ORIGINAL_ARTICLE
Transmission loss allocation in bilateral or multilateral transaction-based markets
In this paper, the problem of transmission loss allocation has been studied and a new method for loss allocation in transaction-based markets has been proposed. To further this end, first transmission line loss equations were used with respect to bus injected currents. The share of each bus from the mentioned transmission line losses was determined. Then, this method was applied to the total network transmission lines. While considering the available transactions, the share of each bus from the total losses was acquired. The proposed method is based on the main network relations and no simplifying assumption has been used. Finally, the proposed method is based on a typical network.
https://www.energyequipsys.com/article_30601_649ab729067d5b291df5f54d460e6cf4.pdf
2018-03-01
1
6
10.22059/ees.2018.30601
Loss Allocation
Sending and Receiving Power
Multilateral Transaction
Rahmat
Aazami
azami.rahmat@yahoo.com
1
Faculty of Engineering, Ilam University, Ilam, Iran
AUTHOR
Amin
Moradkhani
aminmoradkhani@yahoo.com
2
Faculty of Engineering, Ilam University, Ilam, Iran
LEAD_AUTHOR
[1] Ilic M., Galiana F., Fink L., Power Systems Restructuring: Engineering and Economics, Norwell, MA, Kluwer (1998).
1
[2] Sheblé G. B., Computational Auction Mechanisms for Restructured Power Industry Operation, Norwell, MA, Kluwer (1999).
2
[3] Conejo A.J., Arroyo J.M., Alguacil N., Guijarro A.L., Transmission Loss Allocation, A Comparison of Different Practical Algorithms, IEEE Transaction on Power Systems (2002) 17(3).
3
[4] Bialek J. W., Tracing the Flow of Electricity, IEE Proceedings - Generation, Transmission and Distribution (1996) 143: 313-320
4
[5] Kirschen D., Strbac G., Tracing Active and Reactive Power between Generators and Loads Using Real and Imaginary Currents, IEEE Transactions on Power Systems (1999) 14:1312-1319.
5
[6] Rau N. S., Radial Equivalents to Map Networks to Market Formats, IEEE Transactions on Power Systems (2001) 16(4): 856-861.
6
[7] Bialek J.W., Tracing the Flow of Electricity, IEE Proceedings - Generation, Transmission and Distribution (1996) 143: 313–320.
7
[8] Macqueen C. N., M.R. Irving, An Algorithm for the Allocation of Distribution System Demand and Energy Losses, IEEE Transactions on Power Systems (1996)11 (1):147–156
8
[9] Kirschen D., Allan R., Strbac G., Contributions of Individual Generators to Loads and Flows, IEEE Transactions on Power Systems (1997)12 (1): 52–60
9
[10] Tsukamoto Y., Iyoda I., Allocation of Fixed Transmission Cost to Wheeling Transactions by Cooperative Game Theory, IEEE Transactions on Power Systems (1996)11 (2): 620-627
10
[11] Chang Y.C., Lu C. N., An Electricity Tracing Method with Application to Power Loss Allocation, International Journal of Electrical Power & Energy Systems (2000) 23 (1): 13-17
11
[12] Gomez Exposito A., Riquelme Santos J. M., Gonzalez Garcia T., Ruiz Velasco E. A., Fair Allocation of Transmission Power Losses, IEEE Transactions on Power Systems (2000) 15(1): 184–188.
12
[13] Galiana F. D., Phelan M., Allocation of Transmission Losses to Bilateral Contracts in a Competitive Environment, IEEE Transactions on Power Systems (2000) 15(1):143–150.
13
[14] Conejo A. J., Z-Bus Loss Allocation, IEEE Transactions on Power Systems (2001) 16: 105-110.
14
[15] Adsoongnoen C., Ongsakul W., Maurer C., Haubrich H., Transmission Pricing Using the Exact Power and Loss Allocation Method for Bilateral Contracts in a Deregulated Electricity Supply Industry, European Transactions on Electrical Power (2007)17:240–254 DOI: 10.1002/etep.131.
15
[16] Kazemi A., Jadid S., Andami H., A Circuit Based Method for Multiarea Transmission Networks Loss Allocation, European Transactions on Electrical Power (2008) 18:753–766 DOI: 10.1002/etep.225.
16
[17] Aazami R., Monsef H., A Directional-Based Branches Current Method for Transmission Loss Allocation in the Pool-Based Electricity Market, Energy Equipment and Systems (2016)4(2): 177-187. DOI: 10.22059/ees.2016.23036.
17
[18] Enshaee P., Enshaee A., Approach to Evaluate Active Loss Contributions for Transmission Systems, IET Science, Measurement & Technology (2016) 10(5): 456–466.
18
[19] Kargarian A., Raoofat M., Mohammadi M., Artificial Intelligence–Based Loss Allocation Algorithm in Open Access Environments, Journal of Energy Engineering (2014)140 (2): 1–9.
19
ORIGINAL_ARTICLE
Drag coefficient and strouhal number analysis of a rectangular probe in a two-phase cross flow
In some case of laboratory and industrial applications, various kind of measurement instruments must be placed in a conduit, in which multiphase fluid flows. Vortex shedding for any immersed body in flow field is created with a frequency, which according to flow conditions such as flow rates, geometry of body, etc. may be constant or variable. Failure may happen, if this frequency is close to one of the natural frequencies of the instruments. These flows can play a significant role in long-term reliability and safety of industrial and laboratory systems. In this study, an Eulerian–Eulerian approach is employed to simulate Air-Water two-phase flow around a rectangular probe with different volume fractions (0.01-0.5) and Reynolds numbers (1000-3000). Two-phase flow characteristics around the probe have been analyzed numerically. The results show vortex shedding in all cases with distinct Strouhal number. In addition, results illustrate that shedding is intensified by increasing Reynolds number. In order to validate the results, fraction of inlet volume was set to zero, and drag coefficient and its relation with low Reynolds number (1000-3000) in single phase flow were compared to experimental and numerical results in published article. The results show a complete agreement between the simulation and available data.
https://www.energyequipsys.com/article_30607_2035cc8a4526b354b82829df875169f5.pdf
2018-03-01
7
15
10.22059/ees.2018.30607
Two-Phase Cross Flow
Strouhal Number
Rectangular Probe
Drag coefficient
Erfan
Kosari
erfan.kosari@gmail.com
1
Mechanical Engineering Department, University of California, Riverside, CA, USA
LEAD_AUTHOR
Ali
Rahnama
2
School of Mechanical Engineering, University of Tehran, Tehran, Iran
AUTHOR
Mahyar
Momen
mahyar.momen90@gmail.com
3
School of Mechanical Engineering, KNT, University of Technology, Tehran, Iran
AUTHOR
Pedram
Hanafizadeh
hanafizadeh@ut.ac.ir
4
School of Mechanical Engineering, University of Tehran, Tehran, Iran
AUTHOR
Mohammad Mahdi
Rastegardoost
5
School of Mechanical Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Okajima A., Sugitani K., Mizota T., Flow Around a Pair of Circular Cylinders Arranged Side by Side at High Reynolds Numbers, Transactions of the JSME (1986) 52 (480): 2844-2850.
1
[2] Yokosawa M., Kozawa Y., Inoue A., Aoki S., Studies on Two-Phase Cross Flow, Part II: Transition Reynolds Number and Drag Coefficient, International Journal of Multiphase Flow (1986) 12(2)169-184.
