Volume-2 Issue-8

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Volume-2 Issue-8

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S. No

Volume-2 Issue-8, July 2014, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Asadolah Akbarian Aghdam, Alimohammad Ahmadvand, Saeed Alimohammadi

Paper Title:

Future Surface Water Resources Sensitivity to Climate Changes Impacts

Abstract: Due to water resources limitation in most parts of Iran, it is essential to give especial attention on evaluating and managing water resources. Climate changes would significantly affect water resources in future.  In this study clime change impacts on water resources has been evaluated. "Karun" as the most watery river in Iran with an annual discharge of 4927.4 MCM at the site of Karun4 dam, is selected as case study. For this purpose 28 scenarios for precipitation and temperature, by using 11 models of AOGCM (Atmosphere-Ocean Global Circulation Model) models from CCCSN (Canadian Climate Change Scenario Network) are established and downloaded for next 90 years. Scenarios are downscaled for being usable for the study region. Evapotranspiration scenarios are generated by models which are provided for the case study region. The precipitation and temperature scenarios are used as input data by the mentioned models to generate future evapotranspiration scenarios. Multivariable empirical regression models based on 30 years monthly historical recorded data are generated to predict future monthly discharge scenarios. All of the models are tested with historical data. The precipitation, temperature, evapotranspiration and discharge scenarios are taken into account to estimate future surface water resources. The study shows that there would be a reduction of 17.20% (38.63 mm/year) in precipitation and 31.51% (58 m3/s) reduction in annual discharge by the end of 2100. Also annual temperature would have a raise about 22.65% (3.82° C). River runoff would have 27.8% reduction and would cause more than 25% reduction in water surface resources.

MCM, AOGCM, CCCSN, reduction, precipitation, Evapotranspiration.


1.        Aida M. Jose, Nathaniel A. Cruz , "Climate change impacts and responses in the Philippines: water resources",. CLIMATE RESEARCH, Vol. 12: 77–84, 1999
2.        Official website of  "Iran Water & Power Resources Development Company", http://en.iwpco.ir/Karun4

3.        Bettina Schaefli, Benoit Hingray and Andre Musy. Hydrol , "Climate change and hydropower production in the SWISS alps; Quantification of Potential Impacts and Relates Modeling Uncertainties", Earth Syst. Sci., 11(3), 1191-1205, 2007.

4.        Anne E., Adam G. Hart, Richard Stafford, " Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets",   PLoS ONE. Volume 7, Issue 3 ,e34338, March 2012.

5.        S. Hagemann, C. Chen and else, “Climate change impact on available water resources obtained using multiple global climate and hydrology models”, Earth Syst. Dynam. Discuss., 3, 1321-1345, 2012.

6.        Peter J. Robinson, “Climate Change and Hydropower Generation”, International journal of Climatology, Vol. 17, 983-996 (1997).

7.        Lane M.E., Kirshenand P.H. and R.M. Vogel, "Indicators of impact of global climate change on U.S. water resources", ASCE, Journal of Water resource Planning and Management 125(4) (1999) 194-204

8.        Bultot F., Dupriez G.L., Gellens D., "Estimated annual regime of energy balance components, evapotranspiration and soil moisture for a drainage basin in case of a CO2 doublling", Climate Change (1988), 12, 39-56.

9.        Guegan, Marion; Bertacchi Uvo, Cintia and Madani, Kaveh (2012), Developing a module for estimating climate warming effects on hydropower pricing in California, In Energy Policy 42. p.261-271





Aref Naimzad, Yousef Hojjat, Mojtaba Ghodsi

Paper Title:

Comparative Study on Mechanical and Magnetic Properties of Porous and Nonporous Film-Shaped Magnetorheological Nanocomposites Based on Silicone Rubber

Abstract: This paper presents a comparative study on mechanical and magnetic properties of two sets, each including five samples of film-shaped magnetorheological nanocomposites (MRNCs) based on RTV silicone rubber and nano-sized carbonyl iron particles (CIPs). One set of sample was prepared by polymerization of silicone rubber with CIPs and silicone oil, while the other set obtained by filling the ammonium bicarbonate (NH4HC3), CIPs and silicone oils. Both set of samples were manufactured under isotropic condition and their microstructures was characterized by XRD and EFSEM. Porosity characteristics was measured by displacement method and porosity image analysis was applied using ImageJ and Origin Pro Software. The mechanical tensile tests was conducted using Gotech tensile strength tester and the density of samples was observed experimentally and estimated theoretically. The magnetic properties of MRNCs were practically determined using VSM test. Plateau stress induced by the applied magnetics fields and MR effects was determined. Through fabrication of film-shaped MRNCs, the samples deflections was measured against applied magnetic fields .The comparative investigation results show that porosity improve the mechanical and magnetic properties of MRNCs and porous MRNCs will be the good candidate for miniature and flexible gripper’s jaws.

