Development of vibration style ladle slag detection methods and the key technologies

Tan, Dapeng; Ji, Shiming; Li, Peiyu; Pan, Xiaohong

doi:10.1007/s11431-010-4073-6

Cited by 49 publications

(20 citation statements)

References 34 publications

(33 reference statements)

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“…As mentioned above, ANNs have the capabilities of improved learning, associative memory, and parallel processing, and they are hence widely applied in the areas of automatic control [28][29][30], signal processing [31][32][33], and pattern recognition [34][35][36]. The neurons of ANNs are connected by weights and thresholds, and adaptive computing is used to preprocess external input information through the ANNs' nonlinear mapping ability until the output meets the expected values.…”

Section: Sa-ann-based Cognition Mechanismmentioning

confidence: 99%

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen

Tan

2018

Complexity

Self Cite

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Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNsbased cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing-artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.

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Section: Sa-ann-based Cognition Mechanismmentioning

confidence: 99%

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen

Tan

2018

Complexity

Self Cite

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“…For the timevarying objectives, the digital PID algorithms with selfadapting abilities are developed to improve the control accuracy [3][4][5][6][7]. Attaran et al put forward an energy efficiency optimization method for heating, ventilating, and air conditioning (HVAC) system by using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC).…”

Section: Introductionmentioning

confidence: 99%

A Self‐Tuning Proportional‐Integral‐Derivative‐Based Temperature Control Method for Draw‐Texturing‐Yarn Machine

Rong

Chen

2017

Mathematical Problems in Engineering

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Owing to the fast time-varying characteristics, the temperature control for draw-texturing-yarn (DTY) machine has higher technical difficulties and results in challenges for system energy optimization. To address the matter, a self-tuning proportionalintegral-derivative-(ST-PID-) based temperature control method is proposed. Referring to the technical procedures of DTY machine, a thermodynamic model is set up. Then, a ST-PID minimum phase control system is constructed by the pole-point placement method. Subsequently, an artificial neural network based forgetting factor searching (ANN-FFS) algorithm is developed to optimize the system parameter identification. The numerical cases show that the proposed ANN-FFS algorithm can improve the parameter identification process, and the average identifying efficiency ( > 15) can increase by more than 50%; compared with the fuzzy PID controller, the proposed ST-PID method can increase the control accuracy nearly 3 times for the static temperature ascending. The experimental results prove that the proposed ST-PID method has better abilities of characteristics tracing and antiinterference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.

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“…Vibratory SCDT working principle: vibration sensor is installed far away from molten steel, which does not cause any influence to CCP, and can solve the easy consumption problem of sensor at high temperature conditions. quality [1]. In allusion to this problem, slag carry-over detection technology (SCDT) is developed to detect the slag carry-over process and improve the purity of molten steel.…”

Section: Introductionmentioning

confidence: 99%

“…In the early 1980s, Swede first proposed a method that adopted electromagnetic coils to detect the slag [1]. In 1987, E. Julius developed a similar SCDT and obtained foundation support from the European Community [2].…”

Section: Introductionmentioning

confidence: 99%

“…0278-0046/$31.00 © 2012 IEEE Apparently, vibration detection method can overcome the inherent problems of other detection methods [8]. However, as an indirect method, there are some problems to be resolved: 1) because the shock force of steel stream is limited, the vibration signal is relatively weak and apt to be submerged by interferences; 2) the selection of characteristic parameters depends on experimental data and empirical rules, which will cause negative influence to working effectiveness; and 3) steel stream vibration signal is of distortion characteristics and high randomness that will make SCDT require long debugging time to satisfy the practical demands of CCP [1].…”

Section: Introductionmentioning

confidence: 99%

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SA-ANN-Based Slag Carry-Over Detection Method and the Embedded WME Platform

Tan

Li²,

et al. 2013

IEEE Trans. Ind. Electron.

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Slag carry-over detection technology (SCDT) is of important significance for steel continuous casting production (CCP), but has the problems with manufacture cost, service life, installation, and maintenance. Aiming at the problems, this paper brings forward a novel vibration style SCDT realization method based on simulated annealing artificial neural network (SA-ANN). According to ladle pouring process, the vibration signal of steel stream is regarded as the target signal for SCDT. Then, the time point of slag carry-over can be obtained in light of the vibration amplitude difference of pure molten steel and steel slag. Based on the fluid flow similarity principles, an embedded water model experiment (WME) platform is established. The WME platform can simulate the physical process of ladle pouring, reduce the system debugging time under formidable CCP field conditions, and improve the industrial suitability of SCDT. Using an improved SA-ANN algorithm, the status of steel stream is identified to realize automatic control for ladle pouring. WME simulated test results show that the slag detection accuracy (SDA) of this method can reach more 99%. CCP industrial field experiment proves that this method requires low cost and little rebuilding for the current CCP devices, and the practical SDA can reach more 96%.Index Terms-Embedded system, shock vibration, simulated annealing artificial neural network (SA-ANN), slag carry-over detection, water model experiment (WME).

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Development of vibration style ladle slag detection methods and the key technologies

Cited by 49 publications

References 34 publications

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

A Self‐Tuning Proportional‐Integral‐Derivative‐Based Temperature Control Method for Draw‐Texturing‐Yarn Machine

SA-ANN-Based Slag Carry-Over Detection Method and the Embedded WME Platform

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