cff-version: 1.2.0
abstract: "<p>The dataset refers to the research activity performed in the framework of the EU project SAMOSAFER, Task 6.3 - Innovative control model and strategy development</p><p>and applications to MSFR.</p><p><br></p><p>In this activity, an innovative incident detection method has been developed, aiming at improving the safety and reliability of the Molten Salt Fast Reactor</p><p>power plant, focusing on operational scenarios involving some deviations from normal operational conditions.</p><p><br></p><p>The data-driven incident detection and classification methodology (based on the kNN algorithm) aims at identifying abnormal plant conditions thanks to a</p><p>continuous monitoring of some measurable system parameters and variables (e.g., the molten salt temperatures in the secondary circuit).</p><p><br></p><p>In order to train the algorithm, a set of numerical, time-dependent simulation is carried out at the system-level (primary circuit, secondary circuit and</p><p>balance of plant) with the Modelica language.</p>"
authors:
  - family-names: Abrate
    given-names: Nicolò
    orcid: "https://orcid.org/0000-0002-6416-7059"
  - family-names: Caruso
    given-names: Nicolò
  - family-names: Dulla
    given-names: Sandra
    orcid: "https://orcid.org/0000-0002-3019-1472"
  - family-names: Pedroni
    given-names: Nicola
    orcid: "https://orcid.org/0000-0002-0636-613X"
  - family-names: Lorenzi
    given-names: Stefano
    orcid: "https://orcid.org/0000-0003-2747-1825"
title: "Data underlying the research of Innovative control model and strategy development and applications to MSFR"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/0ae20eee-97a6-4634-9f57-eb1887018fc2.v1
license: CC BY 4.0
date-released: 2023-12-06