Intelligent signal validation system for cupola furnace, Part I: methodology for rule-based filtering and inferential sensing

摘要

In this research, we present a methodology for developing a signal validation technique that can be introduced to improve the operation of the cupola iron-melting furnace, an important foundry process used to melt iron. To improve the operation of the furnace, digital controllers are employed. The operation of such controllers, however, depends on accurate measurement of controlled variables. It is necessary to provide reliable signals to the controller for its proper operation. In this research, we develop a signal validation system for one of the process variables of the cupola furnace, namely, iron temperature. An artificial neural network (ANN) rule-based filter and trend estimator (RBFTE) is developed to estimate the measurement signals and to eliminate spikes and other external disturbances from the measurement signals. Analytical redundancy is provided through the use of inferential sensors, developed through the identification of input-output dynamic models for the iron temperature using ANN. Another type of inferential sensor that relies on the identification of nonlinear relations between the iron temperature and another temperature measurement across the furnace body is also developed. In PartⅡ (Paper 99-76), testing results for the RBFTF filter and inferential sensors and a sensor fusion algorithm are provided.