پیوندهای مفید
آمار
| تعداد نشریات | 9 |
| تعداد شمارهها | 446 |
| تعداد مقالات | 5,719 |
| تعداد مشاهده مقاله | 8,018,419 |
| تعداد دریافت فایل اصل مقاله | 6,527,278 |
Hybrid Neural Network and Particle Swarm Optimization Model Applied to Assist Production Planning in a Large Petrochemical Plant | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 1، دوره 6، شماره 4، اسفند 2022، صفحه 491-510 اصل مقاله (1.72 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2022.21146.6029 | ||
| نویسندگان | ||
| Alireza Behroozsarand1؛ Seyyed Salar Meshkat1؛ David . A Wood* 2 | ||
| 1Department of Chemical Engineering, Urmia University of Technology, Urmia, Iran | ||
| 2DWA Energy Limited, Lincoln, United Kingdom | ||
| چکیده | ||
| Process and production planning play an important role in petrochemical production systems. Planning models are essential to optimize the combination of multiple non-linear production processes involved and therefore improve the commercial competitiveness of the such plant. Artificial neural networks offer an effective petrochemical plant planning tool, especially when configured in hybrid form as a back propagation artificial neural network coupled with an optimizer to assist with feature selection. A plant with eight feedstock inputs and thirteen petrochemical products is evaluated, firstly to show the capabilities of a basic backpropagation network model in predicting product outputs. The involvement of a particle swarm optimizer assists in filtering the dataset to remove outlying data records and identifying the input variables that are influential in determining specific product output volumes. The hybrid back propagation network-particle swarm optimization model assists by determining the logical relationship between input and output variables and expressing them in the form of an index matrix. The matrix leads to improved predictions of production outputs and faster convergence of the planning model. The modified back propagation network achieved maximum, minimum, and average relative errors of 59.1%, 0.0%, and 9.9%, respectively. Prediction errors in that range are considered acceptable for the collective production processes of a large-scale petrochemical complex evaluated with a nonlinear planning program. | ||
| کلیدواژهها | ||
| Petrochemical production planning؛ Back-propagating neural network؛ Data filtering؛ Feature selection؛ Hybrid neural network with optimizer؛ generalized regression | ||
| مراجع | ||
|
[1] R. Kadambur, P. Kotecha, Multi-level production planning in a petrochemical industry using elitist Teaching-Learning-Based-Optimization, Expert Syst. Appl., 42(1) (2015) 628-641.
[2] C.E. Escobar-Toledo, Industrial petrochemical production planning and expansion: A multi-objective linear programming approach, Top, 9(1) (2001) 77-89.
[3] A. Almanssoor, Planning of Petrochemical Industry under Environmental Risk and Safety Considerations, in, UWSpace, (2008) 6-18.
[4] S. Petchrompo, A.K. Parlikad, Heuristic optimisation for multi-asset intervention planning in a petrochemical plant, Procedia Manufacturing, 16 (2018) 208-214.
[5] G. Al-Sharrah, I. Alatiqi, A. Elkamel, Planning an Integrated Petrochemical Business Portfolio for Long-Range Financial Stability, 41(11) (2002) 2798-2804.
[6] R. Kadambur, P. Kotecha, Optimal production planning in a petrochemical industry using multiple levels, Computers & Industrial Engineering, 100 (2016) 133-143.
[7] Y. Özcanli, F. Kosovali Çavuş, M. Beken, Comparison of Mechanical Properties and Artificial Neural Networks Modeling of PP/PET Blends, 130(1) (2016) 444-446.
[8] N.K. Jain, V.K. Jain, Computer Aided Process Planning for Agile Manufacturing Environment, in: A. Gunasekaran (Ed.) Agile Manufacturing: The 21st Century Competitive Strategy, Elsevier Science Ltd, Oxford, (2001), 515-534.
[9] T. Gupta, B. K Ghosh, A survey of expert systems in manufacturing and process planning, Computers in Industry, 11 (1989) 195-204.
