Volume-7 , Issue-4 , Dec 2020, ISSN 2348-635X Go Back

• Open Access   Article

  Rain Garden as an Alternative Flood Mitigation Technique Using Storm Water Management Model

  Florante D. Poso, Jr., Lady Lyn E. Escaries, Coleen P. Clarita, Christine M. Fabian, Lovil Christian D. Moreno, Rowena O. Satira

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.1-8, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Manila, Philippines is a low-lying topography having relatively flat areas, became susceptible to frequent flooding. In 2009, Typhoon Ondoy has inflicted damages in the city due to flooding. Improvements on sewer lines and drainages are continuously implemented but still experiencing gutter and half-knee deep floods. Rain garden is a Low Impact Development system composed of berms planted on an engineered soil, allowing 30% of the surface runoff water to infiltrate into the ground. To assess rain garden as a flood mitigating technique, EPA Storm Water Management Model dynamic rainfall-runoff modelling software was utilized that allows simulation of rainfall events and analysis of its effect onto the area. The simulation of the design case with rain garden resulted to a lesser flood volume as compared to the current design. The maximum percentage difference calculated between the base design and catchment with rain garden model is 53.52% and the minimum is 38.29%.

  Key-Words / Index Term
  Flood Mitigation, Low Impact Development, Rain Garden, Surface Runoff, Storm Water Management

  References
  [1] Mohammed Badiuddin Parvez and M. Inayathulla, “Assesment of the Intensity Duration Frequency Curves for Storms in Upper Cauvery Karnataka Based on Pearson Type III Extreme Value”, World Academics Journal of Engineering Sciences, Vol.6, Issue.1, pp.26-46, October 2019.
  [2] US EPA. Urban Runoff: Low Impact Development, US EPA., https://www.epa.gov/nps/urban-runoff-low-impact-development, 2018.
  [3] N. Chang, J. Lu, T. Chui and N. Hartshorn, "Global Policy Analysis of Low Impact Development for Stormwater Management in Urban Regions", Land Use Policy, vol. 70, pp. 368-383, 2018.
  [4] A. Basdeki, L. Katsifarakis and K. Katsifarakis. Rain Gardens as Integral Parts of Urban Sewage Systems-a Case Study in Thessaloniki, Greece. Procedia Engineering, 162, pp.426-432, 2016.
  [5] L. Rossman, Storm Water Management Model User’s Manual Version 5.1, US Environmental Protection Agency, 2015.
  [6] M. Abi Aad, M. Suidan and W. Shuster, "Modeling Techniques of Best Management Practices: Rain Barrels and Rain Gardens Using EPA SWMM-5," Journal of Hydrologic Engineering, vol 15, issue 6, June, 2010.
  [7] D. Vineyard, W. Ingwersen, T. Hawkins, X. Xue, B. Demeke and W. Shuster. Comparing Green and Grey Infrastructure Using Life Cycle Cost and Environmental Impact: A Rain Garden Case Study in Cincinnati, OH. JAWRA Journal of the American Water Resources Association, 51(5), pp.1342-1360, 2015.

  Citation
  Florante D. Poso, Jr., Lady Lyn E. Escaries, Coleen P. Clarita, Christine M. Fabian, Lovil Christian D. Moreno, Rowena O. Satira, "Rain Garden as an Alternative Flood Mitigation Technique Using Storm Water Management Model," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.1-8, 2020

• Open Access   Article

  Productivity Enhancement for SMEs Plastic Bottle Enterprise by Lean Technique Application

  Ubolrat Wangrakdiskul, Paisit Sapparot

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.9-13, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This research aimed to enhance productivity of plastic bottle production process focusing on High Density Polyethylene plastic bottles. There were 3 main stages of production process which divided to 13 steps operated by 8 workers. After analyzing the process, it found that cycle time was 5 seconds/pc with capacity 6,480 pieces/ 9 hours. At the 6th station; cutting bottle fins, it had the long cycle time leading to unmeet the target. In addition, at the 12th station of packing process, it is also the second-longest cycle time leading to delay work. On the contrary, low cycle time was found at the first main stage of mixing plastic beads. For improving productivity, lean technique focusing on 7 wastes was employed. Therefore, the 1st main stage station had been moved to operate at the end of working day for feeding on the next day process. For the 6th step, it was suggested to add the automatic machine for worker replacement. Furthermore, delay work occurred at the 12th step. It was improved by moving workers from the previous station to support work with the current workers at this station. It can be concluded that replacing automatic machine has reduced 3 workers. In addition, waiting time was also decreased. The cycle time was reduced from 5 seconds/ pc to 4 seconds/ pc which decreased by 20%. Production capacity can be enhanced from 6,480 pieces/ 9 hours to 8,100 pieces/ 9 hours which increased by 25%.

