Robust Principal Component Pursuit for Fault Detection in a Blast Furnace Process

Pan, Yijun; Yang, Chunjie; An, Ruqiao; Sun, Youxian

doi:10.1021/acs.iecr.7b03338

Cited by 36 publications

(15 citation statements)

References 29 publications

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“…In Figure 12, it is shown that ACF and PACF of #1 BF are terminated within the confidence band at 5 and 2 orders respectively. Therefore, the prototype of this energy model for #1 BF can be set as ARMA (2,5). 2000 samples of seven BFs have been captured from an ironmaking plant with a production of ten million tons per year.…”

Section: B Practical Preparation For Optimizationmentioning

confidence: 99%

“…In 2019, China has become the largest iron and steel products supplier in the world, accounting for more than 53.26% of the total crude steel production. Therefore, the modeling and optimizing of ironmaking process would have significant benefits in economic development and environmental protection [2].…”

Section: Introductionmentioning

confidence: 99%

“…Width represents the predicted coke ratio and radius represents the practical one. (c) Bottom plot is the out-of-sample prediction performance of ARMA(2,5).…”

mentioning

confidence: 99%

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A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

2020

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The expenses of pig iron manufacturing stay in a relatively high level due to the large consumption of coke and ore. There are a variety of methods to optimize the ironmaking process, including multi-indexes method, production-oriented approach, and cost control strategy. In order to reduce the production cost, we optimize the raw materials supply schedule of an ironmaking plant with five sintering machines and seven blast furnaces. The ironmaking plant used to fix the feeding system, leading to an inefficient operation and mismatched connection between these two procedures. At first, the physical characteristics of each blast furnace is captured by linear regression model. Then, a self-adaptive genetic algorithm with variable population size is constructed under distribution system. Finally, the improved genetic algorithm is applied to optimize the total production cost represented by the aggregation of seven coke ratios on a cloud computing platform. With the application of this cloud collaborative optimization framework, the mean coke ratio of the ironmaking factory has decreased 13.96kg/t Fe.

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Section: B Practical Preparation For Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

2020

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“…Shang et al [8] developed a recursive transformed component statistical analysis (RTCSA)-based algorithms to monitor incipiently happened faults in the iron-making process. In addition, other kinds of PCA-based approaches have been introduced to monitor process faults, such as robust PCA [9] and convex hull-based PCA [10].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Ouyang

Zeng

et al. 2020

Processes

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It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

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“…is a key unit. The blast furnace is a metallurgical furnace used in the manufacture of the industrial metals, and it works under high temperature and high pressure [6], [7]. The iron-making process consists of a blast furnace and five pieces of smelting auxiliary system equipment.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Method for Fault Detection in the Iron-Making Process

Yang

Pan

2020

IEEE Access

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Since the iron-making process is performed in complicated environments and controlled by operators, observation labeling is difficult and time-consuming. Therefore, unsupervised fault detection methods are a promising research topic. Recently, an unsupervised graph-based change point detection method has been introduced, and the graph of observations is constructed by the minimum spanning tree. In this paper, a novel fault detection method based on the graph for an iron-making process is proposed, and a weight calculation method for constructing the minimum spanning tree is introduced. The Euclidean distance and Mahalanobis distance are combined to calculate the weights in the minimum spanning tree, which contain important relations of variables. The distance calculation method is determined by the correlation coefficients of variables. Each testing observation is set as a change point candidate, and a change point candidate divides the observations into two groups. The number of a special type of edge in the minimum spanning tree is used as a fault detection statistic. That special edge connects two observations from two different groups. The minimum number of that type of edge corresponding to the change point candidate is a true change point. Finally, numerical simulation is used to test the power of the proposed method, and a real iron-making process including low stock, cooling, and slip faults is implemented to illustrate the effectiveness of fault detection in industrial processes.

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Robust Principal Component Pursuit for Fault Detection in a Blast Furnace Process

Cited by 36 publications

References 29 publications

A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

A Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

Fault Detection and Identification of Blast Furnace Ironmaking Process Using the Gated Recurrent Unit Network

Graph-Based Method for Fault Detection in the Iron-Making Process

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