Multi-objective optimization of iron ore induration process using optimal neural networks

References

• Nath, N.-K. Simulation of Gas Flow in Blast Furnace for Different Burden Distribution and Cohesive Zone Shape. Mater. Manuf. Processes. 2002, 17(5), 671–681. DOI: 10.1081/AMP-120016090.
   Web of Science ®Google Scholar
• Lovel, R.; Vining, K.; Dell’Amico, M. Iron Ore Sintering with Charcoal. Miner. Process. Extr. Metall. 2007, 116(2), 85–92. DOI: 10.1179/174328507X163887.
   Google Scholar
• Mitra, K.; Majumder, S.; Runkana, V. Multiobjective Pareto Optimization of an Industrial Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm. Mater. Manuf. Processes. 2009, 24(3), 331–342. DOI: 10.1080/10426910802679428.
   Web of Science ®Google Scholar
• Yu, K.; While, L.; Reynolds, M.; Wang, X.; Liang, -J.-J.; Zhao, L.; Wang, Z. Multiobjective Optimization of Ethylene Cracking Furnace System Using Self-adaptive Multiobjective Teaching-learning-based Optimization. Energy. 2018, 148, 469–481. DOI: 10.1016/j.energy.2018.01.159.
   Web of Science ®Google Scholar
• Baraskar, -S.-S.; Banwait, -S.-S.; Laroiya, S.-C. Multiobjective Optimization of Electrical Discharge Machining Process Using a Hybrid Method. Mater. Manuf. Processes. 2013, 28(4), 348–354. DOI: 10.1080/10426914.2012.700152.
   Web of Science ®Google Scholar
• Serin, G.; Ozbayoglu, M.; Unver, H.-O. Integrated Energy-efficient Machining of Rotary Impellers and Multi-objective Optimization. Mater. Manuf. Processes. Published Online: Apr 30, 2019. DOI: 10.1080/10426914.2019.1605177
   Web of Science ®Google Scholar
• Kumar, P.-M.; Sivakumar, K.; Jayakumar, N. Multiobjective Optimization and Analysis of Copper–Titanium Diboride Electrode in EDM of Monel 400™ Alloy. Mater. Manuf. Processes. 2018, 33(13), 1429–1437. DOI: 10.1080/10426914.2017.1415439.
   Web of Science ®Google Scholar
• Zaheer, H.; Pant, M. Solution to Industrial Optimization Problems through Differential Evolution Variants. Mater. Manuf. Processes. 2017, 32(10), 1131–1143. DOI: 10.1080/10426914.2017.1279300.
   Web of Science ®Google Scholar
• Liu, X.; Chen, L.; Feng, H.; Qin, X.; Sun, F. Constructal Design of a Blast Furnace Iron-making Process Based on Multi-objective Optimization. Energy. 2016, 109, 137–151. DOI: 10.1016/j.energy.2016.04.101.
   Web of Science ®Google Scholar
• Mitra, T.-A.; Saxén, H.-E.; Chakraborti, N.-I. Evolutionary Algorithms in Ironmaking Applications; Apple Academic Press: Point Pleasant, 2016.
   Google Scholar
• Deb, K.;. Multi-Objective Optimization Using Evolutionary Algorithms; John Wiley & Sons: Chichester, 2008.
   Google Scholar
• Mahanta, B. K.; Chakraborti, N. Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process. Steel Res. Int. 2018, 89(9), 1800121. DOI: 10.1002/srin.201800121.
   Web of Science ®Google Scholar
• Elsheikh, A.-H.; Sharshir, S.-W.; Elaziz, M.-A.; Kabeel, A. E.; Guilan, W.; Haiou, Z. Modeling of Solar Energy Systems Using Artificial Neural Network: A Comprehensive Review. Sol. Energy. 2019, 180, 622–639. DOI: 10.1016/j.solener.2019.01.037.
   Web of Science ®Google Scholar
• Hirose, A.; Micheli, A.; Garcez, A.-S.; Ahn, C.-K.; Pan, G.; Karimi, H.-R.; Shen, J.; de Jesus Rubio, J.; Zhang, L.; Liu, L.; et al. Booming of Neural Networks and Learning Systems. IEEE Trans. Neural Netw. Learn. Syst. 2019, 30, 2–10. DOI: 10.1109/TNNLS.2018.2884305.
   Web of Science ®Google Scholar
• Mogilicharla, A.; Mittal, P.; Majumdar, S.; Mitra, K. Kriging Surrogate Based Multi-objective Optimization of Bulk Vinyl Acetate Polymerization with Branching. Mater. Manuf. Processes. 2015, 30(4), 394–402. DOI: 10.1080/10426914.2014.921709.
   Web of Science ®Google Scholar
• Pantula, P.-D.; Miriyala, -S.-S.; Mitra, K. KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery. Mater. Manuf. Processes. 2017, 32(10), 1162–1171. DOI: 10.1080/10426914.2016.1269918.
   Web of Science ®Google Scholar
• Chugh, T.; Chakraborti, N.; Sindhya, K.; Jin, Y. A Data-driven Surrogate-assisted Evolutionary Algorithm Applied to A Many-objective Blast Furnace Optimization Problem. Mater. Manuf. Processes. 2017, 32(10), 1172–1178. DOI: 10.1080/10426914.2016.1269923.
   Web of Science ®Google Scholar
• Haykin, S. Neural Networks: A Comprehensive Foundation; Prentice Hall PTR: New Jersey, USA, 1994.
   Google Scholar
• Forrester, A.; Sobester, A.; Keane, A. Engineering Design via Surrogate Modelling: A Practical Guide; John Wiley & Sons: Chichester West Sussex, UK, 2008.
   Google Scholar
• Jafarian, F.; Amirabadi, H.; Sadri, J.; Banooie, H.-R. Simultaneous Optimizing Residual Stress and Surface Roughness in Turning of Inconel718 Superalloy. Mater. Manuf. Processes. 2014, 29(3), 337–343. DOI: 10.1080/10426914.2013.864413.
   Web of Science ®Google Scholar