  Key-Words / Index Term
  High Density Polyethylene Plastic Bottle Plant, Productivity Improvement, Lean Manufacturing

  References
  [1] "https://www.diw.go.th/hawk/content.php?mode=data1search," [Online]. [Accessed 07 10 2020].
  [2] M. A. Spalding and A. M. Chatterjee, Handbook of Industrial, USA: Scrivener Publishing, 2018.
  [3] Introduction to Lean, Limerick, Ireland: LBS Partners, 2014.
  [4] D. CM, K. Tan and M. Lim , "Green as the new lean: How to use lean practices as a catalyst to greening your supply chain,," Journal of Cleaner Production, vol. 40, pp. 93-100, 2013.
  [5] A. Karim and K. Arif-Uz-Zaman, "A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations," Business Process Management Journal, Vols. Vol. 19 No. 1, , pp. 169-196, 2013.
  [6] A. Pool, J. Wijingaard and . D. Zee, "Lean planning in the semi-process industry: a case study," International Journal of Production Economics, vol. Vol. 1 No. 1, pp. 1-10., 2011.
  [7] A. P. Lacerda, A. R. Xambre and H. M. Alvelos, "Applying Value Stream Mapping to Eliminate Waste: A Case Study of an Original Equipment Manufacturer for the Automotive Industry," International Journal of Production Research , vol. 54 (6):, p. 1708–1720, 2015.
  [8] S. K. Tyagi, A. Choudhary, X. Cai and K. Yang, "Value stream mapping to reduce lead-time of a product development process," International Journal of Production Economics, vol. 160, p. 202–212., 2015.
  [9] N. Sinha and M. Matharu, "A comprehensive insight into Lean management: Literature reviewn and trends," Journal of Industrial Engineering and Management, vol. 12(2), pp. 302-317, 2019.
  [10] I. Hoque , P. Hasle and M. Maalouf, "Lean meeting buyer’s expectations, enhanced supplier productivity and compliance capabilities in garment industry," International Journal of Productivity and Performance Management, vol. Vol. 69 No. 7, pp. 1475-1494, 2020.
  [11] U. Wangrakdiskul, "Designing Queuing System for Public Hospitals in Thailand," in 2014 Tokyo International Conference on Engineering and Applied Science, Tokyo, 2014.
  [12] M. Kovacevic, M. Jovicic, M. Djapan and . I. Macuzic, "Lean thinking in healthcare: review of implementation results," International Journal for Quality Research, vol. 10, p. 219–229., 2016.
  

  Citation
  Ubolrat Wangrakdiskul, Paisit Sapparot, "Productivity Enhancement for SMEs Plastic Bottle Enterprise by Lean Technique Application," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.9-13, 2020

• Open Access   Article

  Slag Engineering Methodology for Enhanced De-Sulphurisation Capability in Si Deoxidised Steels

  Somnath Kumar, K K Keshari, A K Bandhyopadhyay, Abdhesh Prasad, Vikash Kumar, N. Sen, K Choudhury, R Kumar

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.14-21, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The quality requirement of customers is becoming more stringent w.r.t low level of sulphur in final product for a better mechanical property of steel. High sulphur in steel leads to poor internal quality in the form of cracks and sometimes causes breakout during continuous casting of steel. In past, different works have been conducted by many researchers to investigate desulphurisation process in steelmaking. Very few studies are available on desulphurisation of steels deoxidised with Si-Mn having a lower basicity top slag. High sulphur in steel was one of the bottlenecks in improving the quality of steel as well as a reason for lower throughput of continuous casters at Durgapur Steel Plant (DSP). De-Sulphurisation of silicon killed steel is a difficult task owing to the inherent nature of steel melt as well as ladle top slag. The present work was carried out in improving the desulphurisation level in steels subjected to silicon killing. Slag engineering was done in a novel way to make conditions more conducive for desulphurisation of steels. Deoxidation and flux addition practice was modified in an innovative way to decrease the SiO2 generation and also to reduce the oxygen potential of slag. Plant scale trials were conducted with modified deoxidation and flux addition practice. 60% De-S was achieved in heats i.e sulphur reduction from 0.039% (S in hot metal) to 0.015% (S in the final product). Process technology was established for the production of low sulphur steels at Durgapur Steel Plant.

  Key-Words / Index Term
  Steel Desulphurisation, Slag Engineering, Sulphide Capacity, Deoxidation, Inclusions, Clean Steel

  References
  [1] Bu?ko, B., J. Kijac, and M. Domec. "Optimalization slag composition in ladle furnace considering to effective steel desulfurization." Acta Metallurgica Slovaca, Vol.15, Issue.4, pp.93-99, 2009
  [2] Pretorius, Eugene, and Baker Refractories. "Fundamentals of EAF and ladle slags and ladle refining principles." Bakers Refractories , 2015
  [3] Andersson, Margareta AT, Pär G. Jönsson, and Mselly M. Nzotta. "Application of the sulphide capacity concept on high-basicity ladle slags used in bearing-steel production." ISIJ International , Vol.39. Issue.11, pp.1140-1149, 1999
  [4] Xu, Jian-fei, Fu-xiang Huang, and Xin-hua Wang. "Desulfurization Behavior and Mechanism of CaO-saturated Slag." Journal of Iron and Steel Research International, Vol.23, Issue.8, pp. 784-791, 2016
  [5] MM, Nzotta, Du Sichen, and S. Seetharaman. "Sulphide capacities in some multi component slag systems." ISIJ International, Vol. 38, Issue.11, pp. 1170-1179, 1998
  [6] Verein Deutscher Eisenhuttenleute (VDEh), Slag Atlas, Second Edition, Verlag Stahleisen mbH Publication, pp:258-259, 1995
  [7] Boström, Anna, “A model for multicomponent reactions between metal/slag using thermo-calc: applied for removal of sulphur during ladle treatment”. Diss. Institutionen för tillämpad processmetallurgi, 1997.
  [8] PG, Jönsson, Lage Jonsson, and Du Sichen. "Viscosities of LF slags and their impact on ladle refining." ISIJ International, Vol.37, Issue.5, pp. 484-491, 1997
  [9] Kumar Abhishek, Rajib Khanda, Somnath Kumar, Avinash Mishra, Sanjay Kumar Dhua, "Development of Process Technology for Making Electrode Quality Steel through Continuous Casting," International Journal of Scientific Research in Multidisciplinary Studies , Vol.6, Issue.10, pp.24-31, 2020

