Share:


Prediction and optimization of a desulphurization system using CMAC neural network and genetic algorithm

    Zhiwei Kong Affiliation
    ; Yong Zhang Affiliation
    ; Xudong Wang Affiliation
    ; Yueyang Xu Affiliation
    ; Baosheng Jin Affiliation

Abstract

In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints.

Keyword : desulphurization system, cost, optimization, CMAC, GA

How to Cite
Kong, Z. ., Zhang, Y. ., Wang, X. ., Xu, Y. ., & Jin, B. . (2020). Prediction and optimization of a desulphurization system using CMAC neural network and genetic algorithm. Journal of Environmental Engineering and Landscape Management, 28(2), 74-87. https://doi.org/10.3846/jeelm.2020.12098
Published in Issue
Apr 7, 2020
Abstract Views
172
PDF Downloads
81
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Albus, J. S. (1975a). A new approach to manipulator control: The Cerebellar Model Articulation Controller (CMAC). Journal of Dynamic Systems, Measurement, and Control, 97(3), 220–227. https://doi.org/10.1115/1.3426922

Albus, J. S. (1975b). Data storage in the Cerebellar Model Articulation Controller (CMAC). Journal of Dynamic Systems, Measurement, and Control, 97(3), 228–233. https://doi.org/10.1115/1.3426923

Ansari, H. R., Zarei, M. J., Sabbaghi, S., & Keshavarz, P. (2018). A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks. International Communications in Heat and Mass Transfer, 91, 158–164. https://doi.org/10.1016/j.icheatmasstransfer.2017.12.012

Armaghani, D. J., Hasanipanah, M., Mahdiyar, A., Majid, M. Z. A., Amnieh, H. B., & Tahir, M. M. (2018). Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications, 29(9), 619–629. https://doi.org/10.1007/s00521-016-2598-8

Biswas, P., Pramanik, S., & Giri, B. C. (2014). Entropy based grey relational analysis method for multi-attribute decision making under single valued neutrosophic assessments. Neutrosophic Sets and Systems, 2, 102–110.

Cheng, H., & Xie, J. (2017, October). Study on the application of recurrent fuzzy neural network in PH control system of absorption tower. In 2017 Chinese Automation Congress (CAC) (pp. 5962–5966). IEEE. https://doi.org/10.1109/CAC.2017.8243850

Cheng, H., Cui, L., & Li, J. (2017, October). Application of improved BP neural network based on LM algorithm in desulfurization system of thermal power plant. In 2017 Chinese Automation Congress (CAC) (pp. 5917–5920). IEEE. https://doi.org/10.1109/CAC.2017.8243841

Ching-Tsan, C., & Chun-Shin, L. (1996). CMAC with general basis functions. Neural Networks, 9(7), 1199–1211. https://doi.org/10.1016/0893-6080(96)00132-3

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X

Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.

Dou, B., Pan, W., Jin, Q., Wang, W., & Li, Y. (2009). Prediction of SO2 removal efficiency for wet flue gas desulfurization. Energy Conversion and Management, 50(10), 2547–2553. https://doi.org/10.1016/j.enconman.2009.06.012

Fang, K. T. (1994). Uniform design and uniform design table. Science Press.

Fang, K. T., Lin, D. K., Winker, P., & Zhang, Y. (2000). Uniform design: Theory and application. Technometrics, 42(3), 237–248. https://doi.org/10.1080/00401706.2000.10486045

Fu, J., Xiao, H., Wang, T., Zhang, R., Wang, L., & Shi, X. (2019, July). Prediction model of desulfurization efficiency of coalfired power plants based on long short-term memory neural network. In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 40–45). IEEE. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00030

Guo, Y., Xu, Z., Zheng, C., Shu, J., Dong, H., Zhang, Y., Weng, W., & Gao, X. (2019). Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method. Journal of the Air & Waste Management Association, 69(5), 565–575. https://doi.org/10.1080/10962247.2018.1551252

Gutiérrez Ortiz, F. J., Vidal, F., Ollero, P., Salvador, L., Cortés, V., & Gimenez, A. (2006). Pilot-plant technical assessment of wet flue gas desulfurization using limestone. Industrial & Engineering Chemistry Research, 45(4), 1466–1477. https://doi.org/10.1021/ie051316o

Horton, P., Jaboyedoff, M., & Obled, C. (2018). Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps. Journal of Hydrology, 556, 1220–1231. https://doi.org/10.1016/j.jhydrol.2017.04.017

Jin, D., & Lin, S. (Eds.). (2012). Advances in computer science and information engineering (Vol. 1). Part of the Advances in Intelligent and Soft Computing book series (AINSC, Vol. 168). Springer. https://doi.org/10.1007/978-3-642-30126-1

Jin, W., Li, Z. J., Wei, L. S., & Zhen, H. (2000, August). The improvements of BP neural network learning algorithm. In WCC 2000-ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000 (Vol. 3, pp. 1647–1649). IEEE. https://doi.org/10.1109/ICOSP.2000.893417

Kiil, S., Michelsen, M. L., & Dam-Johansen, K. (1998). Experimental investigation and modeling of a wet flue gas desulfurization pilot plant. Industrial & Engineering Chemistry Research, 37(7), 2792–2806. https://doi.org/10.1021/ie9709446

Li, R., Lin, D. K. J., & Chen, Y. (2004). Uniform design: Design, analysis and applications. International Journal of Materials and Product Technology, 20(1–3), 101–114. https://doi.org/10.1504/IJMPT.2004.003915

