A one-shot learning framework to model process systems

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

A one-shot learning framework to model process systems

Original language description

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.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategic Partnership for Environmental Technologies and Energy Production</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Name of the periodical

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Volume of the periodical

81

Issue of the periodical within the volume

1

Country of publishing house

IT - ITALY

Number of pages

6

Pages from-to

937-942

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85092313869