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Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace

  • Deynier Montero Góngora EMAIL logo , Jo Van Caneghem , Dries Haeseldonckx , Ever Góngora Leyva , Mercedes Ramírez Mendoza and Abhishek Dutta EMAIL logo
Published/Copyright: July 31, 2020
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International Journal of Chemical Reactor Engineering
From the journal International Journal of Chemical Reactor Engineering Volume 18 Issue 7

Abstract

In a nickel-producing multiple hearth furnace, there is a problem associated to the automatic operation of the temperature control loops in two of the hearths, since the same flow of air is split into two branches. A neural model of the post-combustion sub-process is built and served to increase the process efficiency of the industrial furnace. Data was taken for a three-months operating time period to identify the main variables characterizing the process and a model of multilayer perceptron type is built. For the validation of this model, process data from a four-months operating time period in 2018 was used and prediction errors based on a measure of closeness in terms of a mean square error criterion measured through its weights for the temperature of two of the hearths (four and six) versus the air flow to these hearths. Based on a rigorous testing and analysis of the process, the model is capable of predicting the temperature of hearth four and six with errors of 0.6 and 0.3 °C, respectively. In addition, the emissions by high concentration of carbon monoxide in the exhaust gases are reduced, thus contributing to the health of the ecosystem.

Keywords: artificial neural network; auto-correlation function; dynamic characteristics; multi-hearth furnace; post-combustion
Corresponding authors: Deynier Montero Góngora, Universidad de Moa, Holguín, Cuba, E-mail: ; and Abhishek Dutta, KU Leuven, Campus Groep T, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium, E-mail:

Funding source: Vlaamse Interuniversitaire Raad

Award Identifier / Grant number: 10/04/2015–31/12/2018

Acknowledgments

The authors acknowledge the VLIR-UOS project “A Cuban network of cleaner production (CP) centres and strengthening education and research on CP” (10/04/2015–31/12/2018).

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was funded by Vlaamse Interuniversitaire Raad (no. 10/04/2015–31/12/2018).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Aldrich, C., J. S. J. Van Deventer, and M. A. Reuter. 1994. “The Application of Neural Nets in the Metallurgical Industry.” Minerals Engineering 7: 5–6, https://doi.org/10.1016/0892-6875(94)90107-4.Search in Google Scholar

Angulo, M. 1982. Identificación y control extremal de un horno de reducción. In Praga: ČVUT (Universidad Técnica Superior de Praga).Search in Google Scholar

Angulo, H., P. Terencio, A. Legra, and L. Videaux. 2017. “Análisis especiales en un horno de reducción de níquel a escala de Planta Piloto.” Tecnología Química XXXVII: 3.Search in Google Scholar

Behrooz, F., N. Mariun, M. Hamiruce, M. Amran, and I. Rahman. 2018. “Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy.” Energies 11: 495, https://doi.org/10.3390/en11030495.Search in Google Scholar

Benardos, P. G., and G. C. Vosniakos. 2007. “Optimizing Feedforward Artificial Neural Network Architecture.” Engineering Applications of Artificial Intelligence 20: 3, https://doi.org/10.1016/j.engappai.2006.06.005.Search in Google Scholar

Beyers, L., A. Dutta, S. Lahiri, B. Blanpain, and F. Verhaeghe. 2015. “Hybrid Artificial Neural Network and Genetic Algorithm Modelling of Slag Properties.” In Proceedings European Metallurgical Conference 2015, vol. 2, 1071–85. Düsseldorf, Germany: Clausthal-Zellerfeld: GDMB Verlag.Search in Google Scholar

Birk, W. 2014. “Intelligent Industrial Processes – Automatic Control Perspective.” In Technical report, Luleå University of Technology.Search in Google Scholar

Blasco, J. A., N. Fueyo, C. Dopazo, and J. Ballester. 1998. “Modelling the Temporal Evolution of a Reduced Combustion Chemical System with an Artificial Neural Network.” Combustion and Flame 113: 1–2, https://doi.org/10.1016/s0010-2180(97)00211-3.Search in Google Scholar

Castellanos, J., R. Casto, and I. García. 1986. “Elaboración de minerales oxidados de níquel por el esquema carbonato amoniacal.” Minería & Geología 4: 2.Search in Google Scholar

Cavalcanti, F. M., M. Schmal, R. Giudici, and R. M. Brito. 2019. “A Catalyst Selection Method for Hydrogen Production through Water-Gas Shift Reaction Using Artificial Neural Networks.” Journal of Environmental Management 237, https://doi.org/10.1016/j.jenvman.2019.02.092.Search in Google Scholar PubMed

