Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process

Bashista Kumar Mahanta, Nirupam Chakraborti

Department of Metallurgical and Materials Engineering Indian Institute of Technology, Kharagpur, India.

DOI:

https://doi.org/10.7494/cmms.2021.3.0733

Abstract:

Optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).

Cite as:

Mahanta, B. K., & Chakraborti, N. (2021). Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process. Computer Methods in Materials Science, 31(3), 163-175. https://doi.org/10.7494/cmms.2021.3.0733

Article (PDF):

Keywords:

Deep Learning, Reference vector, Neural net, Genetic programming, Blast furnace

References:

Adema, A.T. (2014). DEM-CFD modelling of the iron making blast furnace. [Doctoral thesis, Delft University of Technology], https://doi.org/10.4233/uuid:f8cd8841-7f88-4c29-86a5-2a1c58021d96.

Agarwal, A., Tewary, U., Pettersson, F., Das, S., Saxén, H., & Chakraborti, N. (2010). Analyzing blast furnace data using evolutionary neural network and multi objective genetic algorithm. Ironmaking and Steelmaking, 37(5), 353–359.

Brännbacka, J., & Saxén, H. (2001). Modeling the liquid levels in the blast furnace hearth. ISIJ International, 41(10), 1131–1138.

Castro, J.A., de, Nogami, H., & Yagi, J.I. (2002). Three dimensional multiphase mathematical modelling of the based on multi-fluid model. ISIJ International, 42(1), 44–52.

Cheng, R., & Jin, Y., Olhofer, M., & Sendhoff, B. (2016). A reference vector guided evolutionary algorithm for many-objective optimizations. IEEE Transactions on Evolutionary Computation, 20(5), 773–791.

Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2016). A surrogate assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, 22(1), 129–142.

Chugh, T., Chakraborti, N., Sindhya, K., & Jin, Y. (2017). A data-driven surrogate-assisted evolutionary algorithm applied to many-objective blast furnace optimization problems. Materials and Manufacturing Processes, 32(10), 1172–1178.

Dong, X.F., Pinson, D., Zhang, S.J., Yu, A.B., & Zulli, P. (2006). Gas-powder flow in blast furnace with different shape of cohesive zone. Applied Mathematical Modeling, 30(11), 1293–1309.

Fabian, T. (1958). A linear programming model of integrated iron and steel production. Management Science, 4(4), 415–441.

Gao, C., Jian, L., Liu, X., Chen, J., & Sun, Y. (2011a). Data-driven modeling based on volterra series for multidimensional blast furnace system. IEEE Transactions on Neural Networks, 22(12), 2272–2283.

Gao, C., Jian, L., & Luo, S. (2011b). Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Transactions on Industrial Electronics, 59(2), 1134–1145.

Gao, C., Ge, Q., & Jian, L. (2013). Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making. IEEE Transactions on Fuzzy Systems, 22(3), 586–596.

Geerdes, M., Chaigneau, R., Kurunov, J., Lingiardi, O., & Ricketts, J. (2015). Modern blast furnace iron making an introduction. IOS Press, Delft University Press.

Giri, B.K., Pettersson, F., Saxén, H., & Chakraborti, N. (2013). Genetic programming evolved through Bi-objective algorithms applied to a blast furnace. Materials and Manufacturing Processes, 28(7), 776–882.

Gujarathi, A.M., & Babu, B.V. (2016). Evolutionary computation. Techniques and application. CRC Press.

Hatano, M., & Kurita, K.A. (1982). Mathematical model of blast furnace with radial distribution of gas flow, heat transfer and reaction considered. Transaction of the Iron and Steel Institute of Japan, 22(6), 448–456.

Helle, M., Pettersson, F., Chakraborti, N., & Saxén, H. (2006). Modelling noisy blast furnace data using genetic algorithms and neural networks. Steel Research International, 77(2), 75–81.

Hodge, B.M., Pettersson, F., & Chakraborti, N. (2006). Re‐evaluation of the optimal operating conditions for the primary end of an integrated steel plant using multi‐objective genetic algorithms and nash equilibrium. Steel Research International, 77(7), 459–461.