Carbonyl Iron, MRNCs, Porosity, Silicone Rubber.


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3.        Naimzad A., Hojjat Y. and Ghodsi M., Fabrication and characterization of MR Nanocomposites based on silicone rubber , Proc. 3rd Int. Conf. on Composites: Characterization , Fabrication and Application (CCFA-3),2012, Iran: Tehran, p 121.

4.        Naimzad A., Hojjat Y. and Ghodsi M., Study on MR Nanocomposites to develop a miniature gripper, Proc. Int. Conf. on Actuator (Actuator 12),2012, Bermen, Germany, p 616.

5.        Naimzad A. ,Hojjat Y. and Ghodsi M., Attempts to design a miniature gripper using Magneto-Rheological Nanocomposites (MRNCs), Proc.

6.        Int. Cong. on Nanoscience & Nanotechnology (ICNN 2012),2012, Kashan, Iran, p 488.

7.        Mehrdad Kokabi, S.A.Moutazedi, M.H.Navid Family, Manufacture of magneto-rheological actuator based on silicone rubber ,Iranian Polymer Science and Technology Journal,  1: 37-43 (2005).

8.        Xinglong Gong, Guojing Liao, Shouhu Xuan, Full-field deformation of magnetorheological elastomer under uniform magnetic field, Appl.Phys.Lett. (100)211909(2012).

9.        Y.Wang,Y.Hu, X.Gong , W. Jiang, P.Zhang, Z.chen, Preparation and Properties of Magnetorheological Elastomers Based on Silicon Rubber/Polystyrene Blend Matrix , J Appl. Polym. Sci.,103: 3143–3149(2007).

10.     M. Yu, B. Ju, H Fu, Influence of composition of carbonyl iron particles on dynamic mechanical properties of magnetorheological elastomers, Journal of Magnetism and Magnetic Materials, 324: 2147-2152(2012).

11.     R.Li, L.Z.Sun,M.ASCE, Dynamic viscoelastic Behaviour of Multi-Walled-Carbon-Nanotube-Reinforced Magnetorheological Nanocomposites, Journal of Naomechanics and Micromechanics, 21535477(2013).

12.     B. X. Ju, M. Yu, J. Fu, Q Yang, X Q Liu and X Zhang, Novel porous magnetorheological elastomer: preparation and evaluation , Smart Mater.Struct,21  035001(2012).

13.     S.M.Zhang, F.Z.Cui, S.S.Liao , Synthesis and biocompatibility of porous nano-hydroxyapatite/ collagen/ alginate composite, Journal of Materials Science: Materials in Medicine,14:641-645(2003).

14.     G. Impoco, S. Carrato, M.Caccamo, L.Tuminello, G.Licitra, Software for Image Analysis of Cheese Microstructure from SEM Imagery, Communications to SIMAI Congress, Vol.2 (2007).

15.     Mikrajuddin A.,Khairurrijal, A Simple Method for Determining Surface Porosity Based on SEM Images Using OriginPro Software, Indonesian Journal of Physics, 20(2):37-40 (2009).

16.     Girish B.M., Basawaraj B.R., Satish B.M, Electrical Resistivity and Mechanical Properties of Tungsten Carbide Reinforced Copper Alloy Composites, International Journal of Composite Materials, 2(3):37-42 (2012).

17.     D.S. Prasad, A.R. Krishna, Production and Mechanical Properties of A356.2/RHA Composites, International Journal of Advanced Science and Technology, Vol. 33(2011).

18.     Naimzad A., Ghodsi M.,Hojjat Y., Maddah A., MREs development and its application on miniature gripper, Proc. Int. Conf. on Advanced Materials Engineering, Singapore, IPCSIT 2011, 15: 75-80(2011).

19.     L.O.Song, T.Keh, J.Zang, X. Zhao, Proc. Int. Conf.(Southeastcon-IEEE) Jacksonville, FL(2013).

20.     A.Boczkowska, S.Awietjan, Microstructure and Properties of Magnetorheological Elastomers, InTech, 147-180(2012).





Rajkiran Bramhane, Arun Arora, H. Chandra

Paper Title:

Simulation of Flexible Manufacturing System using Adaptive Neuro Fuzzy Hybrid Structure for Efficient Job Sequencing

Abstract: The Flexible Manufacturing Systems (FMS) basically belongs to a category of productive systems in which the main characteristic is the simultaneous execution of several processes and sharing a finite set of resource. Analysis and modeling of flexible manufacturing system (FMS) includes priority analysis of machining jobs and machining routing for efficient profit and production. Flexible manufacturing system (FMS) job Priority calculation becomes exceptionally complex when it comes to contain frequent variations in the part designs of incoming jobs. This paper focuses on priority analysis of variety of incoming jobs into the system efficiently and maximizing system utilization and throughput of system where machines are equipped with different tools and tool magazines but multiple machines can be assigned to single operation. For the complete analysis of the proposed work, a cloud of four incoming jobs have been considered. The Jobs have been assigned the priority according to Slack per Remaining Operations. Usually the probability of incoming job priority is calculated based on three parameters based strategy. In this work an adaptive Neuro fuzzy inference system (ANFIS) is developed to calculate the priority of incoming jobs based on Slack per Remaining Operations (S/RO) parameter. Four horizontal CNC lathe machines have been utilized for this work. Therefore, in this paper, an ANFIS system is developed to generate best priority of incoming jobs. The results obtained clearly indicate the higher efficiency of the proposed work to decide the priority of the incoming jobs.

