SYSTEM DYNAMICS MODELLING AND SIMULATION OF COMPUTER CONTROL SYSTEMS FOR STEEL INGOT FURNACES

• Gabriel Somo Department of Industrial Engineering,Tshwane University of Technology, Pretoria, South Africa https://orcid.org/0009-0004-3944-9662
• Ilesanmi Daniyan Department of Mechanical & Mechatronics Engineering, Achievers University, Owo, Nigeria https://orcid.org/0000-0002-7238-9823
• Jan Swanepoel Department of Industrial Engineering,Tshwane University of Technology, Pretoria, South Africa https://orcid.org/0000-0001-6861-4437
• Khumbulani Mpofu Department of Industrial Engineering,Tshwane University of Technology, Pretoria, South Africa https://orcid.org/0000-0003-3429-7677

DOI:

https://doi.org/10.7166/34-4-2862

Keywords:

Automation, Control system, Gas furnace, PLC-IoT, Simulation

Abstract

The problem that necessitated this study was that some furnaces that were responsible for the further processing of steel pipes at Company X were not connected to a programmable logic control (PLC)–Internet of Things (IoT) system. This slowed down the product flow. To address this problem, PLC-IoT systems were integrated so that the production system could be more effective. This study therefore models and simulates the integrated PLC-IoT control system, using the system dynamics modelling approach to investigate the performance of the automated gas furnaces. The PLC-IoT consists of an internal module that is used to process the data stream. After the implementation of the proposed control system, primary data was acquired by continuously evaluating the performance of the system. The research methodology used involved quantitative analysis and a simulation model using the Anylogic software. The result of the delivery and inventory of ingots from an automated system showed an upward trend with delivery and an inventory of an ingot at about 90 minutes; while a system that was not automated with PLC-IoT showed a downward trend of ingots. This shows that the introduction of the PLC-IoT for control makes the system more effective. The findings of this work could assist the manufacturers in achieving manufacturing or production efficiency, significant time savings, and better monitoring of the manufacturing process.