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We propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off-site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on-site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed-integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

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Section: Discussionmentioning

confidence: 99%

Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

AIChE Journal

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“…For example, how to ensure a certain temperature profile across the parallel modular units? In addition to the unit‐by‐unit control analogous to controlling a single‐unit operation, indirect control systems can be a good option to reduce the control variables as shown in Hasebe 55 for microreactors. Distributed decision making is also necessitated which decomposes the large‐scale modular intensified process into constituent subsystems with corresponding localized decision‐making agents 21,56 . The control decisions are then coordinated for the overall process following approaches such as distributed model predictive control 57 …”

Section: Operability and Control Challenges In Intensified And Modulamentioning

confidence: 99%

“…Early PI breakthroughs mostly relied on Edisonian efforts via experimentation 34 . The advanced computational methods and tools, with expertise of the process systems engineering (PSE) community, aim to support quantitative decision making by providing techno‐economic evaluations as well as predictive capabilities on the design, 25,26 optimization, 19,23,24 and operation 18,21,22 of PI and modular systems.…”

Section: Introductionmentioning

confidence: 99%

Operability and control in process intensification and modular design: Challenges and opportunities

2021

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In this article, the importance of considering operability and control criteria in the analysis and design of intensified and modular processes is discussed. We first analyze the impact on operability of key factors including: (i) degrees of freedom, (ii) process constraints, (iii) numbering up vs. scaling up, and (iv) dynamic/periodic operation. Comparative examples are presented to showcase the pros and cons in intensified/modular systems vs. their conventional counterparts from operability and control aspects. Then we look into metrics and tools to address these challenges such as: (i) flexibility analysis, (ii) operability‐based design, and (iii) advanced model‐based control. Considering different conceptual design stages as synthesis intensification, steady‐state design, and dynamic operational optimization, we highlight the need to incorporate different levels of operability considerations. Future research opportunities and perspectives are also identified, particularly emphasizing the importance of a holistic strategy for integrated design, operability, and control of intensified and modular process systems.

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“…In our recent research, we have focused on the idea of using modularity-based community detection for the decomposition of control and optimization problems into a distributed architecture. 96,97,98 The major contributions of these works are reviewed in the following section, together with our recently developed software implementation.…”

Section: The Network Perspective Of Decompositionmentioning

confidence: 99%

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

2019

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Distributed decision making for intensified process systems

Cited by 16 publications

References 41 publications

Modularity measures: Concepts, computation, and applications to manufacturing systems

Modularity measures: Concepts, computation, and applications to manufacturing systems

Operability and control in process intensification and modular design: Challenges and opportunities

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

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