Chen, J., J. Liu, Y. He, L. Huang, S. Sun, J. Sun, K. Chang, J. Kuo, S. Huang, and X. Ning. 2017. “Investigation of Co-combustion Characteristics of Sewage Sludge and Coffee Grounds Mixtures Using Thermogravimetric Analysis Coupled to Artificial Neural Networks Modeling.” Bioresour. Technol. 225, https://doi.org/10.1016/j.biortech.2016.11.069.Search in Google Scholar PubMed

Chuanhou, G., J. Ling, and L. Shihua. 2011. “Modeling of the Thermal State Change of Blast Furnace Hearth with Support Vector Machines.” IEEE Transactions on Industrial Electronics 59: 2. https://doi.org/10.1109/TIE.2011.2159693.Search in Google Scholar

Datta, A., M. Hareesh, P. Kumar, B. Deo, and R. Boom. 1994. “Adaptive Neural Net (ANN) Models for Desulphurization of Hot Metal and Steel.” Steel Research 65: 11, https://doi.org/10.1002/srin.199401195.Search in Google Scholar

Deo, B., A. Datta, B. Kukreja, R. Rastogi, and K. Deb. 1994. “Optimization of Back Propagation Algorithm and GAS-Assisted ANN Models for Hot Metal Desulphurization.” Steel Research 65: 12, https://doi.org/10.1002/srin.199401206.Search in Google Scholar

Dibaba, O. R., S. K. Lahiri, S. T’Jonck, and A. Dutta. 2016. “Experimental and Artificial Neural Network Modeling of a Upflow Anaerobic Contactor (UAC) for Biogas Production from Vinasse.” International Journal of Chemical Reactor Engineering 14: 6, https://doi.org/10.1515/ijcre-2016-0025.Search in Google Scholar

Eros, G. 1997. Manual de operación EROS. In Nicaro, Holguín.Search in Google Scholar

Fletcher, R. 1987. Practical Methods of Optimization. Berlín: Wiley.Search in Google Scholar

Fraser, E. D. G., A. J. Dougill, W. E. Mabee, M. Reed, and P. McAlpine. 2006. “Bottom up and Top Down: Analysis of Participatory Processes for Sustainability Indicator Identification as a Pathway to Community Empowerment and Sustainable Environmental Management.” Journal of Environmental Management 78, https://doi.org/10.1016/j.jenvman.2005.04.009.Search in Google Scholar

Habashi, F. 1997. Handbook of Extractive Metallurgy. Weinheim, Germany: Wiley-VCH.Search in Google Scholar

Hoseinian, F. S., B. Rezai, and E. Kowsari. 2017. “The Nickel Ion Removal Prediction Model from Aqueous Solutions Using a Hybrid Neural Genetic Algorithm.” Journal of Environmental Management 204, https://doi.org/10.1016/j.jenvman.2017.09.011.Search in Google Scholar

Larsen, P. E., L. J. Cseke, R. M. Miller, and F. R. Collart. 2014. “Modeling Forest Ecosystem Responses to Elevated Carbon Dioxide and Ozone Using Artificial Neural Networks.” Journal of Theoretical Biology 359, https://doi.org/10.1016/j.jtbi.2014.05.047.Search in Google Scholar

Li, Q., and M. F. Chan. 2017. “Predictive Time-Series Modeling Using Artificial Neural Networks for Linac Beam Symmetry: An Empirical Study.” Annals of the New York Academy of Sciences: 1387(1), 84–94.10.1111/nyas.13215Search in Google Scholar

Ljung, L. 1999. System Identification: Theory for the User, 2nd ed. Sweden: Prentice Hall information and System Sciences Series,Thomas Kailath.Search in Google Scholar

Miranda, J., R. Chaviano, and J. R. Miranda. 2002. “Nuevas Interpretaciones químico-mineralógicas de las menas lateríticas y serpentínicas, a través del proceso pirometalúrgico, en la tecnología carbonato-amoniacal.” Revista Cubana de Química 14: 2.Search in Google Scholar

Montero, D., M. Ramírez, A. Gilbert, and S. Perdices. 2015. “Modelación matemática para el control de la postcombustión en un horno de reducción de níquel.” RIELAC XXXVI: 3.Search in Google Scholar