  Citation
  Somnath Kumar, K K Keshari, A K Bandhyopadhyay, Abdhesh Prasad, Vikash Kumar, N. Sen, K Choudhury, R Kumar, "Slag Engineering Methodology for Enhanced De-Sulphurisation Capability in Si Deoxidised Steels," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.14-21, 2020

• Open Access   Article

  Modeling the Rate of Mud Invasion in a Permeable Formation

  Kingdom Kponanyie Dune, Bright Bariakpoa Kinate, Adaobi Stephenie Nwosi-Anele

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.22-27, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The invasion of mud filtrates in to the permeable formation is an inevitable experience during drilling operations. It is usually associated with near-complete formation damage in the immediate vicinity of wellbore and the response of well log tools (especially the low penetration tools) are then greatly influenced by the mud filtrates instead of formation fluids which makes its interpretation more difficult. Hence, it is often necessary to know the possible spatial and time variation of mud filtrate concentrations in the porous media during invasion. This study presents a model technique for the numerical simulation of rate of mud invasion using a pressure transient analogy of mud concentration gradient. The model uses a finite difference element of 1000ft3 divided into five (5) uniform gridblocks of length, 2ft each. The results of the study showed clearly that from the time and space of invasion process initialisation, the mud concentration diffuses into the porous medium. At early time of invasion, the mud filtrate is continuously diffused into the porous media while at later stage of invasion, mud concentration builds from the inner gridblocks facilitating the formation of filter cake at the wellbore. Further analysis of the results reveals that the displacement of the in-situ formation fluid by the invading mud is not a piston-like phenomenon as shown by the spatial variation of the mud filtrates concentration during the invasion period. However, the concentration gradient can be used to define the extent of mud filtrate invasion into the permeable formation

  Key-Words / Index Term
  Mud Concentration, Mud Flow rate, Mud invasion,Numerical Simulation, Permeable Formation

  References
  [1] L.A.Calcada,C.M.Scheid,C.A.O.DeAraugo,A.T.A.Waldmann,A.L.Martins “Analysis of Dynamic and Static Filtration and Determination of Mud Cake Parameters,” Brazilian Journal of Petroleum and Gas, Vol.5, , pp.159-170, 2011.
  [2] S.O.Isehunwa, G.K.Falade “An of Approximate Theory of Static Filtration of Drilling Muds in Vertical Wells,” ARPN Journal of Engineering and Applied Sciences, Vol.7, No.1, pp.26-31, 2012
  [3] O.O.Akinsete, D.A. Adekoya ‘‘ Effects of Mud Filtrate Invasion on Well Log Measurements’’SPE –Nigerian Annual International Conference and Exhibition, 2-4 August 2016
  [4] M.S. Jesús, T.V. Carlos, ‘‘Quantitative Comparison of Processes of Oil- and Water-Based Mud-Filtrate Invasion and Corresponding Effects on Borehole Resistivity Measurements’’,Geophysics,Vol.74, pp. E57–E73 ,2009
  [5] S.Won, H.I. Bilgesu, S. Ameri. S, ‘‘Investigation of Mud-Filtrate Invasion Using Computational Fluid Dynamics .Society of Petroleum Engineers. SPE-117769, 2008
  [6] B. Anderson, J. Tabanou, ‘‘ The Quest for True Formation Resistivity’’, Technical Review, Vol.34, No.2 , pp 36-43, 2010
  [7] J. Wu, C.Torres-Verdi., K..Sepehrnoori, M. Delshad, ‘‘Numerical Simulation of Mud-Filtrate Invasion in Deviated well’’,Society of Petroleum Engineers (SPE),2004
  [8] M.E. Semmelbeck, S.A. Holditch, ‘‘ The Effects of Mud Filtrate Invasion on the Interpretation of Induction Logs’’ SPE Formation Evaluation, 1988
  [9] K. Ling, H. Zhang, Z. Shen, A. Ghalambor, G. Han, J. He, P. Pei, ‘‘A Comprehensive Approach To Estimate Invasion Radius Of Mud Filtrate To Evaluate Formation Damage Caused By Overbalanced Drilling’’ SPE International Symposium and Exhibition on Formation Damage, Lafayette, Louisiana. SPE-168184, 2014
  [10] J.M. Breitmeier, W. C. Tosch, M.A . Adewunmi, ‘‘Investigation of Radial Invasion of Mud Filtrate in Porous Media’’. SPWLA Thirtieth Annual Logging Symposium, 1989
  [11] A. David, A. Francois, D. Elizabeth, G. Peter, F. Edmond, W. Russ, ‘‘Mud Invasion ’’Resivited Oilfield Review. pp 10–23,1991
  [12] F.O. Alpak, E.B. V. Dussan,T.M. Habashy, V.C. Torres, ‘‘ Numerical Simulation of Mud-Filtrate Invasion in Horizontal Wells and Sensitivity Analysis of Array Induction Tools’’,Petrophysics,Vol.44. pp. 396–411,2003
  [13] K. Arunesh, K., ‘‘Fluid Loss as a Function of Position around the Wellbore’’AADE -10-DF-HO-18, 2010
  [14] J. Wu, V.C. Torres, S. Kamy, M. Delshad, ‘‘Numerical Simulation of Mud-Filtrate Invasion in DeviatedWells’’,Paper presented at the 2001 SPE Annual Technical Conference and Exhibition, New Orleans,2001
  [15] O.O . Akinsete, A.I. Raji, J.U. Akpabio, ‘‘Formation Damage Analysis Due to Filtrate Invasion in Deviated Wells: A Numerical Approach’’, British Journal of Applied Science & Technology. pp1-10, 2016
  [16] I. Salaudeen, S.O. Isehunwa, D.A. Dauda, I. Ayba, ‘‘ Analysis Of Formation Damage During The Drilling Of Horizontal Wells’’, International Journal of Scientific & Technology Research. pp56-63 ,2017

