Glass Melting Technology: A Technical and Economic ... - OSTI

• Fuzzy Control Automation solutions could optimize forehearth zone temperature controls for better zone
temperature-stability, which leads to increased productivity and quality as well as reduced production costs.
• Multivariable Predictive Control for melting and feeder profile increases throughput and reduces product
changeover times for relatively low capital costs.
• Maintenance Management involves real time monitoring of process efficiency and product quality, and
extends equipment life, reducing maintenance costs and spare parts inventories.
• Model Predictive Control is a model-based control replacement for multiple glass applications (forehearth,
product quality, furnace melt, melt level) and is low cost, requires reduced energy, improves quality and
reduces emissions.
Among the companies that provide Advanced Control Applications systems are Brainwave/University
Dynamics Technologies, Glass Service, Siemens (STG, IPCOS) and TNO.
A Model System:
Advanced Control of Glass Melting and Conditional Processes
Today almost 80 percent of melting furnaces in the world are estimated to be operating either on manual
control or by single PID temperature control. Due to response time of the production system and the
complexity of simultaneous chemical and physical processes, it is very difficult for a human operator to
optimally control such melting furnace systems.
The most important objectives for such control are furnace stability, consistency of control strategy, fuel
firing profiles, temperature profile stability, fuel caloric value compensation, glass level stability, emissions,
forehearth temperature, homogeneity and other important process parameters.
One advanced control system available uses expert rules control, model based predictive control and fuzzy
control to harmonize and optimize the complex system of furnace operations, including the batch charger,
melter, refiner and working end, and forehearths. [1]
Model Predictive Control
Model Predictive Control (MPC) is one of the advanced control techniques that has been proven very
effective for glass melting furnace control. The optimal control solutions are computed inclusive of the
entire operational strategies, which are complex by their very nature. These solutions consider all
measurable relationships between process inputs like heating, cooling, fuel, flows, pressure values, etc., and
process outputs represented by thermocouples and by other sensors, such as Multi Input Multi Output
(MIMO). The MIMO, together with process prediction for future occurrences, makes the MPC technique a
very precise and powerful computational tool. Based on the derived mathematical process model and the
expected future process behavior, an optimization model is built and solved by several methods in accord
with operator requests. [2]
Advanced Furnace Control
One example of MPC approach for a typical float furnace uses manipulated variables, such as gas, electric
power, and dilution air control to better control the target melter temperature. The next important controlled
parameter for a float furnace is the canal temperature, or the area where the melted glass flows into the tin
bath. The prediction and feed forward information that comes from the refiner and working end can help to
keep the forming process temperatures more precisely on the target values.
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