The increase in the efficiency of industrial processes has been necessary in the present day, due to the high market competitiveness. In this context, techniques that seek to increase the performance efficiency of industrial plant control systems, minimizing costs and maximizing product quality, such as the use of Kalman Filter in process variables that feed control systems, has significant relevance. Kalman filter is a parameter estimation technique that allows, for linear systems, the reduction of noise and uncertainties that are subject to process variables. Its wide use is mainly due to the fact that the variables (information) that are obtained through the process, through physical instruments and measurement routines, have finite precision and are naturally corrupted by errors (random or coarse). These variables, when introduced into the control system, without the use of the Kalman Filter, carry all noise signals, reducing the performance of the control system. The aim of this work is to present the results of the Kalman Filter implementation in the industrial process, a propane generator column, controlled through a Predictive Controller, based on real data, in order to improve control performance and energy efficiency of this process. Simulation studies are performed on the MATLAB / SIMULINK platform
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