Skip to main content

Advertisement

SpringerLink
Log in

Furnace operation optimization with hybrid model based on mechanism and data analytics

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is difficult to establish a precise process model for the OOPFP. In this paper, firstly, a novel hybrid modeling method combining temperature mechanism with data analytics is proposed to map the relationship of the temperature variables in the furnace process more accurately. The main idea of the hybrid modeling is to compensate for the deviation of original temperature mechanism model through the least squares support vector machine. Then, an improved differential evolution (DE) with feasible region dynamic adjustment strategy and population size gradual shrinking strategy is proposed to solve the OOPFP under hybrid model. Finally, extensive numerical experiments are carried out to demonstrate the effectiveness and accuracy of the hybrid model, and evaluate the performance of the improved DE on solving OOPFP. Experimental results show that the hybrid model is more precise than the original mechanism one, and the proposed DE outperforms other representative algorithms, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Axelberg PGV, Gu YH, Bollen MHJ (2007) Support vector machine for classification of voltage disturbances. IEEE Trans Power Deliv 22(3):1297–1303

    Article  Google Scholar 

  • Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518

    Article  Google Scholar 

  • Cao LL, Sun YP, Wu HY (2011) Grey-box modeling based on fundamental genes for nonlinear system. J Central South Univ Sci Technol 42(2):414–418

    Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  • Dong Y, Zhao R (2017) Solve train stowage planning problem of steel coil using a pointer-based discrete differential evolution. J Prod Res, Int. https://doi.org/10.1080/00207543.2017.1413260

    Google Scholar 

  • Dong B, Li ZX, Rahman SMM, Vega R (2016) A hybrid model approach for forecasting future residential electricity consumption. Energy Build 117:341–351

    Article  Google Scholar 

  • El-Naqa I, Yang YY, Wernick MN et al (2002) A support vector machine approach for detection of microcalcifications in mammograms. In: IEEE international conference on image processing, Nishikawa, pp 953–956

  • Guo XC, Yang JH, Wu CG et al (2008) A novel LS-SVMS hyper-parameter selection based on particle swarm optimization. Neurocomputing 71:3211–3215

    Article  Google Scholar 

  • Li QY, Zhou H, Lin AP et al (2011) Prediction of ash fusion temperature based on grid search and support vector machine. J Zhejiang Univ Sci A 45(12):2181–2187

    Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. In: 2014 IEEE Congress on Evolutionary Computation.

  • Lin SW, Ying KC et al (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35:1817–1824

    Article  Google Scholar 

  • Liu DW, Lin XH, Ghosh D (2007) Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 63:1079–1088

    Article  MathSciNet  MATH  Google Scholar 

  • Luo N, Li N, Qian F (2007) Support vector machine and mechanism model hybrid modeling method and its application to polyester production process. Polyest Ind 20(2):12–18

    Google Scholar 

  • Mao WT, Yan GR et al (2011) Model selection for least squares support vector regressions based on small-word strategy. Expert Syst Appl 38:3227–3237

    Article  Google Scholar 

  • Mathias MA, Mohamed C, Alain B (2011) Semisupervised learning using Bayesian interpretation: application to LS-SVM. IEEE Trans Neural Netw 22(4):513–524

    Article  Google Scholar 

  • Michalak M (2011) Adaptive kernel approach to the time series prediction. Pattern Anal Appl 14(3):283–293

    Article  MathSciNet  Google Scholar 

  • Pajares G, Cruz JMDL (2004) On combining support vector machines and simulated annealing in stereovision matching. IEEE Trans Syst Man Cybern Part B Cybern 34(4):1646–1657

    Article  Google Scholar 

  • Rong JG, Chen F (2005) Data fusion for nutrient solution components detection based on SVM. Comput Simul 22(11):110–112

    Google Scholar 

  • Storn R, Price K (1995) A simple and efficient adaptive scheme for global optimization over continuous spaces. In: International computer science institute, Berkeley, CA, Technical Report TR–95–012

  • Suykens JAK (2001) Nonlinear modelling and support vector machines. In: IEEE instrumentation and measurement technology conference, pp 287–294

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  • Tang ZH, Yang Y (2014) Two-stage particle swarm optimization-based nonlinear model predictive control method for reheating furnace process. ISIJ Int 54(8):1836–1842

    Article  Google Scholar 

  • Tang LX, Zhao Y, Liu JY (2014) An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Trans Evol Comput 18(2):209–225

    Article  Google Scholar 

  • Tang LX, Dong Y, Liu JY (2015) Differential evolution with an individual-dependent mechanism. IEEE Trans Evol Comput 19(4):560–574

    Article  Google Scholar 

  • Tang LX, Wang XP, Dong ZM (2018) Adaptive multi-objective differential evolution with reference axis vicinity mechanism. IEEE Trans Cyber. https://doi.org/10.1109/tcyb.2018.2849343

    Google Scholar 

  • Tian HX, Mao ZZ, Wang Y (2008) Hybrid modeling of molten steel temperature prediction in LF. ISIJ Int 48(1):58–62

    Article  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  • Wang XP, Tang LX (2016) An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization. Inf Sci 348:124–141

    Article  Google Scholar 

  • Wang XF, Chen JD, Liu C, Pan F (2010) Hybrid modeling of penicillin fermentation process based on least square support vector machine. Chem Eng Res Des 88:415–420

    Article  Google Scholar 

  • Xia Q, Wang XP, Tang LX (2016) Operation optimization of slab reheating process based on differential evolution. ISIJ Int 56(11):2006–2015

    Article  Google Scholar 

  • Xing J, Jiang AN et al (2010) On the DE-SVM-based forecast of rock stratum drillability. J Northeast Univ (Nat Sci) 31(9):1345–1348

    Google Scholar 

  • Xu G, Yu H et al (2005) Modeling fermentation process based on hybrid support vector machine-kinetics mechainsm. J Chem Ind 56(4):653–658

    Google Scholar 

  • Xu CJ, Huang H, Ye SJ (2014) A differential evolution with replacement strategy for real-parameter numerical optimization. In: IEEE congress on evolutionary computation, Beijing, July 6–11

  • Zhao ZW, Yang JM et al (2016) A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems. Eur J Oper Res 250:30–45

    Article  MathSciNet  MATH  Google Scholar 

  • Zhou LL, Zheng ZG et al (2009) Research on work roll wear model based on combination of mechanics and work condition about four-high tandem cold mill. J Yanshan Univ 33(4):284–293

    Google Scholar 

Download references

Funding

This research is supported by the National Key Research and Development Program of China (2016YFB0901900), the Major Program of National Natural Science Foundation of China (71790614), the Fund for Innovative Research Groups of the National Natural Science Foundation of China (71621061), the Major International Joint Research Project of the National Natural Science Foundation of China (71520107004), the Fund for the National Natural Science Foundation of China (61374203, 61573086), and the 111 Project (B16009).

Author information

Authors and Affiliations

  1. Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry, Northeastern University, Shenyang, 110819, China

    Qiong Xia

  2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Qiong Xia

  3. Liaoning Key Laboratory of Manufacturing System and Logistics, Northeastern University, Shenyang, 110819, China

    Xianpeng Wang

  4. Institute of Industrial and Systems Engineering, Northeastern University, Shenyang, 110819, China

    Lixin Tang

Authors
  1. Qiong Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianpeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lixin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xianpeng Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Q., Wang, X. & Tang, L. Furnace operation optimization with hybrid model based on mechanism and data analytics. Soft Comput 23, 9551–9571 (2019). https://doi.org/10.1007/s00500-018-3519-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3519-9

Keywords

Search

Navigation