2
[3] Artemiev V. K., Kornienko Yu. N., Numerical Modeling of Influence Non-Monotonic Profile of Gas (vapor) Content on a Distribution of Velocity and Temperature in a Two-Phase Bubbly Flow, Proceedings, 3rd Russian National Conference on Heat Transfer, Moscow, Russia (2002)5:41–44.
3
[4] Zaichik L. I., Skibin A. P., Soloviev S. L., Simulation of the Distribution of Bubbles in a Turbulent Liquid Using a Diffusion-Inertia Model, International Journal of High Temp (2004) 42:111–118.
4
[5] Kamp A., Colin C., Fabre J., The Local Structure of a Turbulent Bubble Pipe Flow under Different Gravity Conditions, Proceedings, 2nd International Conference on Multiphase Flow, Kyoto, Japan (1995) 3(P6).
5
[6] Antal S. P., Lahey Jr. R. T., Flaherty J. F., Analysis of Phase Distribution in Fully Developed Laminar Bubbly of Two-Phase Flow, International Journal of Multiphase Flow (1991)17:363–652.
6
[7] Lopez M. A., Lahey Jr. R. T., Jones O. C., Phase Distribution in Bubbly Two-Phase Flow in Vertical Ducts, International Journal of Multiphase Flow (1994) 20: 805–818.
7
[8] Carrica P. M., Drew D. A., Bonetto F., Lahey Jr. R. T., A Polydisperse Model for Bubbly Two-Phase Flow Around Surface Ship, International Journal of Multiphase Flow (1999) 25:257–305.
8
[9] Politano M. S., Carrica P. M., Converti J., A Model for Turbulent Polydisperse Two-Phase Flow in Vertical Channel, International Journal of Multiphase Flow (2003)29: 1153–1182.
9
[10] Troshko A. A., Hassan Y. A., A Two-Equation Turbulence Model of Turbulent Bubbly Flow, International Journal. of Multiphase Flow (2001) 27: 1965–2000.
10
[11] Lee S. L., Lahey Jr. R. T., Jones O. C., The Prediction of Two-Phase Turbulence and Phase Distribution Phenomena Using a Model, Japan Journal of Multiphase Flow (1989)3: 335–368.
11
[12] Simonin, C., Viollet, P.L., Predictions of an Oxygen Droplet Pulverization in a Compressible Subsonic Coflowing Hydrogen flow, Numerical Methods for Multiphase Flows (1990) 91: 65–82.
12
[13] Cokljat D., Slack M., Vasquez S.A., Bakker A., Montante G., Reynolds-Stress Model for Eulerian multiphase, Progress in Computational Fluid Dynamics, An International Journal (2006) 6(1/2/3): 168 – 178.
13
[14] Mathur S.R., Murthy J.Y., A Pressure Based Method for Unstructured Meshes, Numerical Heat Transfer (1997)31:195–216.
14
[15] Okajima A., Strouhal Numbers of Rectangular Cylinders, The Journal of Fluid Mechanics (1982)123: 379-398.
15
[16] Norberg C., Flow Around Rectangular Cylinders, Pressure Forces and Wake Frequencies, Journal of Wind Engineering and Industrial Aerodynamics (1993) 49: 187-196.
16
[17] Schiller L., Naumann Z., A Drag Coefficient Correlation (1935) 77: 318.
17
[18] Cokljat D., Ivanov V.A., Sarasola F.J., Vasquez S.A., Multiphase K-Epsilon Models for Unstructured Meshes, ASME Paper FEDSM2000-11282, Proceedings of ASME FEDSM 2000: Fluids Engineering Division Summer Meeting, Boston USA.
18
[19] Mathur S.R., Murthy J.Y., A Pressure Based Method for Unstructured Meshes, Numerical Heat Transfer (1997)31:195–216.
19
[20] Kim S.E., Mathur S.R., Murthy J.Y., Choudhury D., A Reynolds-Averaged Navier-Stokes Solver Using Unstructured Mesh-Based Finite-Volume Scheme, AIAA (1998) 98-0231.
20
[21] Kim S.E., Unstructured Mesh Based Reynolds Stress Transport Modeling of Complex Turbulent Shear Flows, AIAA (2001) 2001-0728.
21
[22] Cokljat D., Slack M., Vasquez S.A., Bakker A., Montante G., Reynolds-Stress Model for Eulerian multiphase, Progress in Computational Fluid Dynamics, An International Journal (2006) 6(1/2/3):168 – 178.
22
[23] Okajima A., Strouhal Numbers of Rectangular Cylinders, The Journal of Fluid Mechanics (1982) 123(1982): 379.
23
[24] Fabio Toshio K., Ribatski G., Void Fraction and Pressure Drop during External upward Two-Phase Crossflow in Tube Bundles–Part I: Experimental Investigation, International Journal of Heat and Fluid Flow 65 (2017): 200-209.
24
[25] Fabio Toshio K., Ribatski G., Void Fraction and Pressure Drop during External upward Two-Phase Crossflow in Tube Bundles–Part II: Predictive Methods, International Journal of Heat and Fluid Flow 65 (2017): 210-219.
25
[26] Hojati A., Hanafizadeh P., Effect of Inclination Pipe Angle on Oil-Water Two Phase Flow Patterns and Pressure Loss, In Proceedings of the Biennial Conference on Engineering Systems Design and Analysis ESDA2014, Copenhagen, Denmark (2014).
26
[27] Ghanbarzadeh S., Hanafizadeh P., Saidi M.H., Bozorgmehry R., Fuzzy Clustering of Vertical Two Phase Flow Regimes Based on Image Processing Technique, In Proceedings of the ASME 3rd Joint US-European Fluids Engineering Summer Meeting (2010) 303-313.
27
ORIGINAL_ARTICLE
A new approach for performance evaluation of energy-related enterprises
Oil is among the most effective and the largest industries in the world. Given that it supplies a large percentage of the world’s energy and plays a significant role in the national power and international credit of countries, it has a huge impact on our world today. Iran has huge oil reserves, and plays a key role in the exchange of the required energy in the world. In order to improve the performance of this critical industry, it is necessary to evaluate the performance of petroleum producing companies. The main purpose of this paper is to present the first three-stage data envelopment analysis-based approach, integrated with a balanced scorecard for performance evaluation of oil companies. Regarding the cause and effect relationships among different aspects of the balanced scorecard, its indicators are employed as input and output variables of the data envelopment analysis model and the efficiency is calculated. The results indicated that among the oil companies investigated in this paper, the National Iranian South Oil Company and Aravindan Oil & Gas Company recorded the highest and lowest efficiencies, respectively. The proposed approach by authors provides a valuable tool for managers in the oil industry to evaluate the performance and take action for performance improvement.
https://www.energyequipsys.com/article_30608_c2e2836d88e2f3b26e1814c9b54d291b.pdf
2018-03-01
16
26
10.22059/ees.2018.30608
Energy Enterprise
performance evaluation
Balanced Scorecard (BSC)
Three- Stage Data Envelopment Analysis (DEA)
Oil Company
Abdorrahman
Haeri
ahaeri@iust.ac.ir
1
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
LEAD_AUTHOR
Mostafa
Jafari
jafari@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Somayyeh
Danesh Asgari
s_daneshasgari@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
[1] Gholipour Khajeh M., Iranmanesh M., Keynia F., Energy Auditing in Cement Industry, A Case Study, Energy Equipment and Systems (2014) 2: 171-184.
