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A one-shot learning framework to model process systems

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU139938" target="_blank" >RIV/00216305:26210/20:PU139938 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.aidic.it/cet/20/81/157.pdf" target="_blank" >https://www.aidic.it/cet/20/81/157.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3303/CET2081157" target="_blank" >10.3303/CET2081157</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    A one-shot learning framework to model process systems

  • Popis výsledku v původním jazyce

    In the era of Big Data, the utilization of data-driven analytics for process engineering systems is rising exponentially. The abundance of data from industrial sensors and various documentation logs have served as a strong basis for such analysis. Nevertheless, there are some critical data in an industry that simply rare and uncommon due to certain processing constraints or confidentiality. Such constraints may include economic costs for data acquisition, the complexity for data collection, the needs for qualified personnel and many other unforeseeable problems. Due to conventional data-driven approach requiring a large volume of data, such rare but critical data cannot be properly utilized. For this aspect, we proposed a one-shot learning framework to model process systems. The novel framework utilizes prior knowledge from multi-sourced data to learn the conditional relationships of critical variables within the process. By utilizing prior generic knowledge of the system, one-shot learning can provide a better representation of the prediction space when acting as a data-driven black-box model. A combined heat and power (CHP) system is used as the case study for one-shot learning modelling which a mean squared error of 0.00616 was achieved. The efficient use of data within this framework is expected to be beneficial when modelling under high-priority and low data availability.

  • Název v anglickém jazyce

    A one-shot learning framework to model process systems

  • Popis výsledku anglicky

    In the era of Big Data, the utilization of data-driven analytics for process engineering systems is rising exponentially. The abundance of data from industrial sensors and various documentation logs have served as a strong basis for such analysis. Nevertheless, there are some critical data in an industry that simply rare and uncommon due to certain processing constraints or confidentiality. Such constraints may include economic costs for data acquisition, the complexity for data collection, the needs for qualified personnel and many other unforeseeable problems. Due to conventional data-driven approach requiring a large volume of data, such rare but critical data cannot be properly utilized. For this aspect, we proposed a one-shot learning framework to model process systems. The novel framework utilizes prior knowledge from multi-sourced data to learn the conditional relationships of critical variables within the process. By utilizing prior generic knowledge of the system, one-shot learning can provide a better representation of the prediction space when acting as a data-driven black-box model. A combined heat and power (CHP) system is used as the case study for one-shot learning modelling which a mean squared error of 0.00616 was achieved. The efficient use of data within this framework is expected to be beneficial when modelling under high-priority and low data availability.

Klasifikace

  • Druh

    J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS

  • CEP obor

  • OECD FORD obor

    20704 - Energy and fuels

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategické partnerství pro environmentální technologie a produkci energie</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Chemical Engineering Transactions

  • ISSN

    2283-9216

  • e-ISSN

  • Svazek periodika

    81

  • Číslo periodika v rámci svazku

    1

  • Stát vydavatele periodika

    IT - Italská republika

  • Počet stran výsledku

    6

  • Strana od-do

    937-942

  • Kód UT WoS článku

  • EID výsledku v databázi Scopus

    2-s2.0-85092313869