  Citation
  Kingdom Kponanyie Dune, Bright Bariakpoa Kinate, Adaobi Stephenie Nwosi-Anele, "Modeling the Rate of Mud Invasion in a Permeable Formation," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.22-27, 2020

• Open Access   Article

  Determine the parameters affecting on the pollution of the Orontes River by statistical analysis methods

  Lina Khouri

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.28-34, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This study investigates the quality of the Orontes River by conducting a statistical analysis at certain monitoring points.as 11 monitoring points were distributed along the river from the point of its entry into the Governorate of Homs until its exit from it, and for this purpose, methods of multivariate statistical analysis were applied (cluster analysis, Factor analysis). By using the statistical program /SPSS/ to find out the advantages of the variables, the results indicated the possibility of reducing the number of measured variables and reducing the number of monitoring points along the stream, as the cluster analysis showed the presence of two clusters (low pollution, polluted), which could reduce the number of sampling sites according to similar specifications. By applying the factor analysis to study the changes to compare the compositional patterns between clusters, It gave /4/ basic factors with the degree of measurement quality of 85.7% > 50%, and the best rate for explaining the total variance ratios is 87.86% of the total variance and includes (7) basic variables: (EC, Na, BOD, COD, NO3, DO, TSS).

  Key-Words / Index Term
  Orontes River Pollution, Statistical Analysis, Cluster Analysis, Factor Analysis, Multivariate Statistical Methods, River Water Quality

  References
  [1] W. Flint, "The Sustainable Development of Water Resources," Water Resources Update, Issue. 127, pp. 41-51, Feb 2004.
  [2] M.Denchack, "Water Pollution: Everything You Need to Know", Networks R (https://www.nrdc.org/stories/water-pollution-everything-you-need-know).
  [3] W. J. Cosgrove, D. P. Loucks, " Current and future challenges and research directions," Vol. 51, Issue. 6, pp. 4823-4839, 2015.
  [4] L. Matejicek, “ Modeling of Water Pollution in Urban Areas with GIS and Multivariate Statistical Methods,” Paper presented at the 1 st Biennial Meeting of iEMSs, Lugano, Switzerland, June 24-27, pp.60-65, 2002.
  [5] K. P. Singh, A. Malik, S. Sinha, D. Mohan, “Multivariate statistical techniques for the evaluation of spatial and temporal variations in water qualityof Gomti River (India)-a case study,” Water Research,Vol. 38, pp. 3980–3992, 2004.
  [6] K. Manoj, S. Ghosh, P. K. Padhy,“ haracterization and Classification of Hydrochemistry Using Multivariate raphical and Hydro Statistical Techniques,” Research journal of chemical sciencesVol 5, No 3, pp.32-42, 2013.
  [7] K. Rahman, S. Barua, S. Alam, A. Aalm, “Ecological Risk Assessment of Heavy Metals concentration in Sediment of the Shitalakhya River,” International Journal of scientific in Multidisciplinary studies, Vol. 6 Issue. 6, pp. 15-20, june 2020.
  [8] M.A.K.Shiker, " Multivariate Statistical Analysis," British jornal of scince,vol.6 No 1, pp 55, july 2012.
  [9] A.Mousa, "Statistical Data Analysis," Center for Advancement of Postgraduate Studies and Research in Engineering sciences,Faculty of Engineering - Cairo University, Vol. ,No.2372, 2005.
  [10] C.O. Adejoro, F. N. Ogwueleka, “Users Perception of Social Media Opportunities and Challenges,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.93, 2020.
  [11] Y. Qian, K. W. Migliaccio, Y. Wan, Y. Li, “Surface Water Quality Evaluation sing Multivariate Methodes and a New Water Quality Index in THe India River Lagoon, Florida,” Sharing Science, Vol 43, No 8 , pp.21, 2007.
  [12] A. Field, “Discovering Statistics Using SPSS third edition,” (3 ed.). London: SAGE Publication, 2009.

  Citation
  Lina Khouri, "Determine the parameters affecting on the pollution of the Orontes River by statistical analysis methods," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.28-34, 2020

• Open Access   Article

  Application of Fuzzy Expert system in Estimating LaborProductivity

  Deepak Sharma, GarimaSolanki, Anjali Upadhyay

  Survey Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.35-39, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Fuzzy expert system have been utilized to tackle complex issues productively for the situation where data accessible is in spellbinding structure as opposed to quantitative number. This examination has intended to utilize Fuzzy expert system to assess the work creation rates through joining the impact of subjective and quantitative factor. In this examination, to assess the Work Efficiency in any industry we have utilized wide information assortment, poll study and by talking industry personals. This task works fundamental expects to distinguish the variables influencing the Work Profitability in any industry. The 50 components of assessing Work Efficiency are browsed the modern tasks. The review was done through wide information assortment, poll overview and specialists sees which are then positioned through RII scale. The positioning is done through RII and afterward it is utilized for choosing highest level components which are answerable for Work Efficiency.