Jiménez, J., Mochón, J., Ayala, J.S., de, & Obeso, F. (2004). Blast furnace hot metal temperature prediction through neural networks-based models. ISIJ International, 44(3), 573–580.

Kilpinen, A. (1988). An on line model for estimating the melting zone in a blast furnace. Chemical Engineering Science, 43(8), 1813–1818.

Li, M., Zhen, L., & Yao, X. (2017). How to read many-objective solutions sets in parallel coordinates [Educational Forum]. IEEE Computational Intelligence Magazine, 12(4), 88–100.

Mahanta, B.K., & Chakraborti, N. (2018). Evolutionary data driven modeling and multi objective optimization of noisy data set in blast furnace iron making process. Steel Research International, 89(9), 1–11.

Mahanta, B.K., & Chakraborti, N. (2019). Evolutionary computation in blast furnace iron making. In Datta, S., & Davim, J.P. (Eds.), Optimization in industry (pp. 211–252), Springer.

Mahanta, B.K., & Chakraborti, N. (2020). Tri-objective optimization of noisy dataset in blast furnace iron-making process using evolutionary algorithms, Materials and Manufacturing Processes, 35(6), 677–686.

Mitra, T., & Saxén, H. (2014). Evolution of charging programs for achieving required gas temperature profile in a blast furnace. Materials and Manufacturing Processes, 30(4), 474–487.

Mitra, T., Pettersson, F., Saxén, H., & Chakraborti, N. (2016). Blast furnace charging optimization using multi objective evolutionary and genetic algorithms. Materials and Manufacturing Processes, 32(10), 1179–1188.

Mondal, D.N., Sarangi, K., Petterson, F., Sen, P.K., Saxén, H., & Chakraborti, N. (2011). Cu–Zn separation by supported liquid membrane analyzed through multi objective genetic algorithms. Hydrometallurgy, 107(3–4), 112–123.

Nath, N.K. (2002). Simulation of gas flow in blast furnace for different burden distribution and cohesive zone shape. Materials and Manufacturing Processes, 17, 671–681.

Omori, Y. (1987). Blast furnace phenomenon and modeling. Elsevier.

Pettersson, F., Chakraborti, N., & Saxén, H. (2007). A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Applied Soft Computing, 7(1), 387–397.

Pettersson, F., Biswas, A., Sen, P., Saxén, H., & Chakraborti, N. (2009). Analyzing leaching data for low-grade manganese ore using neural nets and multi objective genetic algorithms. Materials and Manufacturing Processes, 24(3), 320–330.

Rist, A., & Meysson, N. (1967). A dual representation of the blast furnace mass and heat balance. Journal of Metals, 19, 50–59.

Roy, S., & Chakraborti, N. (2020). Development of an evolutionary deep neural net for materials research. In TMS 2020 149th Annual Meeting & Exhibition. Supplemental Proceedings (pp. 817–828), Springer International Publishing.

Roy, S., Saini, B.S., Chakrabati, D., & Chakraborti, N. (2020). Mechanical properties of micro-alloyed steels studied using an evolutionary deep neural network. Materials and Manufacturing Processes, 35(6), 611–624.

Saxén, H., Gao, C., & Gao, Z. (2012). Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace – A review. IEEE Transactions on Industrial Informatics, 9(4), 2213–2225.

Spirin, N.A., Lavrov, V., Rybolovlev, A.V., Krasnobaev, A.V., & Pavlov, A.V. (2016). Use of contemporary information technology for analyzing the blast furnace process. Metallurgist, 60(5–6), 471–477.

The white book of steel (2012). World Steel Association.

Zhou, Z., Zhu, H., Yu, A., Wright, B., Pinson, D., & Zulli, P. (2005). Discrete particle simulation of solid flow in a model blast furnace. ISIJ International, 45(12), 1828–1837.

Zhou, P., Song, H., Wang, H., & Chai, T. (2016). Data-driven nonlinear subspace modeling for prediction and control of molten iron quality indices in blast furnace ironmaking. IEEE Transactions on Control Systems Technology, 25(5), 1761–1774.