Nabavi-Pelesaraei, A., S. Rafiee, H. Hosseinzadeh-Bandbafha, and S. Sham-shirband. 2016. “Modeling Energy Consumption and Greenhouse Gas Emissions for Kiwifruit Production Using Artificial Neural Networks.” Journal of Cleaner Production 133, https://doi.org/10.1016/j.jclepro.2016.05.188.Search in Google Scholar

Nørgaard, M. 2000. Neural Network Based System Identification Toolbox Version 2. In Denmark: Technical University of Denmark.Search in Google Scholar

Ogata, K. 2010. Modern Control Engineering. New Jersey, USA: Prentice-Hall.Search in Google Scholar

Peifeng, N., M. Yunfei, L. Pengfei, and Y. Zhang. 2013. “Hybrid Neural Network in Circulating Fluidized Bed Boiler Based on Information Fusion Clustering Control.” Neural Computing and Applications 23: 7–8. https://doi.org/10.1007/s00521-012-1187-8.Search in Google Scholar

Prieto, A., B. Prieto, E. M. Ortigosa, E. Ros, F. Pelayo, J. Ortega, and I. Rojas. 2016. “Neural Networks: An Overview of Early Research, Current Frameworks and New Challenges.” Neurocomputing 214: 242, https://doi.org/10.1016/j.neucom.2016.06.014.Search in Google Scholar

Ramírez, M. 2001. “Identificación experimental del subproceso de postcombustión en un horno de reducción de níquel.” Minería y Geología 18: 2.Search in Google Scholar

Ramírez, M. C. 2002a. “Borroso de la Postcombustión en un Horno de Múltiples Hogares.” In Departamento de Automática, 166. La Habana: Instituto Superior Politécnico José Antonio Echeverría.Search in Google Scholar

Ramírez, M. 2002b. “Modelado del proceso de post-combustión en un horno de reducción de níquel.” Revista de Metalurgia 38 (2). https://doi.org/10.3989/revmetalm.2002.v38.i2.396.Search in Google Scholar

Rastogi, R., k Deb, B. Deo, and R. Boom. 1994. “Genetic Adaptive Search Model of Hot Metal Desulphurization.” Steel Research 65: 11, https://doi.org/10.1002/srin.199401196.Search in Google Scholar

Rathbun, T. F., S. K. Rogers, M. P. DeSimio, and M. E. Oxley. 1997. “MLP Iterative Construction Algorithm.” Neurocomputing 17, https://doi.org/10.1016/s0925-2312(97)00054-4.Search in Google Scholar

Reed, M. S., L. C. Stringer, I. Fazey, A. C. Evely, and J. H. J. Kruijsen. 2014. “Five Principles for the Practice of Knowledge Exchange in Environmental Management.” Journal of Environmental Management 146. https://doi.org/10.1016/j.jenvman.2014.07.021.Search in Google Scholar

Reuter, M. A., and J. S. J. Van Deventer. 1991. “Knowledge-based Simulation and Identification of Various Metallurgical Reactors.” Met. Trans. B 22B. https://doi.org/10.1007/BF02654293.Search in Google Scholar

Reuter, M. A., and J. S. J. Van Deventer. 1992. “The Simulation and Identification of Flotation Processes by Use of a Knowledge Based Model.” International Journal of Mineral Processing 35, https://doi.org/10.1007/BF02649724.Search in Google Scholar

Reuter, M. A., T. J. Van der Walt, and J. S. J. Van Deventer. 1992. “Modeling of Metal-Slag Equilibrium Processes Using Neural Nets.” Metallurgical Transactions B 23B. https://doi.org/10.1007/BF02649724.Search in Google Scholar

Reuter, M. A., J. S. J. Van Deventer, and T. J. Van tier Walt. 1993. “A Generalized Neural Net Kinetic Rate Equation.” Chemical Engineering and Science 48: 7, https://doi.org/10.1016/0009-2509(93)81009-k.Search in Google Scholar

Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning Internal Representations by Error Propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition.10.21236/ADA164453Search in Google Scholar

Sangster, K. J., D. K. Presgrave, and I. C. Herbert. 1977. XIIth International Mineral Processing Congress General and Oxidized Copper. Some Aspects of the Extraction of Copper from its Ores by Hydro-Metallurgical Techniques, 23–34. Sao Paulo, Brazil: National - Publicacoes e Publicidade Rua Hungria. 1-GENERAL.Search in Google Scholar

Santana Lopez, E., D. Montero Góngora, and O. Vega Arias. 2018. “Identification of the Air Supply System for Combustion, with the Help of Artificial Neural Networks.” Biomedical Journal of Scientific & Technical Research 10: 2. https://doi.org/10.26717/BJSTR.2018.10.001929.Search in Google Scholar