  Key-Words / Index Term
  Labor Productivity, fuzzy logic, simulation, Prediction, decision-making, soft computing, MATLAB

  References
  [1] Kapil Mittal &Puran Chandra Tewari& Dinesh Khanduja, (2017) “Productivity improvement under manufacturing environment using Shainin system and fuzzy analytical hierarchy process: a case study”, The International Journal of Advanced Manufacturing Technology, Volume 92, Issue 1–4, pp 407–421.
  [2] Toly Chen, (2016) “New fuzzy method for improving the precision of productivity predictions for a factory”, Neural Computation & Application, Issue 11.
  [3] Thomas Czumanski and Hermann Lödding, (2016) “State-based analysis of labor productivity”, International Journal of Production Research, Taylor and Francis, Volume 54, Issue 10.
  [4] PouyaNojedehi&FarnadNasirzadeh, (2016) “A Hybrid Simulation Approach to Model and Improve Construction Labor Productivity”, KSCE Journal of Civil Engineering, Volume 21, Issue 5, pp 1516–1524.
  [5] Laptal Oral, Mustafa Oral, Mehmet Anda, (2016) “Construction Crew Productivity Prediction: Application of Two Novel Methods”, Int. Journal Civil Engineering, pp 181–186.
  [6] SusantiRachman, Mark A Gregory, Seema W Narayan, (2015) “The Role of ICT Services on Indonesian Small to Medium Enterprise Productivity”, International Telecommunication Networks, pp 166-172.
  [7] SeungjunAhn, A. J. Antony Chettupuzha, Ronald Ekyalimpa, (2015) “Fuzzy Cognitive Maps as a Tool for Modeling Construction Labor Productivity” Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS).
  [8] Aminah Robinson Fayek, Abraham AssefaTsehayae, (2012) “Modeling Construction Labor Productivity Using Fuzzy Logic and Exploring the Use of Fuzzy Hybrid Techniques” Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Berkeley, CA.
  [9] MohdFarisKhamidi, Yap KeemSiah, Muhammad Saqib, “Application of Fuzzy Expert Systems for Construction Labor Productivity Estimation”, International Conference on Computer & Information Science (ICCIS), IEEE, Volume 2, pp 506-511.
  [10] Gilberto A. Corona-Suárez, Simaan M. AbouRizk, and Stanislav Karapetrovic, (2014) “Simulation-Based Fuzzy Logic Approach to Assessing the Effect of Project Quality Management on Construction Performance”, Journal of Quality and Reliability Engineering, Hindawi Publishing Corporation, Volume 2014, Article ID 203427, vol. 2014, 18 pages.
  [11] M. Arashpour, (2015) “Analysis of Workflow Variability and Its Impacts on Productivity and Performance in Construction of Multistory Buildings”, Journal of Management in Engineering, Vol. 31, Issue 6.
  [12] TJ Coelli TJ, DSP Rao, CJ O’Donnell, GE Battese, (2005) “An introduction to efficiency and productivity analysis”, second edition,Springer, Berlin.
  [13] K. El-Gohary, R. Aziz, (2014) “Factors influencing construction labor productivity in Egypt”, Journal of Management in Engineering, Vol. 30, No. 1, pp. 1-9.
  [14] YC Wang, T Chen, (2013) “A fuzzy collaborative forecasting approach for forecasting the productivity of a factory”, Adv Mechanical Engineering 5: page 1–13.
  [15] F. Nasirzadeh, A. Afshar, M. Khanzadi, and S. Howick, (2008) “Integrating system dynamics and fuzzy logic modeling for construction risk management”, Construction Management and Economics, Vol. 26, issue 11, pp. 1197-1212.
  [16] Ars hinder, A Kanda, SG Deshmukh, (2007) “Coordination in supply chains: an evaluation using fuzzy logic”, Production Plan Control 18: Volume 18, Issue 5, pp. 420– 435.
  [17] J Beg, MS Shunmugam, (2003) “Application of fuzzy logic in the selection of part orientation and probe orientation sequencing for prismatic parts” International Journal Prod Res 41, Volume 41, Issue 12, pp.2799–2815.
  [18] S Nandy, PP Sarkar, A Das, (2012) “Training a feed-forward neural network with artificial bee colony-based backpropagation method”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 4, No 4, 33-46
  [19] H. R. Thomas, and R. D. Ellis, (2009) “Fundamental Principles of Weather Mitigation.” Practice Periodical on Structural Design and Construction, Vol. 14, No. 1, pp 29-35.
  [20] M Oral, E Oral, (2007) “A computer-based system for documentation and monitoring of construction labor productivity”, CIB 24th W78 conference, Maribor.
  [21] AK Sangaiah, PR Subramaniam, X Zheng, (2015) “A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors”, Neural Computing and Applications, Volume 26, Issue 5, pp 1025–1040.
  [22] T Chen, (2015) “A fuzzy back-propagation network approach for planning actions to shorten the cycle time of a job in dynamic random-access memory manufacturing”, Neural Computing and Applications: Volume 26, Issue 8, pp 1813–1825.
  [23] G Peters, (1994) “Fuzzy linear regression with fuzzy intervals”, Fuzzy Sets Syst 63: pp 45–55.
  [24] Chen T, (2007) “Evaluating the mid-term competitiveness of a product in a semiconductor fabrication factory with a systematic procedure”, Computational Ind Engineering 53: pp. 499–513.
  [25] Dissanayake M, Fayek AR, Russell AD, Pedrycz W, (2005) “A hybrid neural network for predicting construction labour productivity”, Computational Civil Engineering ASCE, PP. 303–328.
  [26] Di Fana, Chris K.Y. Lob, Andy C.L. Yeungc, T.C.E. Chengc, (2018) “The impact of corporate label change on long-term labor productivity”, Journal of Business Research 86, pp 96–108.
  [27] Michele Battisti, Massimo Del Gatto, Christopher F. Parmeter, (2017) “Labor productivity growth: disentangling technology and capital accumulation”, Journal of Economic Growth, Springer, Volume 23, Issue 1, pp 111–143.
  [28] Dave R. Bonham, Paul M. Goodrum, Ray Littlejohn, Mohammed A. Albertha, (2017) “Application of Data Mining Techniques to Quantify the Relative Influence of Design and Installation Characteristics on Labor Productivity”, Journal of Construction Engineering Management, American Society of Civil Engineers, Volume 143, Issue 8.
  [29] Mario Coccia, (2017) “Optimization in R&D intensity and tax on corporate profits for supporting labor productivity of nations”, The Journal of Technology Transfer, Springer, Volume 43, Issue 3, pp 792–814.
  [30] Nirajan Mani, Krishna P. Kisi, Eddy M. Rojas and E. Terence Foster, (2017) “Estimating Construction Labor Productivity Frontier: Pilot Study”, Journal of Construction Engineering Management, American Society of Civil Engineers, Volume 143, Issue 10.
  [31] Joanna Tyrowicz, Lucas van der Velde, Jan Svejnar, (2016) “Effects of Labor Reallocation on Productivity and Inequality—Insights from Studies on Transition”, Journal of Economic Surveys, John Wiley & Sons Ltd, Vol. 00, No. 0, pp. 1–21.
  [32] Christopher F. Baum, Hans Lööf, PardisNabavi& Andreas Stephan, (2016) “A new approach to estimation of the R&D–innovation productivity relationship”, Economics of Innovation and New Technology, Volume 26, Issue 1-2, Pages 121-133.
  [33] Erik A. Poirier, Sheryl Staub-French, Daniel Forgues, (2015) “Measuring the impact of BIM on labor productivity in a small specialty contracting enterprise through action-research”, Automation in Construction 58, Elsevier, Volume 58, Pages 74-84.
  [34] GholamrezaHeravi and Ehsan Eslamdoost, (2015) “Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity”, Journal Construction Eng. Management, American Society of Civil Engineers, Volume 141 Issue 10.
  [35] Rohit B. Ganorkar and K. N. Agrawal, (2018) “Process Optimization in Continuous Corrugation Line at Steel Processing Industry”, Industrial Engineering Journal, Volume 10, Issue 7, pp 27-31.
  [36] Divyesh Prakash Trivedi, B. B Ahu and N. R. Rajhans, (2018) “Process Optimization of Plastic Tap Manufacturing Company”, Industrial Engineering Journal, Volume 10 (10).
  [37] Ravi Nagaich Shukla, Apratul Chandra and Rishabh Mishra, (2018) “Productivity Improvement by Using Method Study in Automobile Industry: A Case Study”, Industrial Engineering Journal, Volume 10 (9).