1
[2] Wang D., Li S., Sueyoshi T., DEA Environmental Assessment on US Industrial Sectors, Investment for Improvement in Operational and Environmental Performance to Attain Corporate Sustainability, Energy Economics (2014) 45: 254-267.
2
[3] Sueyoshi T., Wang D., Sustainability Development for Supply Chain Management in US Petroleum Industry by DEA Environmental Assessment, Energy Economics (2014) 46: 360-374.
3
[4] Sueyoshi T., Goto M., Data Envelopment Analysis for Environmental Assessment: Comparison between Public and Private Ownership in Petroleum Industry, European Journal of Operational Research (2012) 216: 668-678.
4
[5] Mehdizadeh H., Alishah A., Hojjati Astani S., Study on Performance and Methods to Optimize Thermal Oil Boiler Efficiency in Cement Industry, Energy Equipment and Systems (2016) 4: 53-64.
5
[6] Saad S., Mohamed Udin Z., Hasnan N., Dynamic Supply Chain Capabilities: A Case Study in Oil and Gas Industry, International Journal of Supply Chain Management (2014) 3.
6
[7] Sedaghat A., Tahmasebi A., Kalbasi R., Moghimi Zand M., Performance Assessment of A Hybrid Fuel Cell and Micro Gas Turbine Power System, Energy Equipment and Systems (2013) 1: 59-74.
7
[8] Kaplan R. S., Norton D. P., The Balanced Scorecard--Measures That Drive Performance, Harvard Business Review (1992) 70: 71-79.
8
[9] Charnes A., Cooper W. W., Rhodes E., Measuring the Efficiency of Decision Making Units, European Journal of Operational Research (1978) 2: 429-444.
9
[10] Wang C. H., Chien Y. W., Combining Balanced Scorecard with Data Envelopment Analysis to Conduct Performance Diagnosis for Taiwanese LED Manufacturers, International Journal of Production Research (2016) 54: 5169-5181.
10
[11] Eilat H., Golany B., Shtub A., R&D Project Evaluation: An Integrated DEA and Balanced Scorecard Approach, Omega (2008) 36: 895-912.
11
[12] Chiang C. Y., Lin B., An Integration of Balanced Scorecards and Data Envelopment Analysis for Firm's Benchmarking Management, Total Quality Management (2009) 20: 1153-1172.
12
[13] Seo I. W., Lee D. H., Park, I. S., The Empirical Study About Constructing and Application of Performance Measurement System Based on an Integrated DEA Approach. In Convergence and Hybrid Information Technology, ICCIT'08, Third International Conference, IEEE, (2008) 1: 1018- 1024.
13
[14] De Oliveira A. E. L. R., Cicolin L. D. O. M., Evaluating the Logistics Performance of Brazils Corn Exports, A Proposal of Indicators, African Journal of Agricultural Research (2016) 11: 693-700.
14
[15] Chen T. Y., Chen L. H., DEA Performance Evaluation Based on BSC Indicators Incorporated: The Case of Semiconductor Industry, International Journal of Productivity and Performance Management (2007) 56: 335-357.
15
[16] Zervopoulos P. D., Brisimi T. S., Emrouznejad A., Cheng G., Performance Measurement with Multiple Interrelated Variables and Threshold Target Levels: Evidence from Retail Firms in the US, European Journal of Operational Research (2016) 250: 262-272.
16
[17] García Valderrama T., Revuelta Bordoy D., Rodríguez Cornejo V., Balanced Scorecard and Efficiency: Desing and Empirical Validation of strategic Map in the University by Means of DEA (2013).
17
[18] Amado C. A., Santos S. P., Marques P. M., Integrating the Data Envelopment Analysis and the Balanced Scorecard Approaches for Enhanced Performance Assessment, Omega (2012) 40: 390-403.
18
[19] Shafiee M., Lotfi F. H., Saleh H., Supply Chain Performance Evaluation with Data Envelopment Analysis and Balanced Scorecard Approach, Applied Mathematical Modelling (2014) 38: 5092-5112.
19
[20] Ittner C. D., Larcker D. F., Are Nonfinancial Measures Leading Indicators of Financial Performance, An Analysis of Customer Satisfaction, Journal of Accounting Research (1998) 36: 1-35.
20
[21] Liang C. J., Hou L. C., A Dynamic Connection of Balanced Scorecard Applied for the Hotel, Journal of Services Research (2007) 7: 91.
21
[22] Lucianetti L., The Impact of the Strategy Maps on Balanced Scorecard Performance, International Journal of Business Performance Management (2010) 12: 21-36.
22
[23] Färe R., Grosskopf S., Network DEA, Socio-Economic Planning Sciences (2000) 34: 35-49.
23
[24] Rabbani A., Zamani M., Yazdani-Chamzini A., Zavadskas E. K., Proposing a New Integrated Model Based on Sustainability Balanced Scorecard (SBSC) and MCDM Approaches by Using Linguistic Variables for the Performance Evaluation of Oil Producing Companies, Expert Systems with Applications (2014) 41: 7316-7327.
24
[25] Shadidi B., Haji Agha Alizade H., Ghobadian B., The Effect of a Novel Hybrid Nano-Catalyst in Diesel-Biodiesel Fuel Blends on the Energy Balance of a Diesel Engine, Energy Equipment and Systems (2017) 5: 59-69.
25
ORIGINAL_ARTICLE
Analysis of prediction models for wind energy characteristics, Case study: Karaj, Iran
Iran is a country completely dependent on fossil fuel resources. In order to obtain a diversity of energy sources, it requires other resources, especially renewable energy. Utilization of wind energy appears to be one of the most efficient ways of achieving sustainable development. The quantification of wind potential is a pivotal and essential initial step while developing strategies for the development of wind energy. This study presents an investigation of the potential of wind power, using two methods—Weibull and Rayleigh—at Karaj, the center of Alborz province of Iran. The wind speed data for a three-hour time interval measured over a 10-year period (2004–2015) was used to calculate and estimate the wind power generation potential. After calculating the factors related to power density and wind energy, it was concluded that data fitting via Weibull distribution was partly better than the Rayleigh distribution function. The RMSE values of Weibull and Rayleigh were respectively 0.018 and 0.013, and R2 values of Weibull and Rayleigh were 0.95 and 0.97 in Karaj for the years 2004–2015. The wind rose charts of Karaj for the 2004–2015 period show that the most prevalent wind direction is NW (North-West). The wind power density obtained indicates the region is not completely suitable for large on-grid wind farms and related investments. But the region can be suitable for off-grid applications such as water pumping and irrigation, lighting, electric fan, battery charging, and, as hybrid, with other power sources.
https://www.energyequipsys.com/article_30610_0516b12d16e7bba1c0dcdaa5747526a7.pdf
2018-03-01
27
37
10.22059/ees.2018.30610
Wind Power
Wind Energy
Weibull Function
Rayleigh Function
Hiva
Sadeghi
sadeghi.hiva1990@gmail.com
1
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
AUTHOR
Reza
Alimardani
rmardani@ut.ac.ir
2
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
LEAD_AUTHOR
Majid
Khanali
khanali@ut.ac.ir
3
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
AUTHOR
Ahmad
Omidi
ahmad_omidi1391@ut.ac.ir
4
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
AUTHOR
[1] Gabbasa M., Sopian K., Yaakob Z., Faraji Zonooz M.R., Fudholi,Nilofar Asim A., Review of the Energy Supply Status for Sustainable Development in the Organization of Islamic Conference, Renewable and Sustainable Energy Reviews (2013)28: 18-28.
