Title: Mining models for predicting product quality properties of petroleum products

Authors: Ng'ambilani Zulu; Douglas Kunda

Addresses: Indeni Petroleum Refinery Company, P.O. Box 71869, Bwana Mkubwa Industrial Area, Ndola, Zambia ' Mulungushi University, P.O. Box 80415, Kabwe, Zambia

Abstract: There is a huge generation of raw data during production processes of refinery products and in most cases this data remains under-utilised for knowledge acquisition and decision making. The purpose of this study was to demonstrate how data mining techniques can be used to develop models to predict product quality properties for petroleum products. This study used petroleum refinery production raw data to build predicting models for product quality control activities. The plant and laboratory data for the period of about 18 months was mined from the refinery repositories in order to build the datasets required for analysis using Orange3 data mining software. Four data mining algorithms were chosen for experiments in order to determine the best predicting model using cross-validation technique as a validation method. This study only employed two measuring metrics, classification accuracy (CA) and root mean square error (RMSE) as performance indicators. Random forest came out as the best performing model suitable for predicting both categorical (CA) and numeric data (RMSE). The study was also able to establish the relationship between the variables that could be used in critical operational decisions.

Keywords: data mining; machine learning; industries; petroleum refinery; product quality; parameter optimisation.

DOI: 10.1504/IJBIDM.2023.129881

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.3, pp.359 - 388

Received: 07 Jul 2021
Accepted: 19 Oct 2021
Published online: 03 Apr 2023 *

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