The sintering temperature (ST) is a critical index for condition monitoring and process control of coal-fired equipment and is widely used in the production of cement, aluminium, electricity, steel and chemicals. The accurate prediction of the ST is important for control systems to anticipate tragedies. In this paper, we propose a deep learning model for forecasting the ST using automatic spatiotemporal feature extraction from multivariate thermal time series. A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST. DCGNet uses convolutional neural networks (CNNs) and gated recurrent unit (GRU) to extract the local spatial-temporal dependency patterns among the multivariates, and another parallel GRU using historical ST data as input is incorporated to more accurately capture the dynamic characteristics of ST time series. Based on real-world data, application results show that the proposed approach has high forecasting accuracy and robustness, thus having broad application prospects in industrial processes.

Maintaining a normal burning temperature is essential to ensuring the quality of nonferrous metals and cement clinker in a rotary kiln. Recognition of the temperature condition is an important component of a temperature control system. Because of the interference of smoke and dust in the kiln, the temperature of the burning zone is difficult to be measured accurately using traditional methods. Focusing on blurry images from which only the flame region can be segmented, an image recognition system for the detection of the temperature condition in a rotary kiln is presented. First, the flame region is segmented employing a region-growing method with a dynamic seed point. Seven features, comprising three luminous features and four dynamic features, are then extracted from the flame region. Dynamic features constructed from luminous feature sequences are proposed to overcome the problem of mis-recognition when the temperature of the flame region changes rapidly. Finally, classifiers are trained to recognize the temperature state of the burning zone using its features. Experimental results using real datasets demonstrate that the proposed image-based systems for recognizing the temperature condition are effective and robust.

Monocular depth estimation has become one of the most studied topics in computer vision. Most approaches treat depth prediction as a fully supervised regression problem requiring vast amounts of corresponding ground-truth depth and image pairs for training. Unsupervised monocular depth estimation has emerged as a promising alternative that eliminates dataset limitations. This paper proposes an end-to-end unsupervised deep learning framework integrating attention blocks and multi-warp loss for monocular depth estimation. In this framework, to explore more general contextual information among the feature volumes, an attention block that sequentially refines the feature maps along the channel and spatial dimensions is inserted after the first and last stages of the network encoder. Additionally, to further utilize the errors in the original disparity estimation from the network, a novel multi-warp reconstruction strategy is designed for the loss function. The experimental results evaluated on the KITTI, CityScapes and Make3D datasets demonstrate the state-of-the-art performance and satisfactory generalization ability of our proposed method.

Monitoring and forecasting of sintering temperature (ST) is vital for safe, stable, and efficient operation of rotary kiln production process. Due to the complex coupling and time-varying characteristics of process data collected by the distributed control system, its long-range prediction remains a challenge. In this article, we propose a multivariate time series forecasting model based on dynamic spatio-temporal graph attention network (GAT) to model time-varying spatio-temporal correlation between the process data and perform long-range forecasting of ST. Aiming at the problem that there is no preset graph structure for multivariate data, we first propose an adaptive adjacency matrix generation algorithm to construct an elementary graph structure for the process data. Then, we design a spatio-temporal graph attention module, which consists of a multihead GAT for extracting time-varying spatial features and a gated dilated convolutional network for temporal features. Finally, considering the different time delay and rhythm of each process variable, we use dynamic system analysis to estimate the delay time and rhythm of each variable to guide the selection of dilation rates in dilated convolutional layers. The application results based on actual data show that the method has high prediction accuracy, and has broad application prospects in industrial processes.