iosr-JMCE   Volume-11 ~ Issue-4 ~ July-Aug 2014

Abstract: The building construction is one of the most unfamiliar R&D activities in the robotics and automation community. Nevertheless, the building construction is one of the oldest and largest economic sectors. Intense competition, shortages of skilled labor, increase in labour cost and technological advances are forcing rapid change in the building construction. Building construction is divided into two large groups: commercial infrastructure and residential building. Both areas are needed plastering work for increasing the quality of construction. Machines has been developed to automate the plastering work is very much demand for construction field. It includes microcontroller, relay, AC/DC motor, fabricated structure, display, belt drives etc. The present work highlights the design and developments of prototype in this area and also a new model is proposed to automate the plastering work. Initially the model was developed and fabricated for considering the normal constructed brick wall. The completed model was validated by testing the machine using the brick wall and the statistical details of the manual work are also observed at the different conditions.
Keywords: AC/DC motor, Display, Fabricated structure, Masonry Leveling Compound (MLC), Microcontroller, Relay

[1]. S.M.S. Elattar, "Automation and robotics in construction: opportunities and challenges", Emirates Journal for Engineering Research, 13 (2), 21-26, 2008.
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[6]. C. Balaguer et al., "Future Home: An Integrated Construction Automation Approach" IEEE Robotics and Automation Magazine, 55-66, 2002.

Abstract: Need of mobility all across the world is increasing exponentially. This is also an important prerequisite for the progress of modern society. In the past, automobile has played a crucial role and shall continue to play a dominant role in the progress of society. The demand of automobiles is increasing rapidly especially in the countries like China, India, Brazil and Korea. The rising economies of these countries will further increase the demand of automobiles. In order to achieve safety, comfort and environment friendliness, automobile companies are investing heavily in research and development. In this context, nanotechnologies are likely to play an important role. Nanotechnology is opening new doors for innovative products and imaginative applications in automobile sector. This paper focuses and critically analyses the improvement in the tire quality and to increase the life of the tire by using nano materials in every layer of tyres
Keywords: Nano silica, Montmorillonite clay, Carbon nanotubes

[1]. http://nanoparticles.org/pdf/PerezBaxEscolano.pdf
[2]. http://web.mit.edu/newsoffice/2013/magnetic-nanoparticles-could-aid-heat- dissipation-1119.html
[3]. http://www.researchgate.net/publication/47536256_Ultra_high_strength_steel_design_by_using_nanoparticles
[4]. http://www.nanowerk.com/news/newsid=4106.php
[5]. http://ec.europa.eu/health/scientific_committees/opinions_layman/en/nanotechnologies/l-2/5-nanoparticles-consumer-products.html

Abstract: This paper presents an open systemic analysis of bioextracted iron during leaching of iron oxide ore using acidithiobacillus ferrooxidans. The analysis showed prediction of the concentration of bioextracted iron at varying initial bioleaching solution pH and as-beneficiated Fe content of the ore. An empirical model was derived, validated and used as a tool for the analysis. The validity of the polynomial model expressed as;
ζ = 0.0077ϑ3 - 0.1618 ϑ 2 + 0.9345ϑ + 0.0002₰ + 0.0931
was found to be rooted on the core expression ζ - 0.0002₰ - 0.0931 = 0.0077ϑ3 - 0.1618 ϑ 2 + 0.9345ϑ where both sides of the expression are correspondingly approximately equal. Statistical analysis of the extracted iron concentration as obtained from experiment and derived model for each value of the initial bioleaching solution pH used shows standard errors of 0.3917 and 0.3780 respectively. Extracted iron concentrations per unit initial bioleaching solution pH as obtained from experiment and derived model are 0.0917 and 0.0784 %. Deviational analysis indicates that the maximum deviation of the model-predicted iron extraction (from experimental results) was less than 11%, implying a confidence level above 89%.
Keywords: Open system Analysis, Iron Bioextraction, Iron Ore Leaching, Acidithiobacillus Ferrooxidans

[1] Taxiarchour, M., Panias, D., Doumi, I., Paspaliaris, I., Kontopoulos, A., (1997a) Removal of Iron from Silica Sand by Leaching with Oxalic Acid, Hydrometallurgy, 46, 215-227.
[2] Lee, S.O, Tran, T., Park Y.Y., Kim S.J., and Kim, M. J. (2006) Study on the Kinetics of Iron Leaching by Oxalic Acid.Int. J .Miner Process, 80, 144-152.
[3] Taxiarchou, M., Parnias, D., Doumi, I., Paspaliaris, I., and Kontopoulous, A. (1997a) Dissolution of Haematite in Acidic Oxalate Solutions. Hydrometallurgy, 44, 287-299.
[4] Panias, D., Taxiarchou, M., Paspaliaris, I., Kontopoulos, A. (1996) Mechanism of Dissolution of Iron Oxides in Aqueous Oxalic Acid. Hydrometallurgy, 42, 257-265.
[5] Lee, S. O., Oh, J. K., Shin, B. S. (1999) Dissolution of Iron Rust Materials using Oxalic Acid. J. Min. Metall. Inst. Jpn. 115, 815-819. Alafara, A. Baba, D., Adekola, F. A., Folashade, A. O. (2005). Quantitative Leaching of a Nigerian Iron Ore in Hydrochloric Acid, J. Appl. Sci. Environ. Mgt. 9(3):15 - 20

Abstract: Predictability of removed phosphorus concentration was carried out based on multi-factorial influence of pH, resident time and as-beneficiated phosphorus content of the iron ore during microbial leaching with Acidithiobacillus Ferrooxidans. The model was expressed as;
PR = 0.0042ϑ3 - 0.0718 ϑ 2 + 0.3297 ϑ - 0.001₰ + 0.01γ - 0.0903
The validity of the multi-factorial model was found to be rooted on the expression PR + 0.001₰ - 0.01γ + 0.0903 = 0.0042ϑ3 - 0.0718ϑ2 + 0.3297 ϑ where both sides of the expression are correspondingly approximately equal. Statistical analysis of removed phosphorus concentration for each value of the bioleaching solution pH as obtained from experiment and derived model-predicted results show standard errors of 0.1031 and 0.1010% respectively. Furthermore, removed phosphorus concentration per unit solution pH as obtained from experiment and derived model-predicted results were 0.005 and 0.006 % respectively. The maximum deviation of model-predicted removed phosphorus concentration (from experimental result) was less 8%. This translates into a derived model confidence level of above 92%.
Keywords: Analysis, Phosphorus Removal, Acidithiobacillus Ferrooxidans, Agbaja Iron Ore.

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