Fusion Based Quality Control Using Intelligent Estimation and Modeling in Industrial Process Control

In this paper, to improve quality control system and being in the competition at a cement production line, we implemented a Local Linear Neuro-Fuzzy Model (LLNFM) and Kalman Filter Information Fusion (KFIF). We proposed a novel approach for model reference control of a cement milling circuit that has previously been studied several times. To do so, first gathered information from Distributed Sensor Network (DSN), deployed in the plant, is used to model under-control process based on the LLNFM approach. This LLNFM is used to prepare data for a KFIF system to derive the form of the control vector with the goal of driving the response of the system to that of a desired model in a noisy operating environment. The paper demonstrates the extraction of the reference models and the derivation of the control laws and the results observed justify the tracking and stability claims of the paper.