Uncertainty analysis of operational conditions in selective artificial ground freezing applications

• Autorzy: Zueter Ahmad , Akhtar Saad , Sasmito Agus

Identyfikatory

DOI

10.46873/2300-3960.1357

• Języki publikacji: EN

Abstrakty

EN

Artificial ground freezing (AGF) systems are susceptible to uncertain parameters highly affecting their performance. Particularly, selective artificial ground freezing (S-AGF) systems involve several uncertain operational conditions. In this study, uncertainty analysis is conducted to investigate four operational parameters: 1) coolant inlet temperature, 2) coolant flow rate, 3) pipes emissivity, and 4) pipes eccentricity. A reduced-order model developed and validated in our previous work for field-scale applications is exploited to simulate a total of 5,000 cases. The uncertain operational parameters are set according to Monte Carlo analysis based on field observations of a field-scale freeze-pipe in the mining industry extending to 460 m below the ground surface. The results indicate that the freezing time can range between 270 and 350 days with an average of 310 days, whereas the cooling load per one freeze-pipe ranges from 90 to 160 MWh, with an average of 129 MWh. Furthermore, it is observed that the freezing time and energy consumed are mostly dominated by the coolant inlet temperature, while energy dissipated in the passive zone (where ground freezing is not needed) is mostly affected by pipes emissivity. Overall, the conclusions of this study provide useful estimations for engineers and practitioners in the AGF industry.

Słowa kluczowe

EN

selective artificial ground freezing; Monte Carlo; pipes eccentricity; uncertainty analysis; reduced-order modelling

PL

selektywne sztuczne zamarzanie gruntu; Monte Carlo; analiza niepewności; modelowanie zredukowanego rzędu

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2022
• Tom: Vol. 21, iss. 3
• Strony: 169--179
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Zueter Ahmad

• McGill University, Mining Engineering Department, Montreal, Quebec, Canada

• https://orcid.org/0000-0003-4521-3790

autor

Akhtar Saad

• McGill University, Mining Engineering Department, Montreal, Quebec, Canada

• https://orcid.org/0000-0001-5477-4974

autor

Sasmito Agus

• McGill University, Mining Engineering Department, Montreal, Quebec, Canada

• https://orcid.org/0000-0003-3444-8922

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).