1
[2] Belabes B. , Kaabache A., Guerri O., Evaluation of Wind Energy Potential and Estimation of Cost Using Wind Energy Turbines for Electricity Generation in North of Algeria, Renewable and Sustainable Energy Reviews (2015) 51: 1245-1255.
2
[3] Handri D. A., Al-Rwashdeh S.S., Al-Najideen M. I., Evaluation of Wind Energy Potential and Electricity Generation at Five Locations in Jordan, Sustainable Cities and Society (2015) 15: 135-143.
3
[4] Mostafaeipour, A., Dehghan-Niri, A.A., Kalantar V., Wind Energy Feasibility Study for City of Shahrbabak in Iran, Renewable and Sustainable Energy Reviews (2011) 15(6): 2545-2556.
4
[5] Alamdari P., Nematollahi O., Mirhosseini M., Assessment of Wind Energy in Iran, A Review, Renewable and Sustainable Energy Reviews (2012) 16.1: 836-860.
5
[6] Hsin-Fa F., Wind Energy Potential Assessment for the Offshore Areas of Taiwan West Coast and Penghu Archipelago, Renewable Energy (2014) 67: 237-241.
6
[7] Mohammadi K., Mostafaeipour A., Sabzpooshani M., Assessment of Solar and Wind Energy Potentials for Three Free Economic and Industrial Zones of Iran, Energy (2014) 67: 117-128.
7
[8] Dabbaghiyan A., Fazelpour F., Dehghan Abnavi M., A.Rosen M., Evaluation of Wind Energy Potential in Province of Bushehr, Iran, Renewable and Sustainable Energy Reviews (2016) 55: 455-466.
8
[9] Keyhani A., Ghasemi-Varnamkhasti M., Khanali M., Abbaszadeh R., An Assessment of Wind Energy Potential as a Power Generation Source in the Capital of Iran, Tehran, Energy (2010) 35(1): 188-201.
9
[10] Mostafaeipour A., Sedaghat A., Ghalishooyan Dinpashoh M., Evaluation of Wind Energy Potential as a Power Generation Source for Electricity Production in Binalood, Iran, Renewable Energy (2013) 52: 222-229.
10
[11] Mostafaeipour A., Jadidi M., Mohammadi K., Sedaghat A., An Analysis of Wind Energy Potential and Economic Evaluation in Zahedan, Iran, Renewable and Sustainable Energy Reviews (2014) 30: 641-650.
11
[12] Anonymous, 2015, Parsine, at: http:// www.parsine.com.
12
[13] Karthikeya B. R., Prabal S. Negi, Srikanth N., Wind Resource Assessment for Urban Renewable Energy Application in Singapore, Renewable Energy (2016) 87: 403-414.
13
[14] Ramachandra T. V., Rajeev K. J., Vamsee Krishna S., Shruthi B. V., WEPA: Wind Energy Potential Assessment-Spatial Decision Support System, Energy Education Science and Technology (2005) 14(2): 61-80.
14
[15] Tsang-Jung Ch., Yu-Ting W., Hua-Yi H., Chia-Ren Ch., Chun-Min L., Assessment of Wind Characteristics and Wind Turbine Characteristics in Taiwan, Renewable Energy (2003) 28(6): 851-871.
15
[16] Mirhosseini M., Sharifi F., Sedaghat A., Assessing the Wind Energy Potential Locations in Province of Semnan in Iran, Renewable and Sustainable Energy Reviews (2011) 15(1): 449-459.
16
[17] Elamouri M., Ben Amar F., Wind Energy Potential in Tunisia, Renewable Energy (2008) 33(4): 758-768.
17
[18] Pishgar-Komleh S. H., Keyhani A., Sefeedpari P., Wind Speed and Power Density Analysis Based on Weibull and Rayleigh Distributions (a case study: Firouzkooh county of Iran), Renewable and Sustainable Energy Reviews (2015) 42: 313-322.
18
[19] Dundar C, Inan D., Wind Energy Potential of Cesme, Turkey, Solar World Congress, August Taejon, Korea (2001).
19
[20] Islam M. R., Saidur R., Rahim N. A., Assessment of Wind Energy Potentiality at Kudat and Labuan, Malaysia Using Weibull Distribution Function, Energy (2011) 36(2): 985-992.
20
[21] Fazelpour F., Soltani N., Rosen M.A., Assessment of Wind Energy Potential and Economics in the North-Western Iranian Cities of Tabriz and Ardabil, Renewable and Sustainable Energy Reviews (2015) 45: 87-99.
21
[22] Najafi G., Ghobadian B., LLK1694-Wind Energy Resources and Development in Iran, Renew Sustain Energy (2011) 15(6): 2719–28.
22
[23] Kavak Akpinar E., Akpinar S., A Statistical Analysis of Wind Speed Data Used in Installation of Wind Energy Conversion Systems, The Journal Energy Conversion and Management (2005) 46: 515–32.
23
[24] Jaramillo O.A., Saldaña R., Miranda U., Wind Power Potential of Baja California sur, Mexico, Renewable Energy (2004) 29(13): 2087-100.
24
[25] Tizpar A., Satkin M., Roshan M.B., Armoudli Y., Wind Resource Assessment and Wind Power Potential of Mil-E Nader Region in Sistan and Baluchestan Province, Iran – Part 1, Annual Energy Estimation. Energy Conversion and Management (2014) 79: 273–280.
25
ORIGINAL_ARTICLE
Numerical simulation of turbulent flow around the dtmb4119 propeller in open water conditions
In this study, ANSYS-FLUENT packages are employed to simulate the turbulent flow around DTMB4119 propeller in open water conditions. In order to form a mesh, the multiple reference frame (MRF) methodology is used. The results are compared with the experimental results and a good conformity is obtained, which endorses numerical simulation. Furthermore, the turbulence model is used, which is superior to other turbulence models in modeling marine propellers. The investigation focuses on aspects related to the influence of the pressure coefficient and the advance coefficient on hydrodynamic performance and cavitation of the propeller. The results reveal that the pressure coefficient at first decreases and then augments as it moves from the leading edge to trailing edge in the suction surface. Moreover, by increasing the blade radius and its speed, the minimum pressure increases in a way that pressure coefficient reaches its minimum value. Furthermore, volume fraction of the vapor over the blades decreases as the advance coefficient increases. As a result, the possibility of cavitation decreases.
https://www.energyequipsys.com/article_30611_808407834ffbe7eea74b6ffc17935881.pdf
2018-03-01
39
49
10.22059/ees.2018.30611
DTMB4119 Propeller
Turbulent flow
CFD Simulation
Hydrodynamic Performance
Cavitation
Amirhossein
Niroumand
niroumand@raghebisf.ac.ir
1
Department of Mechanical Engineering, Ragheb Isfahani Higher Education Institute, Isfahan, Iran
AUTHOR
Amin
Ashtari Larki
2
Department of Mechanical Engineering, Ragheb Isfahani Higher Education Institute, Isfahan, Iran
AUTHOR
Mahmoud
Abbaszadeh
abbaszadeh.mahmoud@gmail.com
3
School of Engineering, University of Warwick, Coventry, United Kingdom
LEAD_AUTHOR
[1] Kulczyk J., Skraburski Ł., M. Zawislak, Analysis of Screw Propeller 4119 Using the Fluent System, Archives of Civil and Mechanical Engineering (2007) 7: 129-137.