Flexible manufacturing system (FMS), adaptive Neuro fuzzy inference system (ANFIS), Slack per Remaining Operations (S/RO), Incoming job priority.


1.        Zhiqiang Xie; Shuzhen Hao; Lan Lan; Jing Yang, "An Algorithm for Complex Product Dynamic Integrated Flexible Scheduling," ICCMS '10. Second International Conference on Computer Modeling and Simulation, vol.2, no., pp.304, 308, 22-24 Jan. 2010.
2.        Xie Hongyan; Huo hong, "Research on job-shop scheduling problem based on Self-Adaptation Genetic Algorithm," International Conference on Logistics Systems and Intelligent Management, vol.2, no., pp.940, 943, 9-10 Jan. 2010.

3.        Jinling Du; Dalian Liu, "Hybrid Genetic Algorithm for the Multi-objective Flexible Scheduling Problem," International Conference on Computational Intelligence and Security (CIS), vol., no., pp.122, 126, 11-14 Dec. 2010.

4.        Zhang Xiu-li; Huang Yue; Liu nian, "A hybrid optimization algorithm for multi-objective flexible job-shop," Chinese Control and Decision Conference (CCDC), vol., no., pp.1524, 1530, 26-28 May 2010.

5.        Hsiang-Chun Cheng; Chun-Liang Lu; Shih-Yuan Chiu, "Hybrid Multi-Objective PSO with Solution Diversity Extraction for job-shop scheduling management," 6th International Conference on New Trends in  Information Science and Service Science and Data Mining (ISSDM), 2012, vol., no., pp.711,716, 23-25 Oct. 2012.

6.        Hong Li Yin, "GA with Special Encoded Chromosome for FJSP with Machine Disruptions," 9th International Conference on Computational Intelligence and Security (CIS), 2013, vol., no., pp.298, 302, 14-15 Dec. 2013.

7.        Liyi Zhu; Jinghua Wu; Haijun Zhang; Shijian He, "A Hybrid Genetic Algorithm for Flexible Task Collaborative Scheduling," Second International Conference on  Genetic and Evolutionary Computing, 2008. WGEC '08, vol., no., pp.28, 31, 25-26 Sept. 2008.

8.        Zhiqiang Xie; Shuzhen Hao; Lei Zhang; Jing Yang, "Study on integrated algorithm of complex multi-product flexible scheduling," 2nd International Conference on  Advanced Computer Control (ICACC), 2010, vol.1, no., pp.553,557, 27-29 March 2010.

9.        Yu Jian-jun; Xu Xu-jun; Ye Fei, "Study on multi-objective flexible production scheduling based on improved immune algorithm," International Conference on Management Science and Engineering, 2008. ICMSE 2008. 15th Annual Conference Proceedings., , vol., no., pp.541,548, 10-12 Sept. 2008.

10.     Changzhong Hao; Ze Tao, "Approach for Dynamic Job Shop Scheduling Based on GASA," Fourth International Conference on Natural Computation, 2008. ICNC '08, vol.1, no., pp.561, 565, 18-20 Oct. 2008.

11.     Nhu Binh Ho; Joe Cing Tay, "Solving Multiple-Objective Flexible Job Shop Problems by Evolution and Local Search," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol.38, no.5, pp.674, 685, Sept. 2008.

12.     Berrichi, A.; Yalaoui, F., "Bi objective artificial immune algorithms to the joint production scheduling and maintenance planning," International Conference on Control, Decision and Information Technologies (CODIT), vol., no., pp.810, 814, 6-8 May 2013.

13.     Sioud, A.; Gravel, M.; Gagne, C., "A genetic algorithm for solving a hybrid flexible flow shop with sequence dependent setup times," Evolutionary Computation (CEC), IEEE Congress on, vol., no., pp.2512, 2516, 20-23 June 2013.

14.     Yahui Yang; Zezhi Ren, "Research and Application of Assembly Planning and Scheduling System for Automobile Assembly MES," Fifth International Conference on Computational and Information Sciences (ICCIS), vol., no., pp.1206, 1209, 21-23 June 2013.

15.     Buil, R.; Piera, M.A.; Luh, P.B., "Improvement of Lagrangian Relaxation Convergence for Production Scheduling," IEEE Transactions on  Automation Science and Engineering,  vol.9, no.1, pp.137,147, Jan. 2012.



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