  Citation
  Deepak Sharma, GarimaSolanki, Anjali Upadhyay, "Application of Fuzzy Expert system in Estimating LaborProductivity," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.35-39, 2020

• Open Access   Article

  Efficiency of a Working Hydraulic Ram Pump System

  Soumadeep Chatterjee, Tanmoy Saha, Soubhonik Banerjee

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.40-46, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Hydraulic Ram Pump like any other kind of pump delivers water. It is durable and runs without electricity. It can deliver water from one point to another point at a high head. The main advantage of Hydraulic Ram Pump is that it is cheaper & can run at a stretch of 5-10years continuously without stopping or without maintenance. This interesting pump is thus considered for experiments & further explored in this paper. All the findings mentioned are found out from a working model of the hydraulic ram pump which was made within a very reasonable price and cheaper than existing pumps. This paper also focuses on the head & efficiency of the Hydraulic Ram Pump.

  Key-Words / Index Term
  Hydraulic Ram Pump, Energy Conservation, Fluid Mechanics

  References
  [1]. N S M Hussin, “Design and analysis of hydraulic ram water pumping system”, IOP Conf. Series: Journal of Physics: Conf. Series 908 (2017) 012052, Vol. 908, p. 1-9, 2017
  [2]. Akshay Kherde, “Research Paper on Hydraulic Ram Pump”, IRE Journal, Vol.3, Issue.10, p. 282-284, 2020
  [3]. Wanchai Asvapoositkul, “Determination of Hydraulic Ram Pump Performance: Experimental Results”, Advances in Civil Engineering, Vol. 2019, p. 1-9, 2019
  [4]. Shuaibu Ndache Mohammed, “Design and Construction of a Hydraulic Ram Pump”, Leonardo Electronic Journal of Practices and Technologies, Vol. 2007, Issue.11, p. 59-70, 2007
  [5]. Nikita Mishra, “Effect of Water Hammering Action on Performance of Hydraulic Ram Pump”, International Journal of Scientific and Research Publications, Vol. 8, Issue.12, p. 396-399, 2018

  Citation
  Soumadeep Chatterjee, Tanmoy Saha, Soubhonik Banerjee, "Efficiency of a Working Hydraulic Ram Pump System," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.40-46, 2020

• Open Access   Article

  Digital VLSI for Neural Networks

  Priyanjali Jain, Priyanshu Jain

  Survey Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.47-52, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  This chapter discusses digital electronic implementations of ANNs. First, we look at the differences between digital and analog design techniques with a focus on cost- performance trade-offs. Second, we consider the use of traditional processors in parallel configurations for ANN emulation. Third, to convey a sense of some of the issues involved in designing digital structures for ANN emulation, a custom digital ANN processor is discussed: the Adaptive Solutions CNAPS. Although this chip is no longer produced, it is still being used. It’s simple architecture makes it a good vehicle to understand the trade-offs inherent in emulating neural structures digitally. And fourth, we look briefly at FPGA technology as a promising alternative for digital implementation of ANNs.