1
[2] Mirjalili S., Lewis A., Mirjalili S. A. M., Multi-Objective Optimisation of Marine Propellers, Procedia Computer Science (2015) 51: 2247-2256
2
[3] Krasilnikov V., Sun J., Halse K. H., CFD Investigation in Scale Effect on Propellers with Different Magnitude of Skew in Turbulent Flow, In The First International Symposium on Marine Propulsors, Trondheim (2009) 25-40.
3
[4] Nakisa M., Abbasi M. J., Amini A. M., Assessment of Marine Propeller Hydrodynamic Performance in Open Water via CFD, in Proceedings of The 7th International Conference on Marine Technology (MARTEC 2010) (2010).
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[9] Ekinci S., Celik F., Guner M., A Practical Noise Prediction Method for Cavitating Marine Propellers, Brodogradnja (2010) 61:359-366.
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[10] Huang S., Zhu X.-y., Guo C.-y., Chang X., CFD Simulation of Propeller and Rudder Performance when Using Additional Thrust Fins, Journal of Marine Science and Application (2007)6: 27-31.
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[11] Da-Qing L., Validation of RANS Predictions of Open Water Performance of a Highly Skewed Propeller with Experiments, Journal of Hydrodynamics (2006) 18: 520-528.
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18
ORIGINAL_ARTICLE
Stochastic reconstruction of carbon fiber paper gas diffusion layers of PEFCs: A comparative study
A 3D microstructure of the non-woven gas diffusion layers (GDLs) of polymer electrolyte fuel cells (PEFCs) is reconstructed using a stochastic method. For a commercial GDL, due to the planar orientation of the fibers in the GDL, 2D SEM image of the GDL surface is used to estimate the orientation of the carbon fibers in the domain. Two more microstructures with different fiber orientations are generated and compared. The method is verified by comparing the commercial GDL (Toray TGP-H-060) model properties with other simulations or real GDL data. Three different reconstructed models are compared in terms of permeability, and the 3D pore size distribution of the models is also investigated. Through-plane (TP) and in-plane (IP) tortuosity, and the effects of binder addition on tortuosity are also discussed. For the TGH-H-060, tortuosity is derived to be 0.93, 1.50, and 1.42 in IP-x, IP-y, and TP-z directions, respectively. It is shown that adding binders to the fibrous skeleton increases the tortuosity of the pore phase.
https://www.energyequipsys.com/article_30612_224bee8eafa6374ef9454fd64a21c790.pdf
2018-03-01
51
59
10.22059/ees.2018.30612
GDL Reconstruction
Fiber Orientation
Pore Size Distribution
Permeability
Tortuosity
Sepehr Sima
Afrookhteh
1
Renewable Energy Research Center, Mechanical Engineering Department, Babol Noshirvani University of Technology, Babol, Iran
AUTHOR
Jalil
Jamali
ja_ja032@yahoo.com
2
Department of Mechanical Engineering, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran
LEAD_AUTHOR
Mohsen
Shakeri
shakeri@nit.ac.ir
3
Fuel cell research and Technology center, Mechanical Dept. Babol Noshirvani University of Technology, babol , Mazandaran
AUTHOR
Majid
Baniassadi
m.baniassadi@ut.ac.ir
4
School of Mechanical Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Wang Y., A Review of Polymer Electrolyte Membrane Fuel Cells, Technology, Applications, and Needs on Fundamental Research, Applied Energy (2011) 88(4):981-1007.
1
[2] Barbir F., PEM Fuel Cells, Theory and Practice (2013) Academic Press.
2
[3] Wilkinson D.P., Proton Exchange Membrane Fuel Cells, Materials Properties and Performance (2009) CRC Press.
3
[4] Fadzillah D.M., Review on Microstructure Modelling of a Gas Diffusion Layer for Proton Exchange Membrane Fuel Cells, Renewable and Sustainable Energy Reviews (2016).
4
[5] Shojaeefard M.H., A Review on Microstructure Reconstruction of PEM Fuel Cells Porous Electrodes for Pore Scale Simulation, International Journal of Hydrogen Energy (2016) 41(44): 20276-20293.
5
[6] James J.P., Choi H.W., Pharoah J.G., X-Ray Computed Tomography Reconstruction and Analysis of Polymer Electrolyte Membrane Fuel Cell Porous Transport Layers, International Journal of Hydrogen Energy (2012)37(23): 18216-18230.
6
[7] Becker J., Determination of Material Properties of Gas Diffusion Layers, Experiments and Simulations Using Phase Contrast Tomographic Microscopy, Journal of The Electrochemical Society (2009)156(10): B1175-B1181.
7
[8] Fishman Z., Hinebaugh J., Bazylak A., Microscale Tomography Investigations of Heterogeneous Porosity Distributions of PEMFC GDLs, Journal of the Electrochemical Society (2010) 157(11): B1643-B1650.
8
[9] Ostadi H., 3D Reconstruction of a Gas Diffusion Layer and a Microporous Layer, Journal of Membrane Science (2010) 351(1–2): 69-74.
9
[10] Baniassadi M., Three-Dimensional Reconstruction and Homogenization of Heterogeneous Materials Using Statistical Correlation Functions and FEM, Computational Materials Science (2012) 51(1): 372-379.
10
[11] Sheidaei A., 3-D Microstructure Reconstruction of Polymer Nano-Composite Using FIB–SEM and Statistical Correlation Function, Composites Science and Technology (2013) 80: 47-54.
11
[12] Hinebaugh J., Bazylak A., Stochastic Modeling of Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers – Part 1: Physical Characterization, International Journal of Hydrogen Energy (2017).
12
[13] Yiotis A.G., Microscale Characterisation of Stochastically Reconstructed Carbon Fiber-Based Gas Diffusion Layers, Effects of Anisotropy and Resin Content, Journal of Power Sources (2016)320: 153-167.
13
[14] Wu W., Jiang F., Microstructure Reconstruction and Characterization of PEMFC Electrodes, International Journal of Hydrogen Energy (2014) 39(28): 15894-15906.
14
[15] Tayarani-Yoosefabadi Z., Stochastic Microstructural Modeling of Fuel Cell Gas Diffusion Layers and Numerical Determination of Transport Properties in Different Liquid Water Saturation Levels, Journal of Power Sources (2016) 303: 208-221.
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[16] Mukherjee P.P., Kang Q., Wang C.-Y., Pore-Scale Modeling of Two-Phase Transport in Polymer Electrolyte Fuel Cells-Progress and Perspective, Energy & Environmental Science (2011) 4(2): 346-369.
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[17] Schladitz K., Design of Acoustic Trim Based on Geometric Modeling and Flow Simulation for Non-Woven, Computational Materials Science (2006) 38(1): 56-66.
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[18] Thiedmann R., Stochastic 3D Modeling of the GDL Structure in PEMFCs Based on Thin Section Detection, Journal of the Electrochemical Society (2008)155(4): B391-B399.
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[19] Ohser J., Schladitz K., 3D Images of Materials Structures: Processing and Analysis (2009)Wiley.
