A precision on-line model for the prediction of thermal crown in hot rolling processes

Jiang, Min; Li, Xuejun; Wu, Jigang; Wang, Guangbin

doi:10.1016/j.ijheatmasstransfer.2014.07.061

Cited by 29 publications

(16 citation statements)

References 13 publications

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“…Remark 2. From ( 29) and (30), it is easy to find that the online sequential implementation of the least-square solution ( 21) is similar to the recursive least-square (RLS) algorithm. Therefore, all the convergence proof of RLS can be extended to the proposed algorithm.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Li et al [29] proposed an incremental modeling method for DPSs, which refers to adding the hierarchical spatiotemporal kernels incremental algorithm for online learning. Jiang et al [30] proposed a precision online spatiotemporal model to predict the thermal crown in hot rolling processes, where a hybrid intelligent model given in state-space formulation is applied for online learning implementation. However, this method required the mathematical structure of the systems known.…”

Section: Introductionmentioning

confidence: 99%

“…However, they didn't consider the inherent system structure features. Though the online spatiotemporal model proposed by Jiang et al [30] is developed based on the PDEs of the hot rolling processes, the application of NN leads this online model to low computational efficiency. Furthermore, all the above methods cannot reflect the timeliness of online sequential training data well.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Xu¹,

Tan²,

Fan³

et al. 2021

IEEE Access

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Many industrial thermal processes are large-scale time-varying nonlinear distributed parameter systems (DPSs). To effectively model such systems, dual extreme learning machine based online spatiotemporal modeling with adaptive forgetting factor (AFFD-ELM) is proposed in this paper. This method can recursively update the parameters of the low-order temporal model by using newly arriving data under Karhunen-Loè ve (KL) based space/time separation. In this way, the time-varying dynamics can be tracked real-time very well as output data increases over time. Besides, since the training samples are usually timeliness, adaptive forgetting factor (AFF) is also embedded in this method to improve the online learning effects by adding a reasonable weight to previous data. This online learning strategy makes the process promising for online modeling under continuously samples environment. The proposed method is utilized for online temperature prediction of the curing oven. Simulation results verify the efficiency and viability of the online spatiotemporal model.

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Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Xu¹,

Tan²,

Fan³

et al. 2021

IEEE Access

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“…Therefore, the selection of spatial basis functions is critical to the spatiotemporal modeling performance. There are a wide variety of methods, such as the weighted residual method (WRM) [13], the finite-difference method (FDM) [14], [15], the finite-element method (FEM) [16], the spectral method (SM) [17], and the Karhunen-Loève (KL) method [18], [19]. The KL decomposition method has been widely used by lots of researchers, and it turns out to be a suitable and effective approach for data modeling [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network

Yang

et al. 2020

IEEE Access

160

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Estimation of absolute temperature distributions is crucial for many thermal processes in the nonlinear distributed parameter systems, such as predicting the curing temperature distribution of the chip, the temperature distribution of the catalytic rod, and so on. In this work, a spatiotemporal model based on the Karhunen-Loève (KL) decomposition, the multilayer perceptron (MLP), and the long short-term memory (LSTM) network, named KL-MLP-LSTM, is developed for estimating temperature distributions with a three-step procedure. Firstly, the infinite-dimensional model is transformed into a finitedimensional model, where the KL decomposition method is used for dimension reduction and spatial basis functions extraction. Secondly, a novel MLP-LSTM hybrid time series model is constructed to deal with the two inherently coupled nonlinearities. Finally, the spatiotemporal temperature distribution model can be reconstructed through spatiotemporal synthesis. The effectiveness of the proposed model is validated by the data from a snap curing oven thermal process. Satisfactory agreement between the results of the current model and the other well-established model shows that the KL-MLP-LSTM model is reliable for estimating the temperature distributions during the thermal process. INDEX TERMS Spatiotemporal modeling, nonlinear distributed thermal processes, Karhunen-Loève decomposition, multilayer perceptron, long short-term memory.

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“…grooves) [9][10][11][12] • using different rolling regimes. 2,[13][14][15][16] Though there are several well-known mathematical models for roll wear (i.e., Archard, Yasada, Lim and Ashby, Sibakin, Oike, Somers, Tong and Chakko), 1,3 but none of them can be used practically in an industrial environment, where the specifics of several different steel grades and where different rolling regimes are produced and bound to delivery dates, come into play.…”

Section: Introductionmentioning

confidence: 99%

Roll wear modeling using genetic programming – industry case study

Kovačić¹,

Mihevc²,

Terčelj³

2019

Mater. Tehnol.

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Using the continuous rolling line (10 stands-6 horizontal, 4 vertical), all the rolled dimensions, including round (more than 80 nominal diameters), flat (more than 650 shapes and dimensions) and square bars (13 different sizes), can be rolled each month. The purpose of the research was to identify the parameters affecting the working roll wear in the hot-rolling process. For this purpose, we collected data during the 2013 annual production on the first stand of the continuous roll mill for rolling of diameters from f20 mm to f58 mm for which data of the groove shape and surface, roll diameter, contact time, carbon equivalent, rolling temperature and quantity of the rolled material are available. After roll wear-out they are machined using a turning operation. The root cause "why the rolls were machined" was not collected. To evaluate the roll wear-out, the quantity of rolled material before the machining of rolls was used. Prediction of the quantity of rolled material before the machining of rolls was conducted using linear regression and genetic programming. The developed models were validated using the data from 2014. The validation showed that in the case of excluding the fatigue cracks from collected data the prediction could be improved drastically. The results of the research can be used in practice for predicting roll wear and consequently roll maintenance on the basis of rolling schedule quantities.

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A precision on-line model for the prediction of thermal crown in hot rolling processes

Cited by 29 publications

References 13 publications

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network

Roll wear modeling using genetic programming – industry case study

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