System Identification of Blast Furnace Processes with Genetic Programming

Kronberger, Gabriel; Feilmayr, Christoph; Kommenda, Michael; Winkler, Stephan; Affenzeller, Michael; Bürgler, Thomas

doi:10.1109/lindi.2009.5258751

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…The acoustic measurements are carried out inside a fully operational blast furnace as shown in Fig. 9 at voestalpine Stahl Linz GmbH [35]. A total of 10 acoustic transducers are placed on the circumference of the blast furnace.…”

Section: B Tomographic Reconstruction Of the Temperature Inside A Bla...mentioning

confidence: 99%

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Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

bhuvana¹,

Och²,

Schuster³

2022

Preprint

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<p>A typical time-of-flight (ToF) tomography method requires accurate information about the system’s calibration parameters in order to reconstruct the features of interest within a region of interest (RoI). This paper proposes a constrained joint calibration and tomography (CJCT) method based on a separable least squares (SLS) approach for ToF tomographic systems. Contrary to the existing offline and online calibration methods, the proposed method utilizes prior information about the calibration parameters in the form of constraints. The imposed constraints allow for an efficient search for the minimum of the inherent SLS cost function by reducing the system and measurement ambiguities. Thus, the proposed CJCT method can find the optimum values for both tomography and calibration parameters more efficiently compared to unconstrained methods. The general applicability of the proposed method is demonstrated by evaluating it on two applications: tomographic reconstruction of the permittivity distribution in a RoI using ToF measurements from a high resolution millimeter-wave radar system, and the temperature distribution inside an industrial blast furnace using measured and simulated acoustic ToF values. The simulation and experimental results show that the proposed method performs significantly better in terms of tomographic reconstruction accuracy compared to unconstrained methods.</p>

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Section: B Tomographic Reconstruction Of the Temperature Inside A Bla...mentioning

confidence: 99%

“…The red circles and black squares represent transmitter and receiver positioning at each transducer, respectively. Refer to [13], [35], [36] for complete application details. then asymptotically reaches the MAE T of the TLLS method.…”

Section: B Tomographic Reconstruction Of the Temperature Inside A Bla...mentioning

confidence: 99%

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

bhuvana¹,

Och²,

Schuster³

2022

Preprint

View full text Add to dashboard Cite

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“…In [8] as well as in [6] first modeling results with symbolic regression have been presented concerning identified models of the blast furnace and the temper mill. Unlike [8,6] this paper focuses on three adaptations to a symbolic regression system and their effect when used in combination. Section 2 describes the used symbolic regression system and its adaptations, whereas Section 3 states the concrete parameter settings, implementation details and the content of the two datasets.…”

Section: Introductionmentioning

confidence: 99%

Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets

Kommenda

Kronberger

Feilmayr

et al. 2012

Computer Aided Systems Theory – EUROCAST 2011

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Abstract. This work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R 2 is used as fitness function instead of the mean squared error and offspring selection is used to ensure a steady improvement of the achieved solutions. Additionally, as the fitness evaluation consumes most of the execution time, the generated solutions are only evaluated on parts of the training data to speed up the whole algorithm. These three algorithmic adaptations are incorporated in the symbolic regression algorithm and their impact is tested on two real world datasets describing a blast furnace and a temper mill process. The effect on the achieved solution quality as well as on the produced models are compared to results generated by a symbolic regression algorithm without the mentioned modifications and the benefits are highlighted.

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“…In the literature, the efficiency of many systems such as blast furnace process [42], grinding process [43], drilling process [44,45], milling process [46], spinning process [9], vibratory finishing process [47], turning process [48], online fault diagnosis (FD) [49][50][51], software reliability [52], chemical reactions [53]…”

Section: Inappropriate Measure Of Complexity Of Mggp Modelmentioning

confidence: 99%

Modelling of manufacturing processes by a computational intelligence approach

Garg¹

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Modelling is a term widely used in System Identification (SI), which is referred to as the art and science of building mathematical models of systems using some measured data. The systems of interest in this thesis are additive manufacturing processes such as fused deposition modelling, machining processes such as turning, and finishing processes such as vibratory finishing. These processes comprise multiple input and output variables, making their operating mechanisms complex. In addition, it can be costly to obtain the process data and therefore there is a strong need for effective and efficient ways of modelling these systems. The models developed for a system can help to reveal hidden

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System Identification of Blast Furnace Processes with Genetic Programming

Cited by 3 publications

References 17 publications

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets

Modelling of manufacturing processes by a computational intelligence approach

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