Prediction Performance Improvement via Anomaly Detection and Correction of Actual Production Data in Iron Ore Sintering Process

The accuracy and integrity of the actual production data influence the reliability and stability of sintering process in steel industry. However, the actual production data may encounter various outliers due to noise, sensor failure, and operator negligence existing in this process. To tackle this issue, this article develops an original framework for the detection and correction of abnormal production data in the sintering process. First, an improved kernel-based Fuzzy C-Means algorithm is developed to effectively divide normal production data under multiple operating conditions. Then, different one-class support vector machine (SVM) classifiers are constructed for different operating conditions. According to which operating condition the actual production data belongs to, the one-class SVM under this operating condition is called to accurately detect abnormal production data. Finally, the most similar normal historical data in the operating condition is obtained to correct the abnormal data by using $k$ nearest neighbor algorithm based on the Mahalanobis distance. Simulation results involving actual production data illustrate the effectiveness of the proposed method. By taking two existing models of the sintering process as examples, their prediction performance becomes improved after detecting and correcting the abnormal production data, so that the proposed framework has important engineering application impact.