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[20] Hinebaugh J., Gostick J., Bazylak A., Stochastic Modeling of Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers – Part 2, A Comprehensive Substrate Model with Pore Size Distribution and Heterogeneity Effects, International Journal of Hydrogen Energy (2017).
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[22] Mathias M.F., Diffusion Media Materials and Characterisation, In Handbook of Fuel Cells (2010) John Wiley & Sons, Ltd.
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[23] El Hannach M., Kjeang E., Stochastic Microstructural Modeling of PEFC Gas Diffusion Media. Journal of The Electrochemical Society (2014) 161(9): F951-F960.
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[24] Pant L.M., Mitra S.K., Secanell M., Absolute Permeability and Knudsen Diffusivity Measurements in PEMFC gas Diffusion Layers and Micro Porous Layers, Journal of Power Sources (2012)206: 153-160.
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[25] Cooper S.J., Microstructural Analysis of an LSCF Cathode Using in Situ Tomography and Simulation. ECS Transactions (2013)57(1): 2671-2678.
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[26] Didari S., Modeling of Composite Fibrous Porous Diffusion Media. International Journal of Hydrogen Energy (2014)39(17): 9375-9386.
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[27] Hao L., Cheng P., Lattice Boltzmann Simulations of Anisotropic Permeabilities in Carbon Paper Gas Diffusion Layers, Journal of Power Sources (2009)186(1): 104-114.
27
[28] Whitaker S., The Method of Volume Averaging (2013) Springer Netherlands.
28
ORIGINAL_ARTICLE
The development and evaluation of a portable polyethylene biogas reactor
Several factors can influence the process of biogas production. The type of reactor is one of the key factors that influence biogas production. Therefore, the aim of this study was to construct a portable horizontal polyethylene-based biogas reactor. In addition, the performance of the developed biogas reactor was tested through digestion of cow manure. The experiments were carried out in Mashhad, Iran, during June–July 2016. Biogas production was studied over a span of 58 days’ hydraulic retention time. Artificial neural network (ANN) models were used to predict the production of biogas based on temperature and pH. The Levenberg–Marquardt learning algorithm was employed to develop the best model. The obtained biogas productivity was 0.27 m3 kgVS-1, indicating that the developed biogas reactor was optimum to convert the substrate into biogas. The ANN results highlighted that the best developed model consisted of an input layer with two input variables, one hidden layer with 15 neurons, and one output layer with the correlation coefficient of 0.90. Overall, it was concluded that the ANN models can be employed to prognosticate biogas production using a portable polyethylene biogas reactor.
https://www.energyequipsys.com/article_30613_aeada3200d7430285f70737a33a997a2.pdf
2018-03-01
61
68
10.22059/ees.2018.30613
Artificial neural network
Horizontal Reactor
Modeling
Mehdi
Khojastehpour
mkhpour@um.ac.ir
1
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Amin
Nikkhah
amin.nikkhah@mail.um.ac.ir
2
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Alireza
Taheri-Rad
taherirad.alireza@mail.um.ac.ir
3
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
[1] Ahmadi-Pirlou M., Ebrahimi-Nik M., Khojastehpour M., Ebrahimi S.H., Mesophilic co-Digestion of Municipal Solid Waste and Sewage Sludge: Effect of Mixing Ratio, Total Solids, and Alkaline Pretreatment, International Biodeterioration & Biodegradation, (2017) 125: 97-104.
1
[2] Anozie A.N, Layokun S.K, Okeke C.U., An Evaluation of a Batch Pilot-Scale Digester for Gas Production from Agricultural Wastes, Energy Sources (2005) 27(14):1301-1311.
2
[3] Bacenetti J, Sala C, Fusi A, Fiala M., Agricultural Anaerobic Digestion Plants, What LCA Studies Pointed out and What Can be Done to Make Them more Environmentally Sustainable, Applied Energy (2016) 179:669-686.
3
[4] Bond T., Templeton M.R., History and Future of Domestic Biogas Plants in the Developing World, Energy for Sustainable Development (2011) 5(4):347-354.
4
[5] Cheng S., Li Z., Mang, H.P., Huba E.M., Gao R., Wang X., Development and Application of Prefabricated Biogas Digesters in Developing Countries, Renewable and Sustainable Energy Reviews (2014) 34:387-400.
5
[6] Chmielewski A.G., Berbec A., Zalewski M., Dobrowolski A., Hydraulic Mixing Modeling in Reactor for Biogas Production, Chemical and Process Engineering (2012) 33(4):621-628.
6
[7] Comino E., Rosso M., Riggio V., Development of a Pilot Scale Anaerobic Digester for Biogas Production from Cow Manure and Whey Mix, Bioresource Technology (2009) 100(21):5072-5078.
7
[8] D’Agostino R.B., Tests for the Normal Distribution, Goodness-of-fit Techniques, (1986) 68: p.576,
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[10] Fayyazi S., Abbaspour-Fard M.H., Rohani A., Monadjemi S.A., Sadrnia H., Identification and Classification of Three Iranian Rice Varieties in Mixed Bulks Using Image Processing and MLP Neural Network. International Journal of Food Engineering (2017) 13(5). DOI: 10.1515/ijfe-2016-0121
10
[11] Ghalhari G.F., Mayvaneh F., Effect of Air Temperature and Universal Thermal Climate Index on Respiratory Diseases Mortality in Mashhad, Iran. Archives of Iranian Medicine (2016) 19(9):618 – 624.
11
[12] Hajihassani M., Armaghani D.J., Marto A., Mohamad E.T., Ground Vibration Prediction in Quarry Blasting through an Artificial Neural Network Optimized by Imperialist Competitive Algorithm, Bulletin of Engineering Geology and the Environment (2015) 74(3): 873-886.
12
[13] Holubar P., Zani L., Hager M., Froschl W., Radak Z., Braun R., Start up and Recovery of a Biogas-Reactor Using Hierarchial Neural Network-Based Control Tool, Journal of Chemical Technology and Biotechnology (2003) 78:847–54.
13
[14] Islam M.N., Report on Biogas Programme of China, Dacca: Bangladesh University of Engineering and Technology (1979).
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[15] Jayakody K.P.K., Menikpura S.N.M., Basnayake B.F.A., Weerasekara R., Development and Evaluation of Hydrolytic/Acidogenic First Stage Anaerobic Reactor for Treating Municipal Solid Waste in Developing Countries, In Proceedings of international conference on sustainable solid waste management, Chennai, India (2007): 363-369.
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[16] Kana E.G., Oloke J.K., Lateef A., Adesiyan M.O., Modeling and Optimization of Biogas Production on Saw Dust and other co-Substrates Using Artificial Neural Network and Genetic Algorithm, Renewable Energy (2012) 46:276-281.
16
[17] Kanat G, Saral A., Estimation of Biogas Production Rate in a Thermophilic UASB Reactor Using Artificial Neural Networks, Environmental Modeling & Assessment (2009) 14(5):607-614.
17
[18] Kaparaju P., Serrano M., Angelidaki I., Effect of Reactor Configuration on Biogas Production from Wheat Straw Hydrolysate, Bioresource Technology (2009) 100(24): 6317-6323.
18
[19] Kim E., Lee D.H., Won S., Ahn H., Evaluation of Optimum Moisture Content for Composting of Beef Manure and Bedding Material Mixtures Using Oxygen Uptake Measurement. Asian-Australasian journal of animal sciences (2016) 29(5): 753-758.
