On optimal sensing and actuation design for an industrial scale steam methane reformer furnace

Kumar, Ankur; Bâldea, Michael; Edgar, Thomas F.

doi:10.1002/aic.15333

Cited by 14 publications

(6 citation statements)

References 32 publications

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“…Automation in the process manufacturing industry is progressing by the development of advanced, commercial sensors and analytics [65][66][67]. These sensors can collect data not previously available, enabling manufacturers to identify alternative sustainable manufacturing routes.…”

Section: Application Of Collaborative Design and Greater Automation To Process Manufacturingmentioning

confidence: 99%

Cloud manufacturing as a sustainable process manufacturing route

Fisher

Watson

Porcu³

et al. 2018

Journal of Manufacturing Systems

221

108

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Cloud Manufacturing (CM) is a service oriented business model to share manufacturing capabilities and resources on a cloud platform. Manufacturing is under pressure to achieve cost and environmental impact reductions, as manufacturing becomes more integrated and complex. Cloud manufacturing offers a solution, as it is capable of making intelligent decisions to provide the most sustainable and robust manufacturing route available. Although CM research has progressed, a consensus is still lacking on the concepts within CM as well as applications and scope beyond discrete manufacturing.The aim of this paper is to demonstrate how CM offers a more sustainable manufacturing future to the industry as a whole, before focusing specifically on the application to process manufacturing (e.g. food, pharmaceuticals and chemicals). This paper details the definitions, characteristics, architectures and previous case studies on CM. From this, the fundamental aspects of the CM concept are identified, along with an analysis of how the concept has progressed. A new, comprehensive CM definition is formulated by combining key concepts drawn from previous definitions and emphasizes CM potential for sustainable manufacturing.Four key methods of how CM increases sustainability are identified: (1) collaborative design; (2) greater automation; (3) improved process resilience and (4) enhanced waste reduction, reuse and recovery. The first two key methods are common to both discrete and process manufacturing, however key methods (3) and ( 4) are more process manufacturing specific and application of CM for these has yet to be fully realised. Examples of how CM's characteristics may be utilised to solve various process manufacturing problems are presented to demonstrate the applications of CM to process manufacturing. Waste is an important consideration in manufacturing, with strong sustainability implications. The current focus has been on using CM for waste minimisation; however, process manufacturing offers waste as a resource (valorisation opportunities from diversifying co-products, reuse, recycle and energy recovery). Exploring CM's potential to characterise and evaluate alternative process routes for the valorisation of process manufacturing waste is considered for the first time. The specific limitations preventing CM adoption by process manufacturers are discussed. Finally, CM's place in the future of manufacturing is explored, including how it will interact with, and complement other emerging manufacturing technologies to deliver a circular economy and personalised products.

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Section: Application Of Collaborative Design and Greater Automation To Process Manufacturingmentioning

confidence: 99%

Cloud manufacturing as a sustainable process manufacturing route

Fisher

Watson

Porcu³

et al. 2018

Journal of Manufacturing Systems

221

108

View full text Add to dashboard Cite

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“…The approach is based on modeling the local effects of each fuel burner using measured data. Furthermore, the authors examined several sensor and actuator configurations using the same industrial data set …”

Section: Integrating Decisions Across Multiple Time Scalesmentioning

confidence: 99%

“…Furthermore, the authors examined several sensor and actuator configurations using the same industrial data set. 78…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems

Tsay

Bâldea

2019

Ind. Eng. Chem. Res.

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In this paper, we review the role of data as a “bridge” connecting the different time/length scales of chemical processes in mathematical modeling and multiscale, integrated decision making. We argue that this is a fitting role of “big data” in the chemical industry, an area that comprises complicated yet deterministic physical systems. Such systems can be described using physical and chemical laws that are generally well understood. As such, data and their analysis are less likely to provide the unexpected and/or surprising insights that they have generated in other sectors (e.g., the transactional economy, social sciences). Nevertheless, historical operating data (which are often plentiful and available at little cost) can be converted to very useful information for multiscale mathematical modeling of chemical processes. Several examples of integration are provided, mapped on the continuum of time and length scales of process systems. Existing research challenges and potential directions for future work are discussed.

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“…Where x, F(x,y), y denote decision variables, outer objective function, inner decision variables, respectively. It is usually called a leader-follower problem, where x is decided by the leader while y is decided by the follower [54].…”

Section: Optimization Basismentioning

confidence: 99%

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

He¹,

Jia²,

Zhao³

et al. 2018

Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering &Amp; Science (MEEES 2018)

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This paper presents a broad overview of the various sensor placement strategies to diagnose discrete-part manufacturing system. Due to the technical complexity, the performance of sensor system would be cursed by any modules of sensor placement strategies. Therefore, the current state of the sensor placement strategies is outlined with three key modules, namely cause-effect relationship model, optimization basis, and optimization algorithm, are surveyed in detail. The challenges faced by industry and academia are discussed and several principle conclusions are drawn, which could create a clear platform for the neophyte researchers for sensor deployment to diagnose discrete-part manufacturing system.

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On optimal sensing and actuation design for an industrial scale steam methane reformer furnace

Cited by 14 publications

References 32 publications

Cloud manufacturing as a sustainable process manufacturing route

Cloud manufacturing as a sustainable process manufacturing route

110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

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