Li, J. F., Liao, H., Normand, B., Cordier, C., Maurin, G., Foct, J., & Coddet, C. (2003). Uniform design method for optimization of process parameters of plasma sprayed TiN coatings. Surface and Coatings Technology, 176(1), 1–13. https://doi.org/10.1016/S0257-8972(03)00019-7

Lin, C., & Chiang, C. (1997). Learning convergence of CMAC technique. IEEE Transactions on Neural Networks, 8(6), 1281–1292. https://doi.org/10.1109/72.641451

Lin, C. C. D., & Wang, H. P. B. (1996). Performance analysis of rotating machinery using enhanced cerebellar model articulation controller (E-CMAC) neural networks. Computers & Industrial Engineering, 30(2), 227–242. https://doi.org/10.1016/0360-8352(95)00168-9

Liu, L., Deng, Q., Zheng, C., Wang, S., Wang, J., & Gao, X. (2019). An insight into electrostatic field effects on SO3 adsorption by CaO with CO2, SO2 and H2O: A DFT approach. Aerosol and Air Quality Research, 19(10), 2320–2330. https://doi.org/10.4209/aaqr.2019.04.0206

Liu, S., & Forrest, J. Y. L. (2010). Grey systems: Theory and applications. Springer Science & Business Media.

Liu, S., Cai, H., Cao, Y., & Yang, Y. (2011, October). Advance in grey incidence analysis modelling. In 2011 IEEE International Conference on Systems, Man, and Cybernetics. Anchorage, AK (pp. 1886–1890). IEEE. https://doi.org/10.1109/ICSMC.2011.6083947

Mostofi, N., & Hasanlou, M. (2017). Feature selection of various land cover indices for monitoring surface heat island in Tehran city using Landsat 8 imagery. Journal of Environmental Engineering and Landscape Management, 25(3), 241–250. https://doi.org/10.3846/16486897.2016.1223084

Munroe, S., Sandoval, K., Martens, D. E., Sipkema, D., & Pomponi, S. A. (2019). Genetic algorithm as an optimization tool for the development of sponge cell culture media. In Vitro Cellular & Developmental Biology-Animal, 55(3), 149–158. https://doi.org/10.1007/s11626-018-00317-0

Schafer, R. (2011). What is a Savitzky-Golay filter? IEEE Signal Processing Magazine, 28(4), 111–117. https://doi.org/10.1109/MSP.2011.941097

Tamura, T., Eguchi, M., Qiao, M., & Ohmori, H. (2017). Diesel engine combustion control with triple fuel injections based on cerebellar model articulation controller (CMAC) in feedback error learning. In The Proceedings of the International Symposium on Diagnostics and Modeling of Combustion in Internal Combustion Engines, 2017.9 (p. C110). The Japan Society of Mechanical Engineers. https://doi.org/10.1299/jmsesdm.2017.9.C110

Tao, T., Lu, H. C., & Su, S. F. (2002). Robust CMAC control schemes for dynamic trajectory following. Journal of the Chinese Institute of Engineers, 25(3), 253–264. https://doi.org/10.1080/02533839.2002.9670700

Villanueva Perales, A. L., Gutiérrez Ortiz, F. J., Vidal Barrero, F., & Ollero, P. (2010). Using neural networks to address nonlinear pH control in wet limestone flue gas desulfurization plants. Industrial & Engineering Chemistry Research, 49(5), 2263–2272. https://doi.org/10.1021/ie9007584

Wang, P., & Dai, G. (2018). Synergistic effect between spraying layers on the performance of the WFGD spray column. AsiaPacific Journal of Chemical Engineering, 13(6), e2266. https://doi.org/10.1002/apj.2266

Warych, J., & Szymanowski, M. (2001). Model of the wet limestone flue gas desulfurization process for cost optimization. Industrial & Engineering Chemistry Research, 40(12), 2597– 2605. https://doi.org/10.1021/ie0005708

Warych, J., & Szymanowski, M. (2002). Optimum values of process parameters of the “wet limestone flue gas desulfurization system”. Chemical Engineering & Technology, 25(4), 427–432. https://doi.org/10.1002/1521-4125(200204)25:4<427::AID-CEAT427>3.0.CO;2-X

Whitley, D., & Tutorial, A. G. A. (1994). A genetic algorithm tutorial. Statistics and Computing, 4, 65–85. https://doi.org/10.1007/BF00175354

Wu, Y., Shen, L. E., & Zhang, L. (2011). Study on nonlinear pH control strategy based on external recurrent neural network. Procedia Engineering, 15, 866–871. https://doi.org/10.1016/j.proeng.2011.08.160

Yang, Z. K., Liu, C. Y., Song, X. L., Song, Z. Y., & Wang, Z. S. (2016, July). Application of RBF neural network PID in wet flue gas desulfurization of thermal power plant. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 1, pp. 301–306). Jeju, South Korea. IEEE. https://doi.org/10.1109/ICMLC.2016.7860918

Zhang, Y., Liu, Q., Wei, H., Du, Z., & Zhu, Y. (2019, October). Study on economy of flue gas ultra-low emission in coal-fired power. In IOP Conference Series: Earth and Environmental Science (Vol. 349, No. 1, 012017). IOP Publishing. https://doi.org/10.1088/1755-1315/349/1/012017

Zhou, D., Shi, M., Chao, F., Lin, C. M., Yang, L., Shang, C., & Zhou, C. (2018). Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller. Neurocomputing, 282, 218–231. https://doi.org/10.1016/j.neucom.2017.12.016