[10] T. Gupta, An expert system approach in process planning: Current development and its future, Computers & Industrial Engineering, 18(1) (1990) 69-80.
[11] M. Wang, H. Walker, Creation of an intelligent process planning system within the relational DBMS software environment, Computers in Industry, 13(3) (1989) 215-228.
[12] T.K. Gupta, K. Raza, Chapter 7 - Optimization of ANN Architecture: A Review on Nature-Inspired Techniques, in: N. Dey, S. Borra, A.S. Ashour, F. Shi (Eds.) Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, Academic Press, (2019), 159-182.
[13] S.B. Ashrafi, M. Anemangely, M. Sabah, M.J. Ameri, Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field, Journal of Petroleum Science and Engineering, 175 (2019) 604-623.
[14] A. Taik, A. Sedki, D. Ouazar, Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting, Mathematical Modelling of Natural Phenomena, 5(7) (2010) 132-138.
[15] J.F. Wang, W.L. Kang, J.L. Zhao, K.Y. Chu, A simulation approach to the process planning problem using a modified particle swarm optimization, 11(2) (2016) 77-92.
[16] M.A. Wilhelm, A.E. Smith, B. Bidanda, Process Planning Using An Integrated Expert System And Neural Network Approach, Corpus ID: 15612920 (1998).
[17] R. Kadambur, P. Kotecha, Constraint Programming Based Production Planning in a Petrochemical Industry, 4(1) (2014) 81-87.
[18] G.K. Al-sharrah, G. Hankinson, A. Elkamel, Decision-making for Petrochemical Planning Using Multiobjective and Strategic Tools, Chemical Engineering Research and Design, 84(11) (2006) 1019-1030.
[19] P.A. Gutiérrez, C. Martínez, Hybrid Artificial Neural Networks: Models, Algorithms and Data, Advances in Computational Intelligence, (2011) 177-184.
[20] R.K. Phanden, A. Jain, R. Verma, Integration of process planning and scheduling: a state-of-the-art review, International Journal of Computer Integrated Manufacturing, 24(6) (2011) 517-534.
[21] K.Y. Al-Qahtani, A. Elkamel, Planning and Integration of Refinery and Petrochemical Operations, Wiley, 2011.
[22] S. Mitin, P. Bochkarev, Mathematical modelling in the computer-aided process planning, 2016.
[23] A. Sedki, D. Ouazar, Hybrid Particle Swarm and Neural Network Approach for Streamflow Forecasting, Published online by Cambridge University Press, 5(7) (2010).
[24] R. Kadambur, P. Kotecha, Multi-level production planning in a petrochemical industry using elitist Teaching–Learning-Based-Optimization, Expert Systems with Applications, 42(1) (2015) 628-641.
[25] R.G. Siwi, F. Aljumah, J. Li, X. Xao, Optimal Strategic Planning of Integrated Petroleum and Petrochemical Supply Chain, in: A. Friedl, J.J. Klemeš, S. Radl, P.S. Varbanov, T. Wallek (Eds.) Computer Aided Chemical Engineering, Elsevier, 28 (2018), 1201-1206.
[26] Y.W. Guo, W.D. Li, A.R. Mileham, G.W. Owen, Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach, International Journal of Production Research, 47(14) (2009) 3775-3796.
[27] H. Kwon, B. Lyu, K. Tak, J. Lee, I. Moon, Optimization of Petrochemical Process Planning using Naphtha Price Forecasting and Process Modeling, in: K.V. Gernaey, J.K. Huusom, R. Gani (Eds.) Computer Aided Chemical Engineering, Elsevier, (2015), 2039-2044.
[28] Q.T. Pham, T. Kim Dung Phan, Apply neural network for improving production planning at Samarang petrol mine, (2016).
[29] Y.-M. Han, Z.-Q. Geng, Q.-X. Zhu, Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis, Energy Conversion and Management, 124 (2016) 73-83. | ||
|
آمار تعداد مشاهده مقاله: 518 تعداد دریافت فایل اصل مقاله: 495 |
||