A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 5
Aug.  2018

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Junfei Qiao and Hongbiao Zhou, "Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 968-976, Sept. 2018. doi: 10.1109/JAS.2018.7511168
Citation: Junfei Qiao and Hongbiao Zhou, "Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 968-976, Sept. 2018. doi: 10.1109/JAS.2018.7511168

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

doi: 10.1109/JAS.2018.7511168
Funds:

the National Science Foundation for Distinguished Young Scholars of China 61225016

the State Key Program of National Natural Science of China 61533002

More Information
  • Modeling of energy consumption (EC) and effluent quality (EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process (WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network (DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity. The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

     

  • loading
  • [1]
    J. F. Wan, J. Gu, Q. Zhao, and Y. Liu, "Cod capture: a feasible option towards energy self-sufficient domestic wastewater treatment, " Sci. Rep. , vol. 6, pp. Article No. 25054, Apr. 2016.
    [2]
    D. Mamais, C. Noutsopoulos, A. Dimopoulou, A. Stasinakis, and T. D. Lekkas, "Wastewater treatment process impact on energy savings and greenhouse gas emissions, " Water Sci. Technol. , vol. 71, no. 2, pp. 303-308, Jan. 2015. http://www.ncbi.nlm.nih.gov/pubmed/25633956
    [3]
    F. Hernández Sancho, M. Molinos-Senante, and R. Sala-Garrido, "Energy efficiency in Spanish wastewater treatment plants: a non-radial DEA approach, " Sci. Total Environ. , vol. 409, no. 14, pp. 2693-2699, Jun. 2011. http://europepmc.org/abstract/MED/21549411
    [4]
    Y. Yang, J. K. Yang, J. L. Zuo, Y. Li, S. He, X. Yang, and K. Zhang, "Study on two operating conditions of a full-scale oxidation ditch for optimization of energy consumption and effluent quality by using CFD model, " Water Res. , vol. 45, no. 11, pp. 3439-3452, May 2011. http://www.ncbi.nlm.nih.gov/pubmed/21529877
    [5]
    I. Vera, K. Sáez, and G. Vidal, "Performance of 14 full-scale sewage treatment plants: comparison between four aerobic technologies regarding effluent quality, sludge production and energy consumption, " Environ. Technol. , vol. 34, no. 15, pp. 2267-2275, Jan. 2013. http://europepmc.org/abstract/med/24350481
    [6]
    D. J. Dürrenmatt and W. Gujer, "Data-driven modeling approaches to support wastewater treatment plant operation, " Environ. Model. Softw. , vol. 30, pp. 47-56, Apr. 2012. http://europepmc.org/abstract/med/2673599
    [7]
    R. Noori, H. D. Yeh, M. Abbasi, F. T. Kachoosangi, and S. Moazami, "Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand, " J. Hydrol. , vol. 527, pp. 833-843, Aug. 2015. http://www.sciencedirect.com/science/article/pii/S0022169415003960
    [8]
    J. F. Qiao, W. Li, and H. G. Han, "Soft computing of biochemical oxygen demand using an improved T-S fuzzy neural network, " Chin. J. Chem. Eng. , vol. 22, no. 11-12, pp. 1254-1259, Nov. 2014. http://www.sciencedirect.com/science/article/pii/S100495411400130X
    [9]
    A. Kusiak, Y. H. Zeng, and Z. J. Zhang, "Modeling and analysis of pumps in a wastewater treatment plant: A data-mining approach, " Eng. Appl. Artif. Intell. , vol. 26, no. 7, pp. 1643-1651, Aug. 2013. http://www.sciencedirect.com/science/article/pii/S0952197613000572
    [10]
    H. G. Han, L. Zhang, and J. F. Qiao, "An energy consumption model of wastewater treatment process based on adaptive regressive kernel function, " CIESC J. , vol. 67, no. 3, pp. 947-953, Mar. 2016.
    [11]
    M. Pratama, M. J. Er, X. Li, R. J. Oentaryo, E. Lughofer, and I. Arifin, "Data driven modeling based on dynamic parsimonious fuzzy neural network, " Neurocomputing, vol. 110, pp. 18-28, Jun. 2013. http://www.sciencedirect.com/science/article/pii/S0925231212008934
