Bayesian Network-Based Process Downtime Cost Determination of an Industrial Plant
Kevin M. Suliva1, Senen D. Fenomeno2
1Kevin M. Suliva, Graduate Studies, Department of EECE, Mapua University, Philippines.
2Senen D. Fenomeno, Graduate Studies, Department of EECE, Mapua University, Philippines.
Manuscript received on 16 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 2768-2776 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B13400982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1340.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Industrial plants utilize sensitive equipment to produce their products and meet their financial targets. Equipment downtime caused by power quality issues such as voltage sag affects production and entails cost hence poses a threat to their ability to deliver their financial objectives. This research aims to determine the response of industrial equipment to sag events and quantify the downtime cost caused by interruption in the production process. The study used the voltage tolerance curve to determine the individual equipment response to sag events and the Bayesian Network to establish the network structure of the production process. The probability of process interruption and the associated downtime losses was computed using a mathematical software. The research shows a strong relationship between the equipment’s response to voltage sag events and the production downtime cost and highlights the importance of the immunity of equipment to voltage sags.
Keywords: Industrial Process, Sensitive Equipment, Voltage Sag, Voltage Tolerance Curve, Bayesian Network.
Scope of the Article: Process & Device Technologies