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.

Finding the densest subgraph in a single graph is a fundamental problem that has been extensively studied. In many emerging applications, there exist dual networks. For example, in genetics, it is important to use protein interactions to interpret genetic interactions. In this application, one network represents physical interactions among nodes, for example, protein--protein interactions, and another network represents conceptual interactions, for example, genetic interactions. Edges in the conceptual network are usually derived based on certain correlation measure or statistical test measuring the strength of the interaction. Two nodes with strong conceptual interaction may not have direct physical interaction. In this article, we propose the novel dual-network model and investigate the problem of finding the densest connected subgraph (DCS), which has the largest density in the conceptual network and is also connected in the physical network. Density in the conceptual network represents the average strength of the measured interacting signals among the set of nodes. Connectivity in the physical network shows how they interact physically. Such pattern cannot be identified using the existing algorithms for a single network. We show that even though finding the densest subgraph in a single network is polynomial time solvable, the DCS problem is NP-hard. We develop a two-step approach to solve the DCS problem. In the first step, we effectively prune the dual networks, while guarantee that the optimal solution is contained in the remaining networks. For the second step, we develop two efficient greedy methods based on different search strategies to find the DCS. Different variations of the DCS problem are also studied. We perform extensive experiments on a variety of real and synthetic dual networks to evaluate the effectiveness and efficiency of the developed methods.

Metamaterials, characterized by unique structures, exhibit exceptional properties applicable across various domains. Traditional methods like experiments and finite-element methods (FEM) have been extensively utilized to characterize these properties. However, exploring an extensive range of structures using these methods for designing desired structures with excellent properties can be time-intensive. This paper formulates a machine-learning-based approach to expedite predicting effective metamaterial properties, leading to the discovery of microstructures with diverse and outstanding characteristics. The process involves constructing 2D and 3D microstructures, encompassing porous materials, solid–solid-based materials, and fluid–solid-based materials. Finite-element methods are then employed to determine the effective properties of metamaterials. Subsequently, the Random Forest (RF) algorithm is applied for training and predicting effective properties. Additionally, the Aquila Optimizer (AO) method is employed for a multiple optimization task in inverse design. The regression model generates accurate estimation with a coefficient of determination higher than 0.98, a mean absolute percentage error lower than 0.088, and a root mean square error lower than 0.03, indicating that the machine-learning-based method can accurately characterize the metamaterial properties. An optimized structure with a high Young’s modulus and low thermal conductivity is designed by AO within the first 30 iterations. This approach accelerates simulating the effective properties of metamaterials and can design microstructures with multiple excellent performances. The work offers guidance to design microstructures in various practical applications such as vibration energy absorbers.

Nitrogen oxides (NOx) are among the primary pollutants emitted by coal-fired power plants. Accurate prediction of NOx concentrations at the boiler outlet is crucial for optimizing unit control and reducing emissions. This study introduces a data-driven NOx emissions prediction methodology based on a multilayered Gradient Boosting Decision Tree (mGBDT) framework. Initially, Kernel Independent Component Analysis (KICA) is employed to eliminate nonlinear correlation among collected auxiliary variables. Subsequently, high-quality variables, grounded in physical mechanisms, are integrated with the extracted independent features. The robust Gaussian Mixture Model (RGMM) is then applied to capture the intrinsic multimode operational characteristics from these integrated features. Finally, local mGBDT-based NOx emissions prediction models are developed for each identified mode. The optimal hyperparameters for each local model are determined using Particle Swarm Optimization (PSO) and 10-fold cross-validation techniques. Utilizing historical measurements from the studied boiler, the proposed framework achieved a square of correlation coefficient (R 2) of 0.947, a root-mean-square error (RMSE) of 6.09 mg/m3, and a mean absolute error (MAE) of 4.009 mg/m3, outperforming five comparison models.