Condition Identification of Calcining Kiln Based on Fusion Machine Learning and Semantic Web

The static control limits restrict self-healing capabilities and decision-making processes, impeding adaptability to the dynamic shifts in intricate industrial operations, frequently leading to suboptimal or anomalous conditions that undermine production efficiency. This paper presents a methodology for the identification of suboptimal operating conditions with respect to yield and quality. A GSR model for the identification of suboptimal operating conditions of yield and quality based on random forest classification was established. The experimental results demonstrate that the method is capable of rapidly and accurately identifying the production and quality of the calcining kiln. The identification accuracy of the yield and quality of the suboptimal operating conditions is 99.82% and 99.18% respectively. In the production process, real-time identification of operating parameters enables rapid detection of suboptimal operating conditions in yield and quality, providing the basis for optimal regulation and control, which in turn can improve production efficiency.