The industries have been often seeking to reduce operating expenses, as to increase profits and competitiveness. To achieve this goal, it must take into account, among other factors, the design and implementation of new tools that accurately, efficiently and inexpensively allow access to information relevant to process. Soft sensors have been increasingly applied in
industry. Since it offers flexibility, it can be adapted to make estimations of any measurement, thus a reducing in operating costs without compromising the measurements, and in some cases even improve the quality of generated information. Since they are completely softwarebased, they are not subjected to physical damage as the real sensors, and are better adaptated to harsh environments with hard access. The success of this king of sensors is due to the use
of computational intelligence techniques, which have been widely used in the modeling of several nonlinear complex processes. This work aims to estimate the quality of alumina
fluoride from a Gas Treatment Center (GTC), which is the result of gaseous adsorption on
alumina virgin, using a soft sensor. The model that emulates the behavior of a alumina quality sensor the plant was created using an artificial intelligence technique known as Artificial Neural Network. The motivations of this work are: perform virtual simulations without compromising the GTC and make accurate decisions based not only on the operator's experience, to diagnose potential problems before they can interfere with the quality of alumina fluoride; maintain the aluminum reduction pot control variables within normal limits,
since the production from low quality alumina strongly affects the reaction of breaking the molecule that contains this metal. The benefits this project brings include: increasing the GTC efficiency, producing high quality fluoridated alumina and emitting fewer greenhouse gases into the atmosphere and increasing the pot lifespan.
|