19
[20] Kral I., Piringer G., Saylor M.K., Gronauer A., Bauer A., Environmental Effects of Steam Explosion Pretreatment on Biogas from Maize—Case Study of a 500-kw Austrian Biogas Facility, BioEnergy Research (2015) 9(1): 198–207.
20
[21] Mahanty B., Zafar M., Park H.S., Characterization of co-Digestion of Industrial Sludges for Biogas Production by Artificial Neural Network and Statistical Regression Models, Environmental Technology (2013) 34(13-14):2145-2153.
21
[22] Moog F.A., Avilla H.F., Agpaoa E.V., Valenzuela F.G., Concepcion F.C., Promotion and Utilization of Polyethylene Biodigester in Smallhold Farming Systems in the Philippines, Livestock Research for Rural Development (1997) 9(2).
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[23] Mosaedi A., Sough M.G., Sadeghi S.H., Mooshakhian Y., Bannayan M., Sensitivity Analysis of Monthly Reference Crop Evapotranspiration Trends in Iran: a Qualitative Approach, Theoretical and Applied Climatology (2016) 1-17.
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[24] Mushtaq K., Zaidi A.A., Askari S.J., Design and Performance Analysis of Floating Dome Type portable Biogas Plant for Domestic use in Pakistan, Sustainable Energy Technologies and Assessments (2016)14:21-25.
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[25] Negri M., Bacenetti J., Manfredini A., Lovarelli D., Maggiore T.M., Fiala M., Bocchi S., Evaluation of Methane Production from Maize Silage by Harvest of Different Plant Portions, Biomass and Bioenergy (2014) 67:339-346.
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[26] Ozkaya B., Demir A., Bilgili M.S., Neural Network Prediction Model for the Methane Fraction in Biogas from Field-Scale Landfill Bioreactors, Environmental Modelling & Software (2007) 22(6):815-822.
26
[27] Qdais H.A., Hani K.B., Shatnawi, N., Modeling and Optimization of Biogas Production from a Waste Digester Using Artificial Neural Network and Genetic Algorithm, Resources, Conservation and Recycling (2010) 54(6):359-363.
27
[28] Rajendran K., Aslanzadeh S., Johansson F., Taherzadeh M.J., Experimental and Economical Evaluation of a Novel Biogas Digester, Energy Conversion and Management (2013) 74:183-191.
28
[29] Rajendran K., Aslanzadeh S., Taherzadeh M.J., Household Biogas Digesters—A Review, Energies (2012) 5(8):2911-2942.
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[30] Rohani A., Abbaspour-Fard M.H., Abdolahpour S., Prediction of Tractor Repair and Maintenance Costs Using Artificial Neural Network, Expert Systems with Applications (2011) 38(7):8999-9007.
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[31] Saeidirad M.H., Rohani A., Zarifneshat S., Predictions of Viscoelastic behavior of Pomegranate using Artificial Neural Network and Maxwell Model, Computers and Electronics in Agriculture (2013) 31:98:1-7.
31
[32] Sanaei-Moghadam A., Abbaspour-Fard M.H., Aghel H., Aghkhani M.H., Abedini-Torghabeh J., Enhancement of Biogas Production by co-Digestion of Potato Pulp with Cow Manure in a CSTR System. Applied Biochemistry and Biotechnology (2014) 173(7):1858-1869.
32
[33] Soltanali H., Nikkhah A., Rohani A. Energy Audit of Iranian Kiwifruit Production Using Intelligent Systems, Energy (2017) 139: 646-654.
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[37] Taheri-Rad A., Khojastehpour M., Rohani A., Khoramdel S., Nikkhah, A., Energy Flow Modeling and Predicting the Yield of Iranian Paddy Cultivars Using Artificial Neural Networks, Energy (2017) 135: 405–412.
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[39] Taylor C., Hassan M., Ali S., Integrated Portable Biogas Systems for Managing Organic Waste, In Presentation at the 4th WSEAS International Conference on Energy Planning, Energy Saving, Environmental Education (EPESE’10) and 4th WSEAS International Conference on Renewable Energy Sources (RES ‘10) (2010).
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41
[42] Zeynali R., Khojastehpour M., Ebrahimi-Nik M., Effect of Ultrasonic Pre-Treatment on Biogas Yield and Specific Energy in Anaerobic Digestion of Fruit and Vegetable Wholesale Market Wastes, Sustainable Environment Research (2017) 27(6):259-264.
42
ORIGINAL_ARTICLE
Xergy analysis and multiobjective optimization of a biomass gasification-based multigeneration system
Biomass gasification is the process of converting biomass into a combustible gas suitable for use in boilers, engines, and turbines to produce combined cooling, heat, and power. This paper presents a detailed model of a biomass gasification system and designs a multigeneration energy system that uses the biomass gasification process for generating combined cooling, heat, and electricity. Energy and exergy analyses are first applied to evaluate the performance of the designed system. Next, the minimizing total cost rate and the maximizing exergy efficiency of the system are considered as two objective functions and a multiobjective optimization approach based on the differential evolution algorithm and the local unimodal sampling technique is developed to calculate the optimal values of the multigeneration system parameters. A parametric study is then carried out and the Pareto front curve is used to determine the trend of objective functions and assess the performance of the system. Furthermore, sensitivity analysis is employed to evaluate the effects of the design parameters on the objective functions. Simulation results are compared with two other multiobjective optimization algorithms and the effectiveness of the proposed method is verified by using various key performance indicators.
https://www.energyequipsys.com/article_30614_45240db3c449b9111bc7079403ae6fc6.pdf
2018-03-01
69
87
10.22059/ees.2018.30614
Multiobjective Optimization
Exergy Analysis
Pareto Front
Biomass Gasification
Differential Evolution Algorithm
Local Unimodal Sampling
Halimeh
Rashidi
halimeh_rashidi@yahoo.com
1
Faculty of Engineering, University of Hormozgan, Bandar Abbas, Iran
LEAD_AUTHOR
Jamshid
Khorshidi
khorshidijamshid@yahoo.com
2
Faculty of Engineering, University of Hormozgan, Bandar Abbas, Iran
AUTHOR
[1] Zhang L., Xu C.C., Champagne P., Overview of Recent Advances in Thermo-chemical Conversion of Biomass, Energy Conversion and Management. (2010)51(5):969-82.
1
[2] Dong Y., Steinberg M., Hynol an Aconomical Process for Methanol Production from Biomass and Natural Gas with Reduced CO2 Emission, International Journal of Hydrogen Energy (1997) 22(10-11):971-7.
2
[3] Ahmadi P., Dincer I., Rosen M.A., Thermoeconomic Multi-Objective Optimization of a Novel Biomass-Based Integrated Energy System, Energy (2014) 68:958-70.
3
[4] Ahmadi P., Dincer I., Rosen M.A., Development and Assessment of an Integrated Biomass-Based Multigeneration Energy System, Energy (2013)56:155-66.
4
[5] Wang J., Yang Y., Energy, Exergy and Environmental Analysis of a Hybrid Combined Cooling Heating and Power System Utilizing Biomass and Solar Energy, Energy Conversion and Management (2016)124:566-77.