    [12]
    R. J. Hathaway and Y. K. Hu, "Density-weighted fuzzy c-means clustering, " IEEE Trans. Fuzzy Syst. , vol. 17, no. 1, pp. 243-252, Feb. 2009.
    [13]
    L. Teslic, B. Hartmann, O. Nelles, and I. Skrjanc, "Nonlinear system identification by gustafson-kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process, " IEEE Trans. Neural Netw. , vol. 22, no. 12, pp. 1941-1951, Dec. 2011. http://ieeexplore.ieee.org/document/6060917/
    [14]
    H. Malek, M. M. Ebadzadeh, and M. Rahmati, "Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm, " Appl. Intell. , vol. 37, no. 2, pp. 280-289, Sep. 2012.
    [15]
    S. Q. Wu and M. J. Er, "Dynamic fuzzy neural networks -a novel approach to function approximation, " IEEE Trans. Syst. Man Cybern. B (Cybern. ), vol. 30, no. 2, pp. 358-364, Apr. 2000.
    [16]
    H. G. Han, X. L. Wu, and J. F. Qiao, "Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm, " IEEE Trans. Cybern. , vol. 44, no. 4, pp. 554-564, Apr. 2014. http://europepmc.org/abstract/med/23782841
    [17]
    B. M. Wilamowski and H. Yu, "Neural network learning without backpropagation, " IEEE Trans. Neural Netw. , vol. 21, no. 11, pp. 1793-1803, Nov. 2010.
    [18]
    M. Davanipoor, M. Zekri, and F. Sheikholeslam, "Fuzzy wavelet neural network with an accelerated hybrid learning algorithm, " IEEE Trans. Fuzzy Syst. , vol. 20, no. 3, pp. 463-470, Jun. 2012. http://ieeexplore.ieee.org/document/6081924/
    [19]
    W. Q. Zhao, K. Li, and G. W. Irwin, "A new gradient descent approach for local learning of fuzzy neural models, " IEEE Trans. Fuzzy Syst. , vol. 21, no. 1, pp. 30-44, Feb. 2013. http://ieeexplore.ieee.org/document/6203571/
    [20]
    G. Leng, T. M. McGinnity, and G. Prasad, "Design for self-organizing fuzzy neural networks based on genetic algorithms, " IEEE Trans. Fuzzy Syst. , vol. 14, no. 6, pp. 755-766, Dec. 2006. http://ieeexplore.ieee.org/document/4016084/
    [21]
    A. Rodriguez and A. Laio, "Clustering by fast search and find of density peaks, " Science, vol. 344, no. 6191, pp. 1492-1496, Jun. 2014. http://www.ncbi.nlm.nih.gov/pubmed/24970081
    [22]
    B. M. Wilamowski and H. Yu, "Improved computation for levenbergmarquardt training, " IEEE Trans. Neural Netw. , vol. 21, no. 6, pp. 930-937, Jun. 2010. http://europepmc.org/abstract/MED/20409991
    [23]
    H. G. Han, L. M. Ge, and J. F. Qiao, "An adaptive second order fuzzy neural network for nonlinear system modeling, " Neurocomputing, vol. 214, pp. 837-847, Nov. 2016. http://www.sciencedirect.com/science/article/pii/S0925231216307329
    [24]
    J. F. Qiao and W. Zhang, "Dynamic multi-objective optimization control for wastewater treatment process, " Neural Comput. Appl. , vol. 29, no. 11, pp. 1261-1271, Jun. 2018.
    [25]
    T. Maere, B. Verrecht, S. Moerenhout, S. Judd, and I. Nopens, "BSMMBR: a benchmark simulation model to compare control and operational strategies for membrane bioreactors, " Water Res. , vol. 45, no. 6, pp. 2181-2190, Mar. 2011. http://www.ncbi.nlm.nih.gov/pubmed/21329957
    [26]
    P. Vanrolleghem and S. Gillot, "Robustness and economic measures as control benchmark performance criteria, " Water Sci. Technol. , vol. 45, no. 4-5, pp. 117-126, Feb. 2002. http://www.ncbi.nlm.nih.gov/pubmed/11936624
    [27]
    E. Lee, S. Han, and H. Kim, "Development of software sensors for determining total phosphorus and total nitrogen in waters, " Int. J. Environ. Res. Public Health, vol. 10, no. 1, pp. 219-236, Jan. 2013. http://europepmc.org/articles/PMC3564139/

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(3)

    Article Metrics

    Article views (1335) PDF downloads(36) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return