ارزیابی و بهینه سازی سیستم جمع آوری و حمل پسماند شهر ارومیه با ترکیب روش سطح پاسخ و شبکه عصبی مصنوعی
محورهای موضوعی : مدیریت پسماندسعید جعفرزاده قوشچی 1 , شبنم حمیدی مقدم 2
1 - استادیار، گروه مهندسی صنایع، دانشکده صنایع، دانشگاه صنعتی ارومیه، ایران. * (مسوول مکاتبات)
2 - کارشناسی، گروه مهندسی صنایع، دانشکده صنایع، دانشگاه صنعتی ارومیه، ایران.
کلید واژه: شبکه عصبی مصنوعی, زباله شهری, روش سطح پاسخ, بهینه سازی,
چکیده مقاله :
زمینه و هدف : بهینه سازی سیستم جمع آوری و حمل مواد زاید شهری بیش ترین سهم هزینه های مدیریت مواد زاید را از آن خود کرده است. بنابراین بهبود این سیستم و کاهش هزینه های عملیاتی آن به عنوان یک ضرورت در مدیریت پسماند شهری همواره مورد توجه قرار گرفته است. روش بررسی: به موجب بالا بودن نوسان، تغییر در اندازه پسماند ها، تغییرات آب و هوایی و بافت های جمعیتی و زیر ساختی استفاده از سیستم شبکه عصبی مصنوعی (ANN)یک روش مناسب برای پیش بینی اندازه پسماند تولیدی می باشد و از طرفی برای بهینه سازی سیستم مدیریتی این پسماندها نیز از روش سطح پاسخ (RSM) استفاده می گردد. یافته ها: نتایج حاصل از این روش ترکیبی نشان می دهد که بهترین ترکیب از عوامل تاثیرگذار در سیستم حمل زباله شهری توسط RSM با در نظر گرفتن بیش ترین بار حمل شده با حدود 26 کارگر، 10 وانت و 6 کامیون پیشنهاد شد. این ترکیب قادر به حمل بار حدود 34836 تن با هزینه 596696000 ریال می باشد، که نسبت به مقادیر واقعی کارایی بالایی را نشان می دهد. همچنین برای پیش بینی بار حمل شده الگوریتمپس انتشار (BP)با 9 نرون در لایه پنهان به عنوان بهترین مدل با قدرت پیش بینی 19/99% در پیش بینی وزن و 62/96% در پیش بینی هزینه انتخاب شد. بحث و نتیجه گیری: نتایج نشان داد که با استفاده از ترکیب دو روش سطح پاسخ به عنوان یک روش آماری و شبکه عصبی مصنوعی به عنوان یک روش ریاضی می توان به نتایج مناسبی برای ارزیابی و بهینه سازی سیستم جمع آوری و حمل پسماند رسید.
Background and Objective: Optimization of urban waste collection and transportation system has the largest part of waste management costs. Therefore, improving this system and reducing its operating costs as a necessity in urban waste management has always been considered. Method: Due to the high volatility, changes in the size of the waste, climate change and demographic and substructure tissue, the use of artificial neural network system (ANN) is a suitable method for predicting the production waste size, and on the other hand, for The optimization of the management system of these wastes is also used by the surface response method (RSM). Findings The results of this combined method show that the best combination of factors affecting urban waste transport system was proposed by RSM considering the largest loaded pack with about 26 workers, 10 pickups and 6 trucks. This combination is capable of carrying around 34836 tons of cargo at a cost of 596696000 Rials, which represents a high efficiency over actual values. Also, to predict load, the back propagation algorithm (BP) with 9 neurons in the hidden layer was selected as the best model with a predictive power of 99/19% in prediction of weight and 96/62% in cost prediction. Discussion and Conclusion: The results showed that using the combination of two methods of surface response as a statistical method and artificial neural network as a mathematical method, we can find suitable results for evaluation and optimization of waste collection and transportation system.
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- Abdollahi, T., (2016). Strategies Develop of Ardabil city Solid waste management With SWOT and QSPM,(2016), the 1st International Conference of Iranian nature hazards and environmental crise strategies and challenges,Ardebil,ShahrKord Water Resource Researche Center, https://www.civilica.com/Paper-ICINH01-ICINH01_033.html. (In Persian)
- Jalilzadeh, Ali., (2003). Evaluation of solid waste collection and transportation system in Urmia City and providing appropriate solutions, thesis for obtaining a master's degree in environmental health engineering, Department of Health Environmental , Faculty of Health, Isfahan University of Medical Sciences, page 85-1. ( In Persian)
- Ghanbarzadeh Lak, Mehdi; Aziz Qal; Reza Sepahi and Saeed Alizadeh, (2013), Urmia Municipal Solid Waste Management, Challenges and Opportunities, 7th National Congress of Civil Engineering, Zahedan, Sistan and Baluchestan University, https://www.civilica.com/Paper-NCCE07-NCCE07_1462.html. (In Persian)
- Medhat, Azadeh., Munavari, Massoud., Javid, Amir Hossein., Islami, Akbar., Ahadnejad, Mohsen.(2011). Evaluation and optimization of the collection and transportation system of solid waste management in Zanjan with GIS application. Journal of Human and Environment, Volume 9, Issue 16, Pages 33-40. (In Persian)
- Jafarzadeh-Ghoushchi, S., & Rahman, M. N. A. (2016). Performance study of artificial neural network modelling to predict carried weight in the transportation system. International Journal of Logistics Systems and Management, 24(2), 200-212.
- Shamshiry, E., Nadi, B., Mokhtar, M. B., Komoo, I., Hashim, H. S., & YAhya, N. (2011). Forecasting generation waste using artificial neural networks. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 1). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
- Shamshiry, E., Mokhtar, M. B., & Abdulai, A. M. (2014, March). Comparison of artificial neural network (ANN) and multiple regression analysis for predicting the amount of solid waste generation in a tourist and tropical area—Langkawi Island. In proceeding of International Conference on Biological, Civil, Environmental Engineering (BCEE) (pp. 161-166).
- Salem, S., & Jafarzadeh-Ghoushchi, S. (2016). Estimation of optimal physico-chemical characteristics of nano-sized inorganic blue pigment by combined artificial neural network and response surface methodology. Chemometrics and Intelligent Laboratory Systems, 159, 80-88.
- Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons.
- Moghadam, M. R., Shabani, A. M. H., & Dadfarnia, S. (2011). Spectrophotometric determination of iron species using a combination of artificial neural networks and dispersive liquid–liquid microextraction based on solidification of floating organic drop. Journal of hazardous materials, 197, 176-182.