  Key-Words / Index Term
  ANN , CNAPS, FPGA

  References
  [1] Ambros-Ingerson, J., R. Granger, et al., 1990, Simulation of Paleocortex Performs Hierarchical Clustering, in Science 247, pp.1344-1348.
  [2] Bailey, J., 1993, A VLSI Interconnect Strategy for Biologically Inspired Artificial Neural Networks, PhD Thesis, Department of Computer Science / Engineering, Beaverton, OR, Oregon Graduate Institute.
  [3] Bailey, J. and D. Hammerstrom, 1988, Why VLSI Implementations of Associative VLCNs Require Connection Multiplexing, 1988 International Conference on Neural Networks, pp.173-180.
  [4] Boahen, K. A., 2000, Point-to-Point Connectivity Between Neuromorphic Chips Using Address Events, IEEE Transactions on Circuits and Systems II - Analog and Digital Signal Processing , 47(5), pp.416-434.
  [5] Braitenberg, V. and A. Schüz, 1998, Cortex: Statistics and Geometry of Neuronal Connectivity , Springer-Verlag.
  [6] Chua, L. and T. Roska, 2001, Cellular Neural Networks and Visual Computing , Cambridge University Press.
  [7] Fahlman, S. E. and M. Hoehfeld, 1992, Learning with Limited Numerical Precision Using the Cascade-Correlation Algorithm, IEEE Transactions on Neural Networks , 3(4), pp. 602-611.
  [8] Hammerstrom, D., 1995, A Digital VLSI Architecture for Real-World Applications, An Introduction to Neural and Electronic Networks , S. F. Zornetzer, J. L. Davis, C. Lau and T. McKenna, San Diego, CA, Academic Press, pp.335-358.
  [9] Hatano, F. and et al., 1999, Implementation of Cell Array Neuro-Processor by Using FPGA, International Joint Conference on Artificial Neural Networks, Washington DC, IEEE.
  [10] Hennessy, J. L. and D. A. Patterson, 1991, Computer Architecture: A Quantitative Approach , Palo Alto, CA, Morgan Kaufmann.
  [11] Intel, 2001, IA-32 Intel Architecture Software Developer’s Manual, Volume 1: Basic Architecture, Intel, 2001.
  [12] Reschke, C., T. Sterling, et al., 1996, A Design Study of Alternative Network Topologies for the Beowulf Parallel Workstation , High Performance and Distributed Computing.
  [13] Shan, H., J. P. Singh, et al., 2000, A Comparison of Three Programming Models for Adaptive Applications on the Origin2000 , Proceedings of SC2000, Dallas, TX.
  [14] Sharma, A. K., 1998, Programmable Logic Handbook - PLDs, CPLDs & FPGAs , McGraw-Hill Handbooks.

  Citation
  Priyanjali Jain, Priyanshu Jain, "Digital VLSI for Neural Networks," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.47-52, 2020

• Open Access   Article

  Experimental Investigation on Carbon Dioxide Absorbing Paver Blocks

  Pradeepa P.

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.53-55, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Global warming is caused due to the emission of greenhouse gases such as carbon dioxide, methane, nitrous oxide. 72% of totally emitted greenhouse gases are CO2. Carbon dioxide emission is the most important causes of global warming, most of the CO2 has been released from the vehicles. To control this CO2, absorbing a paver block was made by using various materials such as lime, potassium hydroxide, seaweeds, and potato peels in various proportions in M35 grade concrete and the absorption test were done by two methods such as carbonation and titration. The compressive strength is also tested for 7 days and 28 days. The experimental work has been carried out and the results obtained are presented and discussed in this report.

  Key-Words / Index Term
  Lime, Potassium hydroxide, Seaweeds, Potatopeel

  References
  [1]. Michael North and James Clark,”CO2 Capturing can be achieved using waste-derived materials” Technology Org., PP 46-52, Aug 2016.
  [2]. Prathikkulkarn and Dr. A. Muthadhi,” Seaweed as an integral curing agent and strengthening in concrete-A Review” in SSRG International Journal of Civil Engineering (SSRG-IJCE)-Volume 4 Issue 6-June 2017.
  [3]. IS 10262:2019-Concrete mix proportioning
  [4]. IS 383:1970 – Specification for coarse and fine aggregate from the natural sources of concrete,
  [5]. IS 15658:2006 – Precast concrete blocks for paving specifications.
  

  Citation
  Pradeepa P. , "Experimental Investigation on Carbon Dioxide Absorbing Paver Blocks," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.53-55, 2020

• Open Access   Article

  Improved Bandwidth MEMS Dual-Resonant Electromagnetic Energy Harvesting

  Mohammad Reza Balazadeh Bahar, Manouchehr Bahrami, Mohammad Bagher Bannae Sharifian

  Research Paper | Journal-Paper (WAJES)

  Vol.7 , Issue.4 , pp.56-62, Dec-2020

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  In this article, analysis of dual-resonant electromagnetic micro generator is performed. Also, output power, bandwidth and performance of dual-resonant systems are discussed. Multi-resonant micro generator exhibits higher bandwidth in comparison with resonant mode structure. The micro generator is designed to convert 1 – 3 Hz heart beat mechanical vibrations to output electrical power. MEMS technology is utilized to construct the device in tiny dimensions. Finite Element Analysis (FEA) software is utilized to determine the mechanical and electromagnetic characteristics of the power harvester. For 2 Hz and 2.1 Hz resonant frequencies, the output power of 0.30568 µW and 0.33746 µW are achieved, respectively. The maximum overall output power is 0.3794 µW. The achieved results illustrate that, the dual-resonant system exhibits better and wider frequency response in comparison with resonant system.

