Expert Systems Controlling the Iron Making Process in Closed Loop Operation

Klinger, Angelika; Kronberger, Thomas; Schäler, Martin; Schürz, Bernhard; Stohl, Klaus

doi:10.5772/7075

Cited by 14 publications

(5 citation statements)

References 3 publications

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“…4,8,10,11,13,15,16,18, and 21 and part of No. 12 and 17 are employed as the testing set, and Abnormalities No.…”

Section: Resultsmentioning

confidence: 99%

“…1, the classification procedure is implemented online, i.e., for a newly arriving sample  x t with unknown labels, energy-based classification considers it as an extra training sample and computes the loss func-tion J for each possible label y t with the optimal matrix M that is obtained by solving the SDP (8). Then, the unlabeled sample is classified by label y optimal if it minimizes the total loss function, 29) i.e.,…”

Section: )mentioning

confidence: 99%

“…[5][6][7][8] However, the requirements of comprehensive rules and sufficient historical information about abnormalities, which are not always available, limit the application of these systems in many factories.…”

Section: Introductionmentioning

confidence: 99%

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PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Zhang

Hai-feng³

et al. 2016

ISIJ Int.

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Fault detection and fault classification are important in the modern ironmaking process. Some studies based on principal component analysis (PCA) techniques have been performed for fault detection in the ironmaking process. However, studies on fault classification in the ironmaking process remain limited. In this paper, problems that are related to the classification of abnormalities in blast furnaces are considered. We fuse historical abnormal data that were collected from three real blast furnaces to address the problem of insufficient historical faulty data. To extract common features for the same type of abnormalities, which are not affected by different operation points or different blast furnaces, we propose the use of a contribution vector as a fault feature, which is calculated by the PCA-based technique. The large marginal nearest neighbor (LMNN) technique is employed to train a classifier with contribution vectors as inputs. Twentyone historical abnormalities in three different real blast furnaces are employed to validate the proposed method. The results indicate that this method achieves the desired performance.

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“…4,8,10,11,13,15,16,18, and 21 and part of No. 12 and 17 are employed as the testing set, and Abnormalities No.…”

Section: Resultsmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

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PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Zhang

Hai-feng³

et al. 2016

ISIJ Int.

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“…The PAS automates, optimizes, and tracks different stages of the metallurgical production process. It comprises systems for process automation of iron production, i.e., melting iron ore and raw materials to produce iron [13], steel production, i.e., refining liquid iron and other materials to produce steel, as well as continuous casting, i.e., casting liquid steel into solid steel slabs [8]. These independently developed systems size up to several million LoC.…”

Section: Monitoring An Industrial Sos: Variability-related Challengesmentioning

confidence: 99%

Variability Management for a Runtime Monitoring Infrastructure

Rabiser

Vierhauser

Grünbacher

2015

Proceedings of the Ninth International Workshop on Variability Modelling of Software-Intensive Systems

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Many software systems today are systems of systems (SoS), which are difficult to analyze due to their size, complexity, heterogeneity, and variability. For instance, unexpected behavior of SoS is often caused by the complex interactions between the involved systems and their environment at runtime. Monitoring infrastructures (MIs) provide support for engineers and support staff analyzing the behavior of SoS during development and operation. Variability plays an important role in MIs, however, while some approaches exist, managing variability of MIs remains challenging. In this paper, we describe how we applied a variability management approach to support the reconfiguration of a SoS monitoring infrastructure (MI) at runtime. Our approach provides configuration support for setting up the MI to reflect system variability. It also supports runtime reconfiguration of the MI to reflect the different monitoring tasks of users and to support evolution. We motivate our work using the case of monitoring a realworld SoS from the domain of industrial automation and discuss variability-related challenges in four monitoring scenarios. We evaluate the feasibility of our approach by applying it to these scenarios. We also demonstrate that our approach reduces manual reconfiguration effort and helps to reduce the overhead of the MI.

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“…The reasonable and stable state of sintering process is the guarantee of improving the quality and yield indices of the sinter. There are many literatures about the state control of sintering process, such as the sintering process control system of sintering process based on the principle of artificial intelligence that developed by Japanese in 1980s, [3][4][5] the sintering integrated optimisation system based on expert system that developed by Siemens and VAI in 1990s, 6,7 and the state optimisation control system of sintering process that developed by Central South University. [8][9][10][11] In addition, some scholars have done some research on the control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion

Fan

Huang

Chen

et al. 2016

Ironmaking & Steelmaking

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The process state of iron ore sintering is characterised via the bed permeability, the temperature rising point, the burn rising point and the burn through point. The intelligent control strategy of sintering process based on fan frequency conversion is put forward. The stability of process state is firstly evaluated based on the knowledge of domain experts. If process state is fluctuating, an expert controller is activated to realise a normal production. If process state becomes steady, then a fuzzy controller is activated to obtain the optimisation of process state and reduce the power consumption of main exhaust fan. The closed-loop control of sintering production showed that the system can realise the optimisation of sintering process state, and reduce the energy consumption of sintering production while ensure the stability of sinter quality indices, it theoretically saves power consumption of 1×10 4 kWh every day.

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Expert Systems Controlling the Iron Making Process in Closed Loop Operation

Cited by 14 publications

References 3 publications

PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Variability Management for a Runtime Monitoring Infrastructure

Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion

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