Authors: Pavel Maksimov; Tuomas Koiranen
Addresses: LUT School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851, Lappeenranta, Finland ' LUT School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851, Lappeenranta, Finland
Abstract: Modern process engineering industry offers great opportunities for harvesting tremendous amounts of data, both structured and unstructured. However, significant volumes of information as well as frequently encountered inconsistencies, missing values and other discrepancies render data processing with traditional tools rather inefficient. New software solutions are being constantly developed to address this challenge, yet, as regards analytics of actual industry related data, adaptation of these instruments has been comparatively limited so far. Consequently, within the limits of this work, applicability of novel analytical instruments in the context of process engineering industry is studied for both structured and unstructured data processing. In the former case, the data describing the copper matte smelting process is analysed focusing on identification of interdependencies between key process parameters and products' properties, while in the latter case, a collection of relevant scientific articles is investigated with a view to extracting key concepts and determining major relations among them.
Keywords: data processing; predictive analysis; cognitive search; data extraction; process engineering; metal industries.
International Journal of Computer Applications in Technology, 2020 Vol.62 No.3, pp.200 - 215
Received: 01 Feb 2019
Accepted: 18 Sep 2019
Published online: 10 Apr 2020 *