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Wyszukujesz frazę "workflow net" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
An analytical method for well-formed workflow/Petri net verification of classical soundness
Autorzy:
Clempner, J.
Powiązania:
https://bibliotekanauki.pl/articles/331027.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Lyapunov stability
Petri net
decidability
workflow net
soundness
verification
sieć Petriego
sieć przepływu pracy
stabilność Lapunova
Opis:
In this paper we consider workflow nets as dynamical systems governed by ordinary difference equations described by a particular class of Petri nets. Workflow nets are a formal model of business processes. Well-formed business processes correspond to sound workflow nets. Even if it seems necessary to require the soundness of workflow nets, there exist business processes with conditional behavior that will not necessarily satisfy the soundness property. In this sense, we propose an analytical method for showing that a workflow net satisfies the classical soundness property using a Petri net. To present our statement, we use Lyapunov stability theory to tackle the classical soundness verification problem for a class of dynamical systems described by Petri nets. This class of Petri nets allows a dynamical model representation that can be expressed in terms of difference equations. As a result, by applying Lyapunov theory, the classical soundness property for workflow nets is solved proving that the Petri net representation is stable. We show that a finite and non-blocking workflow net satisfies the sound property if and only if its corresponding PN is stable, i.e., given the incidence matrix A of the corresponding PN, there exists a Φ strictly positive m vector such that AΦ ≤ 0. The key contribution of the paper is the analytical method itself that satisfies part of the definition of the classical soundness requirements. The method is designed for practical applications, guarantees that anomalies can be detected without domain knowledge, and can be easily implemented into existing commercial systems that do not support the verification of workflows. The validity of the proposed method is successfully demonstrated by application examples.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 931-939
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Emission Reduction in Oil & Gas Subsurface Characterization Workflow with AI/ML Enabler
Autorzy:
Thanh, Thuy Nguyen Thi
Lee, Samie
Nguyen, The
Duyen, Le Quang
Powiązania:
https://bibliotekanauki.pl/articles/27323253.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
CO2 emission
net zero carbon
machine learning
CCUS
digital transformation
emission reduction
digital subsurface workflow
emisja CO2
transformacja cyfrowa
redukcja
Opis:
According to (McKinsey & Company, 2020), drilling and extraction operations are responsible for 10% of approximately 4 billion tons of CO2 emitted yearly by Oil and Gas sector. To lower carbon emissions, companies used different strategies including electrifying equipment, changing power sources, rebalancing portfolios, and expanding carbon-capture-utilization-storage (CCUS). Technology evolution with digital transformation strategy is essential for reinventing and optimizing existing workflow, reducing lengthy processes and driving efficiency for sustainable operations. Details subsurface studies take up-to 6–12 months, including seismic & static analysis, reserve estimation and simulation to support drilling and extraction operations. Manual and repetitive processes, aging infrastructure with limited computing-engine are factors for long computation hours. To address subsurface complexity, hundred-thousand scenarios are simulated that lead to tremendous power consumption. Excluding additional simulation hours, each workstation uses 24k kWh/month for regular 40 hours/month and produces 6.1kg CO2. Machine Learning (ML) become crucial in digital transformation, not only saving time but supporting wiser decision-making. An 80%-time-reduction with ML Seismic and Static modeling deployed in a reservoir study. Significant time reduction from days-tohours-to-minutes with cloud-computing deployed to simulate hundreds-thousands of scenarios. These time savings help to reduce CO2-emissions resulting in a more sustainable subsurface workflow to support the 2050 goal.
Źródło:
Inżynieria Mineralna; 2023, 2; 289--294
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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