Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM_2.5 and PM₁₀ using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O_3, SO₂, NO₂, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings.

Dynamic statistical process monitoring methods have been widely studied and applied in modern industrial processes. These methods aim to extract the most predictable temporal information and develop the corresponding dynamic monitoring schemes. However, measurement noise is widespread in real-world industrial processes, and ignoring its effect will lead to suboptimal modeling and monitoring performance. In this article, a probabilistic predictable feature analysis (PPFA) is proposed for multivariate time series modeling, and a multistep dynamic predictive monitoring scheme is developed. The model parameters are estimated with an efficient expectation–maximization algorithm, where the genetic algorithm and the Kalman filter are designed and incorporated. Furthermore, a novel dynamic statistical monitoring index, the dynamic index, is proposed as an important supplement of T2 and SPE to detect dynamic anomalies. The effectiveness of the proposed algorithm is demonstrated via its application on the three-phase flow facility and a medium-speed coal mill.

Process monitoring and fault diagnosis (PM-FD) of coal mills are essential to the security and reliability of the coal-fired power plant. However, traditional methods have difficulties in addressing the strong nonlinearity and multi-modality of coal mills. In this paper, a novel multi-mode Bayesian PM-FD method is proposed. Gaussian mixture model (GMM) is first applied to identify the operating modes of the coal mill. Subsequently, combined with multi-output relevance vector regression (MRVR), Bayesian inference is introduced to reconstruct and monitor the newly observed samples from different running modes. Additionally, the squared prediction error and the contribution plot method are employed for fault detection and isolation. The performance of the proposed PM-FD method is verified through its application in a self-defined nonlinear system and two actual fault cases of a medium-speed coal mill. Compared with the traditional methods, the experimental results demonstrate the effectiveness of the proposed method.