Furnaces
Using artificial intelligence with near infra-red furnace imaging Erik Muijsenberg* and Robert Bodi** discuss the rise of artificial intelligence and how modern technologies can improve the glass manufacturing process
Fig1. Furnace images by a near infrared camera and temperature
Furnace modeling
M
ost leading engineering firms and glass producers around the world are already using furnace modeling, also known as Computation Fluid Dynamics (CFD), such as the GS Glass Furnace Model (GS GFM) software package. While in 1990 it was a discussion about the accuracy of modeling, today it is considered reliable and useful. It is now state-of-the-art and is used for almost every furnace design or rebuild. The GS Expert System III (ESIII) is a model-based predictive furnace and forehearth control system, that has evolved beyond CFD. People were initially sceptical to believe it was possible to control a furnace with Model Based Predictive Control (MPC) but today there are more than 300 furnaces globally with MPC installed, with many of these glass furnaces in operation also on forehearths. Since 2010, there has been tremendous interest in Industry 4.0, as the glass industry has become aware that industrial
producers (including glassmakers) are instigating new standards like furnace cameras and batch convection movement monitoring within the furnace. The question has become: What is happening now? What will come after this evolution of MPC? Industry 4.0 captures many aspects of the automation of the manufacturing process, including robots, the Internet of Things, simulations of the process, cyber security, system integration, cloud computing, 3D, big data and augmented reality. When looking at a modern glass production line today, viewing a typical end-fired furnace, a regenerator, melter and a forehearth deliver the glass to the forming machines, many of the processes are already automated within different areas. On the melting side, PID DCS control has had limited success previously, because of slow furnace reactions. Therefore, GS began by applying model-
based predictive control strategies, because PID control by an operator for 24 hours non-stop was limiting, along with slow temperature reaction times of the furnace and a very large dead time for the responses. With MPC and its dynamic matrix algorithms, it is possible to capture process behaviours with such models and equations and improve furnace operation. For MPC models and the dynamic base of its algorithms, the process can be driven for optimum quality with the lowest emissions, lowest operational costs and with minimal assistance, even sometimes without the operator. That is why GS has integrated the furnace camera into the ESIII concept.
Furnace camera uses The main motivation for GS A.SENS furnace camera was to start monitoring the batch blanket to relieve the operator Continued>>
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43 Glass International May 2021
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17/05/2021 06:49:27