Santos, M. 2011. “Un Enfoque Aplicado del Control Inteligente.” RIAI 8, https://doi.org/10.1016/j.riai.2011.09.016.Search in Google Scholar

Selva, A. 1984. “Regulación del Perfil de Temperatura de un Horno de Reducción.” In Departamento de Automática. Santiago de Cuba: Universidad de Oriente.Search in Google Scholar

Singh, H., N. Venkata, and B. Deo. 1996. “Artificial Neural Nets for Prediction of Silicon Content of Blast Furnace Hot Metal.” Steel Research 67: 12, https://doi.org/10.1002/srin.199605531.Search in Google Scholar

Smith, C. A, and A. Corripio. 2006. Principles and Practice of Automatic Process Control. New Jersey: John Wiley and Sons.Search in Google Scholar

Tseng, S. C., and S. W. Hung. 2014. “A Strategic Decision-Making Model Considering the Social Costs of Carbon Dioxide Emissions for Sustainable Supply Chain Management.” Journal of Environmental Management 133, https://doi.org/10.1016/j.jenvman.2013.11.023.Search in Google Scholar PubMed

Unión del Níquel. 1996. Proyecto de Modernización – Planta de Hornos de Reducción. In Nicaro, Cuba, 38–45.Search in Google Scholar

Valverde, R., and D. Gachet. 2007. “Identificación de sistemas dinámicos utilizando redes neuronales RBF.” RIAI 4: 2.10.1016/S1697-7912(07)70207-8Search in Google Scholar

Wilson, J. A., and L. E. M. Zorzetto. 1997. “A Generalised Approach to Process State Estimation Using Hybrid Artificial Neural.” Computers & Chemical Engineering 21: 9, https://doi.org/10.1016/s0098-1354(96)00336-5.Search in Google Scholar

Xiong, Y., R. Wallach, and A. Furman. 2011. “Modeling Multidimensional Flow in Wettable and Water-Repellent Soils Using Artificial Neural Networks.” Journal of Hydrology: 410. https://doi.org/10.1016/j.jhydrol.2011.09.019.Search in Google Scholar

Yilmaz, S., and M. Z. Bilgin. 2013. “Modelling and Simulation of Injection Control System on a Four-Stroke Type Diesel Engine Development Platform using Artificial Neural Networks.” Neural Computing and Applications 22, https://doi.org/10.1007/s00521-012-1054-7.Search in Google Scholar

Received: 2019-10-26
Accepted: 2020-07-01
Published Online: 2020-07-31

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijcre-2019-0191
Keywords for this article
artificial neural network; auto-correlation function; dynamic characteristics; multi-hearth furnace; post-combustion

Articles in the same Issue

  1. Editorial
  2. Preface: Special issue dedicated to the International Energy Conference, IEC-2019, Morelia, México “towards energy sustainability with a social approach”
  3. Special Issue Articles
  4. Controlled Evaluation in a Diesel Engine of the Biofuel Obtained with Ni/γ-Al2O3 Nanoparticles in the Hydrodeoxygenation of Oleic Acid
  5. Environmental Problems and the State of Compliance with the Right to a Healthy Environment in a Mining Region of México
  6. Heterojunctions for Photocatalytic Wastewater Treatment: Positive Holes, Hydroxyl Radicals and Activation Mechanism under UV and Visible Light
  7. Ultrasound-assisted extraction of phenolic compounds from avocado leaves (Persea americana Mill. var. Drymifolia): optimization and modeling
  8. Flow characteristics of the Rushton and pitched blade turbines in turbulent and laminar mixing
  9. Modelling and multi-objective optimization for simulation of hydrogen production using a photosynthetic consortium
  10. Heterogeneous PVC cation-exchange membrane synthesis by electrospinning for reverse electrodialysis
  11. Hydrodesulfurization of dibenzothiophene using NiMoWS catalysts supported on Al–Mg and Ti–Mg mixed oxides
  12. Hydrodynamics of a modified up-flow anaerobic sludge blanket reactor treating organic fraction of municipal solids waste
  13. Temperature effects on VO2 thin films deposited by RF sputtering for the degradation by photocatalysis of methylene blue and naproxen
  14. Post-combustion artificial neural network modeling of nickel-producing multiple hearth furnace
  15. Effect of organic loading rate on anaerobic digestion of raw cheese whey: experimental evaluation and mathematical modeling
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