Intelligent measurements for monitoring and control of glass production furnace for green and efficient manufacturing

Liquefied petroleum (LP) gas is used as one of the fuel systems for glass production furnaces. This research was conducted to develop an intelligent online measurement system for monitoring and control of LP gas so as to achieve green and efficient manufacturing. LP gas is mixed with air at a desired ratio in order to get a proper specific gravity for glass production. Counterpropagation neural networks (CPNs), which are based on competitive learning, were used in this work. Three inputs, air inlet pressure, air/mixed gas differential pressure, and propane/mixed gas differential pressure, were selected for online measurements of specific gravity for monitoring and control of a glass production furnace. Using a 3 × 12 × 1 CPN yields exceedingly successful results for online measurements of specific gravity. An average error of 1.68 %, a minimum error of 0.08 %, and a maximum error of 4.43 % were achieved for online measurements. Control actions can then be taken to achieve much higher energy efficiency which is very important for glass production for green and efficient manufacturing.