  Key-Words / Index Term
  Electromagnetic micro generator, dual-resonant, mechanical vibrations, electrical power, MEMS (Micro Electro Mechanical Systems), FEA

  References
  [1] J. Ch. Park, D. H. Bang, and J. Y. Park, Micro-Fabricated Electromagnetic Power Generator to Scavenge Low Ambient Vibration, IEEE transactions on magnetics, Vol. 46, No. 6, June 2010.
  [2] P. Podder, P. Constantinou, D. Mallick, and S. Roy, Silicon MEMS bistable electromagnetic vibration energy harvester using double-layer micro-coils, Journal of Physics: Conference Series, 660, 2015.
  [3] P. Podder, P. Constantinou, D. Mallick, A. Amann, and S. Roy, Magnetic Tuning of Nonlinear MEMS Electromagnetic Vibration Energy Harvester, Journal of Microelectromechanical Systems, 2017.
  [4] S. Roundy, P. K. Wright, and J. Rabaey, A study of low level vibrations as a power source for wireless sensor nodes, Comput. Commun., Vol. 26, No. 11, pp. 1131–1144, Jul. 2003.
  [5] S. P. Beeby, M. J. Tudor, and N. M. White, Energy harvesting vibration sources for microsystems applications, Meas. Sci. Technol., Vol. 17, pp. R175–195, Oct. 2006.
  [6] R. M. Siddique, Sh. Mahmud, and B. Heyst, A comprehensive review on vibration based micro power generators using electromagnetic and piezoelectric transducer mechanisms, Energy conversion and management, 106, pp. 728–747, 2015.
  [7] T. Sato, and H. Igarashi, A Chaotic Vibration Energy Harvester Using Magnetic Material, Smart Mater. Struct., 2015.
  [8] Kumar, S. S. Balpande, and S. C. Anjankar, Electromagnetic Energy Harvester for Low Frequency Vibrations using MEMS, 7th International conference on communication, computing and virtualization 2016, Procedia Computer Science, 79, pp. 785 – 792, 2016.
  [9] K. El-Rayes, S. Gabran, E. Abdel-Rahman, and W. Melek, Variable-flux Biaxial Vibration Energy Harvester, IEEE Sensors Journal, Vol. 18, Issue 8, 2018.
  [10] L. B. Zhang, H. L. Dai, Y. W. Yang, L. Wang, Design of high-efficiency electromagnetic energy harvester based on a rolling magnet, Energy Conversion and Management, p.p. 202–210, 2019.
  [11] M. H. S. Alrashdan, A. A. Hamzah, B. Y. Majlis, Design and optimization of cantilever based piezoelectric micro power generator for cardiac pacemaker, Microsyst Technol, 21:1607–1617, DOI 10.1007/s00542-014-2334-1, 2015.
  [12] H. Madinei, H. HaddadKhodaparast, S. Adhikari, M. I. Friswell, Design of MEMS piezoelectric harvesters with electrostatically adjustable resonance frequency, Mechanical Systems and Signal Processing, 81 360–374, 2016.
  [13] K. Tao, J. Miao, S. W. Lye, X. Hu, Sandwich-structured two-dimensional MEMS electret power generator for low-level ambient vibrational energy harvesting, Sensors and Actuators A, 228, 95–103, 2015.
  [14] N. Wada, N. Horiuchi, K. Mukougawa, K. Nozaki, M. Nakamura, A. Nagai, T. Okura, K. Yamashita, Electrostatic induction power generator using hydroxyapatite ceramic electrets, Materials Research Bulletin, 74, 50–56, 2016.
  [15] J. Lueke, and W. A. Moussa, MEMS-Based Power Generation Techniques for Implantable Biosensing Applications, Sensors, 11, pp. 1433-1460; doi:10.3390/s110201433, 2011.
  [16] L. Dal Bo, P. Gardonio, E. Turco, Analysis and scaling study of vibration energy harvesting with reactive electromagnetic and piezoelectric transducers, Journal of Sound and Vibration, 2020.
  [17] P. D. Mitcheson, T. C. Green, E. M. Yeatman, and A. S. Holmes, Architectures for Vibration-Driven Micropower Generators, JOURNAL OF MICROELECTROMECHANICAL SYSTEMS, 2004.
  [18] www.comsol.com
  [19] PFENNIGER, M. JONSSON, A. ZURBUCHEN, V. M. KOCH, and R. VOGEL, Energy Harvesting from the Cardiovascular System, or How to Get a Little Help from Yourself, Annals of Biomedical Engineering, 2013.
  [20] altairhyperworks.com/product/flux
  [21] D. Mallick, P. Constantinou, P. Podder, S. Roy, Multi-frequency MEMS electromagnetic energy harvesting, Sensors and Actuators A: Physical, 2017.

  Citation
  Mohammad Reza Balazadeh Bahar, Manouchehr Bahrami, Mohammad Bagher Bannae Sharifian , "Improved Bandwidth MEMS Dual-Resonant Electromagnetic Energy Harvesting," World Academics Journal of Engineering Sciences, Vol.7, Issue.4, pp.56-62, 2020