5
[6] Salemme L., Simeone M., Chirone R., Salatino P., Analysis of the Energy Efficiency of Solar Aided Biomass Gasification for Pure Hydrogen Production, International Journal of Hydrogen energy (2014) 39(27):14622-32.
6
[7] Nakyai T., Authayanun S., Patcharavorachot Y., Arpornwichanop A., Assabumrungrat S., Saebea D., Exergoeconomics of Hydrogen Production from Biomass Air-Steam Gasification with Methane co-Feeding, Energy Conversion and Management. (2017)140:228-39.
7
[8] Wang J.J., Xu Z.L., Jin H.G., Shi G.H., Fu C., Yang K., Design Optimization and Analysis of a Biomass Gasification Based BCHP System: A case Study in Harbin, China, Renewable Energy (2014)71:572-83.
8
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9
[2] Gholamian E., Zare V., Mousavi S.M., Integration of Biomass Gasification with a Solid Oxide Fuel Cell in a Combined Cooling, Heating and Power System: A Thermodynamic and Environmental Analysis, International Journal of Hydrogen Energy (2016)41(44):20396-406.
10
[3] Fernandes A., Woudstra T., Aravind P.V., System Simulation and Exergy Analysis on the Use of Biomass-Derived Liquid-Hydrogen for SOFC/GT Powered Aircraft, International Journal of Hydrogen Energy (2015) 40(13):4683-97.
11
[4] Seyitoglu S.S., Dincer I., Kilicarslan A., Energy and Exergy Analyses of Hydrogen Production by Coal Gasification, International Journal of Hydrogen Energy (2017)42(4):2592-600.
12
[5] Kalinci Y., Dincer I., Hepbasli A., Energy and Exergy Analyses of a Hybrid Hydrogen Energy System: A Case Study for Bozcaada, International Journal of Hydrogen Energy (2017)42(4):2492-503.
13
[6] Borji M., Atashkari K., Ghorbani S., Nariman-Zadeh N., Parametric Analysis and Pareto Optimization of an Integrated Autothermal Biomass Gasification, Solid Oxide Fuel Cell and Micro Gas Turbine CHP System, International Journal of Hydrogen Energy (2015)40(41):14202-23.
14
[7] Kalinci Y., Hepbasli A., Dincer I., Exergoeconomic Analysis of Hydrogen Production from Biomass Gasification, International Journal of Hydrogen Energy (2012)37(21):16402-11.
15
[8] El-Emam R.S., Dincer I., Thermal Modeling and Efficiency Assessment of an Integrated Biomass Gasification and Solid Oxide Fuel Cell System, International Journal of Hydrogen Energy (2015)40(24):7694-706.
16
[9] Ramadan M., Khaled M., Ramadan H.S., Becherif M., Modeling and Sizing of Combined Fuel Cell-Thermal Solar System for Energy Generation, International Journal of Hydrogen Energy (2016) 41(44):19929-35.
17
[10] Khalid F., Dincer I., Rosen M.A., Techno-Economic Assessment of a Solar-Geothermal Multigeneration System for Buildings, International Journal of Hydrogen Energy (2017) 42(33): 21454-21462.
18
[11] Yuksel Y.E., Ozturk M., Thermodynamic and Thermoeconomic Analyses of a Geothermal Energy Based Integrated System for Hydrogen Production, International Journal of Hydrogen Energy (2017)42(4):2530-46.
19
[12] Eveloy V., Karunkeyoon W., Rodgers P., Al Alili A., Energy, Exergy and Economic Analysis of an Integrated Solid Oxide Fuel Cell–Gas Turbine–Organic Rankine Power Generation System, International Journal of Hydrogen Energy (2016)41(31):13843-58.
20
[13] Alhayek B, Agelin‐Chaab M, Reddy B., Analysis of an Innovative Direct Steam Generation‐Based Parabolic Trough Collector Plant Hybridized with a Biomass Boiler, International Journal of Energy Research (2017) DOI: 10.1002/er.3785.
21
[14] Ozcan H., Dincer I., Performance Evaluation of an SOFC Based Trigeneration System Using Various Gaseous Fuels from Biomass Gasification, International Journal of Hydrogen Energy (2015) 40(24):7798-807.
22
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51
ORIGINAL_ARTICLE
A method for bubble volume measurement under constant flow conditions in gas–liquid two-phase flow
Measuring the volume of a bubble, especially at its detachment, is a basic subject in gas-liquid two-phase flow research. A new indirect method for this measurement under constant flow conditions is presented. An electronic device is designed and constructed based on laser beam intensity. This device calculates the frequency of the bubble formation by measuring the total time of the formation process and counting the number of bubbles crossing the laser beam. The bubble volume at detachment can be calculated by dividing the volumetric flow rate of air by the frequency of bubble formation. The latter and the bubble volume at detachment are measured for three different heights of water above the the tip of the orifice (50, 100, and 150 mm), three orifice diameters (1, 2, and 3 mm), and different gas flow rates between 2000 and 10000 ml/hr. Comparing and validating the results with the results of the image processing (IP) method and the correlations presented by other studies shows the strong accuracy of the present method.
https://www.energyequipsys.com/article_30615_1f645217729ad16e5181b27b5dad0ba5.pdf
2018-03-01
89
99
10.22059/ees.2018.30615
Frequency
Bubble Volume
Constant Flow
image processing
Seyed Erfan
Hosseini-Doost
1
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 11155-4563
AUTHOR
Amirmohammd
Sattari
2
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 11155-4563
AUTHOR
Pedram
Hanafizadeh
hanafizadeh@ut.ac.ir
3
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 11155-4563
LEAD_AUTHOR
Morteza
Molaei
mrtzmolaei@ut.ac.ir
4
Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran, P. O. Box: 11155-4563
AUTHOR
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30
ORIGINAL_ARTICLE
Optimizing the AGC system of a three-unequal-area hydrothermal system based on evolutionary algorithms
This paper focuses on expanding and evaluating an automatic generation control (AGC) system of a hydrothermal system by modelling the appropriate generation rate constraints to operate practically in an economic manner. The hydro area is considered with an electric governor and the thermal area is modelled with a reheat turbine. Furthermore, the integral controllers and electric governor parameters are optimized using integral squared error (ISE) criterion. Also, a novel Teaching-Learning-Based Optimization (TLBO) algorithm, Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) with controller are proposed for optimizing AGC. Investigations have been conducted for the selection of a suitable value for governor speed regulation parameter R for the hydro and thermal areas, to explore the effect of tie-line power on the dynamic response. The advantages of the proposed approach are demonstrated by comparing the results of optimizing the AGC system of a three-unequal-area hydrothermal system with mentioned algorithms for the first one in comparison with other recently published techniques. The results confirm the flexibility and the suitability of the proposed AGC model for optimizing the different approaches. Moreover, it is more practical to use the proposed method to make a wide variety of changes in the system parameters using sensitivity analysis.
https://www.energyequipsys.com/article_30616_7d77c929e6b362c3d8d396816aa2ff97.pdf
2018-03-01
101
116
10.22059/ees.2018.30616
Automatic Generation Control (AGC)
Multi-Area Hydrothermal System
Teaching-Learning-Based Optimization (TLBO)
Particle Swarm Optimization (PSO)
Gravitational Search Algorithm (GSA)
Ramin
Sakipour
raminsakipoor@gamil.com
1
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
AUTHOR
Hamdi
Abdi
hamdiabdi@gmail.com
2
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
LEAD_AUTHOR
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