Regeneration Management Tool for Industrial Ecosystem

Diez, Laëtitia; Marangé, Pascale; Levrat, Éric

doi:10.1016/j.ifacol.2017.08.1797

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Current research on industrial ecosystems includes a toolkit for managing industrial ecosystem regeneration (Diez et al, 2017); building and optimizing a resource-based industrial ecosystem (Fan et al, 2017); studying the impact of industrial symbiosis initiatives on the emergence of regional industrial ecosystems (Susur et al, 2019); finding an approach to lifecycle management based on symbiosis for sustainable resource flows in an industrial ecosystem (Shi and Li, 2019); developing an industrial ecosystem orchestration model in a circular economy (Parida et al, 2019); developing an innovative model of industrial ecosystem transformation based on artificial intelligence (Burström et al, 2021), etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Бабкин¹,

Глухов²,

Shkarupeta³

et al. 2021

IJTech

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The essence of the transformation processes currently taking place in all sectors of the economy, including industry, is increasingly described by the ecosystem approach. Industrial ecosystems have proven to be the models with the highest production efficiency. The effective management of industrial ecosystems is possible only if the mechanisms underlying their dynamics are understood. Achieving a high level of maturity for industrial ecosystems in the framework of digital technology implementation is of particular relevance, and this research aims to develop and test a methodology for assessing the maturity level of such industrial ecosystems by relying on principal component analysis and hierarchical agglomerative clustering. A mature industrial ecosystem gets rapidly integrated into the new technological paradigm and global value chains, is able to compete in global markets in the long term, and has increased potential for industrialization and digital transformation. The proposed methodology is based on the environmental, social, and corporate governance (ESG) methodology for determining the levels of interests in the area of sustainable development of an industrial ecosystem. The proposed approach singles out a fourth assessment projection in addition to ESG, namely the digital maturity. All four maturity assessment projections are proven to be positively and significantly correlated. An industrial ecosystem maturity assessment scale has been developed, including six levels (very high; advanced; basic; elementary; zero; and minus one). The methodology has been tested on the national industrial ecosystem and the metallurgy and mining industrial ecosystem of Russia. The results show that the maturity level of the Russian industrial ecosystem as of mid-2021 is "basic" with the prospect of transition to "advanced." The Russian metallurgy and mining industrial ecosystem maturity level is "advanced." The key directions for increasing the maturity levels of Russian industrial ecosystems based on innovations in various industrial aspects are proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Бабкин¹,

Глухов²,

Shkarupeta³

et al. 2021

IJTech

View full text Add to dashboard Cite

show abstract

“…Also, several leading scientific journals are specifically dedicated to this research field involving many researchers and engineers from different disciplines: general computing, computer networks and IoT (Internet of Things), advanced statistics, signal processing, Artificial Intelligence, data science and data mining, intensive computing, embedded systems, etc.. Some diagnostic approaches are specifically designed or adapted for application fields and sectors such as manufacturing and production systems [3], industry 4.0 [4], material/tools interaction in milling [5] [6] [7], aeronautic sector [8], energy [9] [ 10], health [11], mobility/transport [12], waste management/wastewater treatment plants [13], rotating machines [14], … Predictive diagnostics and maintenance approaches are traditionally based on different methods:…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

Ringler,

Knittel,

Ponsart

et al. 2023

2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)

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Edge Computing is becoming more and more essential for the Industrial Internet of Things (industrial IoT) for predictive maintenance in the industry 4.0 framework. The transition from central to distributed approaches (edge nodes), will enhance the capabilities of handling real-time big data from IoT by ensuring low latency and high bandwidth. Moreover, with the developed architecture the possibility is given to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models. Therefore, the performances of the global diagnostic models can be improved. In this paper, the developed setup is composed of PC's, NVIDIA Jetson Nano Developers kits (for edge computing), and a smartphone for real-time displaying. The implemented real-time supervised machine learning approaches are applied on an industrial oilinjection screw compressor instrumented with vibration sensors. Time domain features are calculated online with the help of sliding windows and the features are automatically classified. Embedded in the equipment, the used algorithms obtained very good real-time diagnostic performances.

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“…The specialization deepening contributed to the use of concepts of the system methodology in the sectoral aspect, primarily in the process of forming the infrastructure of territorial systems of mineral resources specialization, which was emphasized in the works of foreign scientists (Mahmood et al, 2023), (Diez, Marangé, and Levrat, 2017) and Russian researchers (Litvinenko et al, 2023), (Matrokhina, Makhovikov, and Khaikin, 2023), (Litvinenko and Sergeev, 2019), and others. In this regard, increasing the investment attractiveness of oil and gas enterprises by activating services based on the infrastructure being created contributes to the sustainable development of territories.…”

Section: Introductionmentioning

confidence: 99%

Assessment of The Impact of Services and Digitalization Level on The Infrastructure Development in Oil and Gas Regions

Glukhov,

Shchepinin,

Lyubek

et al. 2023

IJTech

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The relevance and feasibility of studying the problem lie in increasing the investment attractiveness of oil and gas enterprises by activating regional services in mineral resource specialization based on the extensive infrastructure being created. The purpose of this study is to establish a typology for infrastructure services catering to oil and gas enterprises by enhancing the service component and implementing digitalization programs in the regions specializing in mineral resources in Russia. The work substantiates the conceptual foundations of an integrated approach to the formation of infrastructure for economic systems of mineral resource specialization and related services in the form of a synthesis of concepts of reproductive, structural-functional, and systemic approaches. An analysis of the current state of the methodology for implementing a typology of infrastructure services for oil and gas enterprises in the Russian Federation and abroad was carried out. The formation of innovative infrastructure facilities was substantiated through economic modelling of the influence of oilfield services and the level of development of the territory's digitalization on the development of infrastructure facilities in oil and gas regions.

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Regeneration Management Tool for Industrial Ecosystem

Cited by 4 publications

References 8 publications

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Methodology for Assessing Industrial Ecosystem Maturity in the Framework of Digital Technology Implementation

Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

Assessment of The Impact of Services and Digitalization Level on The Infrastructure Development in Oil and Gas Regions

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