Optimization of parameters effecting the heat recovery from a sand casting process

GopalaKrishna V S, Prakash Marimuthu

Abstract


Energy crises has gripped the world to a large extent. Researchers are experimenting on newer and newer avenues to produce energy efficiently, economically. Energy saved is actually twice produced. Energy conservation is the need of the hour. Keeping this in mind the authors of the paper have developed a method to harness the heat that gets wasted during the casting process. Casting is basically one of the manufacturing processes were molten metal is poured into mould and then solidified. The solidifying metal takes the shape of the mould. Huge amount of energy is required for the melting of the raw material. When the metal solidifies it gives way the heat. In the present work the mould is modified such that the raw material that will be used for subsequent process gets pre heated by the heat that emanates due to the solidification process. A number of factors influence the amount of heat that is recovered. Here attempt is made to find out the factors that influence the pre heat of raw material and also using Particle swarm optimization technique the factors are optimized so that the heat recovered is maximised. The implementation of the process is simple and the gains are enormous in terms of the energy that is going to be saved.

Keywords


Energy; Casting; Particle Swarm optimization; conservation

Full Text:

PDF

References


S. C. Bhattacharya and C. Jana, “Renewable energy in India: Historical developments and prospects,” Energy, vol. 34, no. 8, pp. 981–991, Aug. 2009.

J. Pucher, N. Korattyswaropam, N. Mittal, and N. Ittyerah, “Urban transport crisis in India,” Transp. Policy, vol. 12, no. 3, pp. 185–198, May 2005.

G. Ömeroğlu, “A scrutiny study on wave energy potential and policy in TURKEY,” Period. Eng. Nat. Sci., vol. 5, no. 3, pp. 286–297, Oct. 2017.

T. V Ramachandra and G. Hegde, “Energy Trajectory in India: Challenges and Opportunities for Innovation,” J. Resour. Energy Dev., vol. 12, no. 1–2, pp. 1–24, Dec. 2015.

J. Zheng and J. Li, “Study on the ecologic network system of energy-intensive industries,” Energy Procedia, vol. 5, pp. 1987–1992, 2011.

J. Selvaraja, P. Marimuthu, S. Devanathan, and K. I. Ramachandran, “Mathematical modelling of raw material preheating by energy recycling method in metal casting process,” Pollut. Res., vol. 36, no. 3, pp. 598–609, 2017.

D. Zhao, Z. Zhang, X. Tang, L. Liu, and X. Wang, “Preparation of slag wool by integrated waste-heat recovery and resource recycling of molten blast furnace slags: From fundamental to industrial application,” Energies, vol. 7, no. 5, pp. 3121–3135, 2014.

J. Wallace and D. Schwam, “Advanced Melting Technologies : Energy Saving Concepts and Opportunities for the Metal Casting Industry,” no. November, pp. 1–42, 2005.

P. Solding and P. Thollander, “Increased Energy Efficiency in a Swedish Iron Foundry Through Use of Discrete Event Simulation,” in Proceedings of the 2006 Winter Simulation Conference, 2006, pp. 1971–1976.

A. Neumaier, Modeling Languages in Mathematical Optimization, vol. 88. Boston, MA: Springer US, 2004.

B. M. Turai, C. Satish, and P. M. K, “Mathematical modelling and numerical simulation of forces in milling process Mathematical Modelling and Numerical Simulation of Forces In Milling Process,” vol. 20068, 2018.

K. P. Marimuthu, C. S. C. Kumar, and H. P. T. Prasada, “Mathematical modelling to predict the residual stresses induced in milling process,” Int. J. Mech. Prod. Eng. Res. Dev., vol. 8, no. 1, pp. 423–428, 2018.

G. Bisio, “Energy recovery from molten slag and exploitation of the recovered energy,” Energy, vol. 22, no. 5, pp. 501–509, May 1997.

K. Tyagi and K. Tyagi, “A Comparative Analysis of Optimization Techniques,” Int. J. Comput. Appl., vol. 131, no. 10, pp. 6–12, Dec. 2015.

M. J. Rempe, J. Grønli, T. Thue, J. Mrdalj, A. Marti, J. P. Wisor, J. Mrdalj, A. Marti, P. Meerlo, and J. P. Wisor, “Mathematical Modeling of Sleep State Dynamics in a Rodent Model of Shift Work,” Neurobiol. Sleep Circadian Rhythm., 2018.

Z. Yilmaz, M. Okşar, and F. Başçİftçİ, “Multi-Objective Artificial Bee Colony Algorithm to Estimate Transformer Equivalent Circuit Parameters,” vol. 5, no. 3, pp. 271–277, 2017.

U. Can and B. Alatas, “Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems,” Period. Eng. Nat. Sci., vol. 5, no. 3, pp. 328–340, 2017.

M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58–73, 2002.

S. Ekiz, “Solving Constrained Optimization Problems with Sine-Cosine Algorithm,” Period. Eng. Nat. Sci., vol. 5, no. 3, pp. 378–386, 2017.




DOI: http://dx.doi.org/10.21533/pen.v6i2.181

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 GopalaKrishna V S, Prakash Marimuthu

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License