THE BEST MANUFACTURING PROCEDURE FOR THE COMMERCIAL PRODUCTION OF UREA, USING AHP BASED TOPSIS

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Published Dec 10, 2019
Yousaf Ali
Muhammad Haroon Muhammad Abdullah Amin Ullah Khan

Abstract

Nitrogen is one of the most significant nutrients needed for the proper growth and development of crops and other plants. In synthetic nitrogen fertilizers, solid urea is the largest source of nitrogen (N) as a nutrient. Prilling, granulation, and hybrid systems are the commercial processes used for the production of urea. One of the biggest challenges involved in the determination and implementation of those alternatives is rationalized decision making.  The objective of this research study is to evaluate these processes by considering some of the significant attributes like profit, environmental friendliness, process flexibility and reliability to determine which process is the most optimal. The results show that the prilling process is the best technology for urea production. It is the most optimal process in terms of profitability and reliability, and is therefore widely used in the fertilizer industry. Prilling is not the best option when it comes to the environment when compared to granulation. The granulation process is not the best fit for the commercial production of urea because it is not a reliable process, especially for high agricultural demands and market competition. The results show that it would be very difficult to keep up with the rapid growth of the population using the granulation process. If the environmental and urea quality concerns are considered, the hybrid system is the highest priority and may be preferred.    

How to Cite

Ali, Y., Haroon, M., Abdullah, M., & Khan, A. U. (2019). THE BEST MANUFACTURING PROCEDURE FOR THE COMMERCIAL PRODUCTION OF UREA, USING AHP BASED TOPSIS. International Journal of the Analytic Hierarchy Process, 11(3), 313–330. https://doi.org/10.13033/ijahp.v11i3.636

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Keywords

Fertilizers;, Reliability;, Urea Production; AHP, TOPSIS, MCDM, Pakitan

References
Alamdari, A. Jahanmiri, N. Rahmaniyan (2000). Mathematical modeling of the urea prilling process. Chemical Engineering Communication, 178, 185-198. Doi: https://doi.org/10.1080/00986440008912182

Aber, J.D. et al. (1997). Human alteration of the global nitrogen cycle: sources and consequences, Ecological Appliations, 7(3), 7-50.

Ali, Y., Iftikhar, N. and Edwin, A.C. (2017a). Assessment of career selection problems in developing countries: a MCDM approach. International Journal of the Analytic Hierarchy Process, 9(2), 219-243. Doi: https://doi.org/10.13033/ijahp.v9i2.488

Ali, Y., Asghar, A., Muhammad, N. and Salman, A. (2017b). Selection of a fighter aircraft to improve the effectiveness of air combat in the war on terror: Pakistan Air Force- a case in point. International Journal of the Analytic Hierarchy Process, 9(2), 244-273. Doi: https://doi.org/10.13033/ijahp.v9i2.489

Ali, Y., Aslam, Z., Dar, H.S. and Mumtaz, U., 2018. A multi-criteria decision analysis of solid waste treatment options in Pakistan: Lahore City—a case in point. Environment Systems and Decisions, 38(4), 528-543. Doi: https://doi.org/10.1007/s10669-018-9672-y

Baba, S. (2012). Characterisation of strength and structure of granular and prilled urea fertilizer. Petronas: Universiti Technology Petronas.

Chen, S. (1992). Fuzzy multiple attributes decision making: methods and applications. Heidelberg: Springer-Verlag.

Cheng, J. et al. (2018). Structural optimization of a high-speed press considering multi-source uncertainties based on a new heterogeneous TOPSIS. Applied Sciences, 8(1), 126. Doi: https://doi.org/10.3390/app8010126

Constant, K.M. (1992). World Nitrogen Survey. World Bank Technical Paper 174.

Day, J. H., Lees, R. E., Clark, R. H. & Pattee, P. L. (1984). Respiratory response to formaldehyde and off-gas of urea-formaldehyde foam insulation. Canadian Medical Association Journal, 131(9), 1061.

Dweiri, F., Kumar, S., Khan, S. A. & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in the automotive industry. Expert Systems with Applications,62, 273-283. Doi: https://doi.org/10.1016/j.eswa.2016.06.030

Emady, H., Hapgood, K. & Smith, R. (2016). Granulation and tabletting. Cham: Springer.

Galloway, J. N. et al. (2003). The nitrogen cascade. Bioscience, 53(4), 341-356.
Gupta, H. & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242-258. Doi: https://doi.org/10.1016/j.jclepro.2017.03.125

Hodgett, R. (2013). Multiple criteria decision making in whole process design. Newcastle University Publisher.

Hwang, C.-L. & Yoon, K. (1981). Methods for multiple attribute decision making. Berlin: Springer.

Jahanmiri, A. et al. (2013). Mathematical modeling of the urea prilling process. Chemical Engineering Communication, 178(1), 1-19. Doi: https://doi.org/10.1080/00986440008912182

Jain, V. et al., 2018. Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555-564. Doi: https://doi.org/10.1007/s00521-016-2533-z

Karim, R. & Karmaker, C. (2016). Machine selection by AHP and TOPSIS methods. American Journal of Industrial Engineering, 4(1), 7-13.

Mavi, R. K., Goh, M. & Mavi, N. K. (2016). Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. Procedia-Social and Behavioral Sciences, 235(24), 216-225. Doi: https://doi.org/10.1016/j.sbspro.2016.11.017 Doi: https://doi.org/10.1016/j.sbspro.2016.11.017

Markovic, Z. (2010). Modification of TOPSIS method for solving of multi-criteria tasks. Journal of Operation Research, 20, 117-143.

Quin, B. F., Harold, S., Spilsbury, S. & Bates, G.,(2017). Commercialisation of ONEsystem (Wetted, NBPT-treated prilled Urea) in NewZealand and Victoria, Australia, s.l.: Fertilizer and Lime Research Centre.

Rahmanian, N. et al. (2011). Effect of process parameters on the granule properties made in a high shear granulator. Chemical Engineering Research and Design, 89(5), 512-518. Doi: https://doi.org/10.1016/j.cherd.2010.10.021

Rehmanian, N. et al. (2015). Urea finishing process: prilling vs granulation. Procedia Engineering, 102, 174-181. Doi: https://doi.org/10.1016/j.proeng.2015.01.122

Rose, M. T. et al. (2016). A slow-release nitrogen fertilizer produced by simultaneous granulation and superheated steam drying of urea with brown coal. Chemical and Biological Technologies in Agriculture, 3(1), 10. Doi: https://doi.org/10.1186/s40538-016-0062-8

Ishfaq, S., Ali, S. and Ali, Y., 2018. Selection of optimum renewable energy source for energy sector in Pakistan by using MCDM approach. Process Integration and Optimization for Sustainability, 2(1), 61-71. Doi: https://doi.org/10.1007/s41660-017-0032-z

Saaty, T. L., 1989. Group decision making and AHP. Berlin: Springer, Berlin.

Wang, T.-K., Zhang, Q., Chong, H.-Y. & Wang, X. (2017). Integrated supplier selection framework in a resilient construction supply chain: An approach via the analytic hierarchy process (AHP) and grey relational analysis (GRA). Sustainability, 9(2), 289. Doi: https://doi.org/10.3390/su9020289

Wind, Y. & Saaty, T. L. (1980). Marketing applications of the analytic hierarchy process. Management Science, 26(7), 641-658. Doi: https://doi.org/10.1287/mnsc.26.7.641
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