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An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process

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Abstract

The real-time availability of key water quality parameters is of great importance for an advanced and optimized process control in wastewater treatment plants (WWTPs). However, due to the complex environment conditions and costly measuring instruments, it is generally difficult and time-consuming to measure certain key water quality parameters online, such as the effluent biochemical oxygen demand (BOD) and the effluent total nitrogen (TN). Recently, artificial neural networks have powered the online prediction tasks in several WWTPs. Hence, in this paper, an adaptive task-oriented radial basis function (ATO-RBF) network is developed to design prediction models for accurate timely acquirements of the effluent BOD and the effluent TN. The advantage of ATO-RBF network is that the architecture is not designed by human engineers; it is adaptively generated from the data to be processed. First, to enhance the learning ability and generalization performance of prediction models, an error correction-based growing strategy and a second-order learning algorithm are combined to design the ATO-RBF network. Then, RFB nodes with low significance would be pruned without sacrificing the learning accuracy, making the prediction model more compact. Additionally, the convergence of the ATO-RBF network is analyzed based on the Lyapunov criterion, which can guarantee its feasibility in practical applications. Finally, the proposed methodology is verified by benchmark simulations and real industrial data, showing superior prediction accuracy in compared with conventional methods.

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References

  1. Van Loosdrecht M, Brdjanovic D (2014) Anticipating the next century of wastewater treatment. Science 344(6191):1452–1453

    Article  Google Scholar 

  2. Olsson G, Carlsson B, Comas J et al (2014) Instrumentation, control and automation in wastewater—from London 1973 to Narbonne 2013. Water Sci Technol 69:1373–1385

    Article  Google Scholar 

  3. Corominas L, Garrido-Baserba M, Villez K et al (2018) Transforming data into knowledge for improved wastewater treatment operation: a critical review of techniques. Environ Model Softw 106:89–103

    Article  Google Scholar 

  4. Haimi H, Corona F, Mulas M et al (2015) Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP. Environ Model Softw 72:215–229

    Article  Google Scholar 

  5. Haimi H, Mulas M, Corona F, Vahala R (2013) Data-derived soft-sensors for biological wastewater treatment plants: an overview. Environ Model Softw 47:88–107

    Article  Google Scholar 

  6. Olsson G (2012) ICA and me—a subjective review. Water Res 46:1585–1624

    Article  Google Scholar 

  7. Kaelin D, Manser R, Rieger L et al (2009) Extension of ASM3 for two-step nitrification and denitrification and its calibration and validation with batch tests and pilot scale data. Water Res 43(6):1680–1692

    Article  Google Scholar 

  8. Yang M, Sun P, Wang R et al (2013) Simulation and optimization of ammonia removal at low temperature for a double channel oxidation ditch based on fully coupled activated sludge model (FCASM): a full-scale study. Biores Technol 143:538–548

    Article  Google Scholar 

  9. Harrou F, Dairi A, Sun Y, Senouci M (2018) Statistical monitoring of a wastewater treatment plant: a case study. J Environ Manage 223:807–814

    Article  Google Scholar 

  10. Dürrenmatt DJÔ, Gujer W (2012) Data-driven modeling approaches to support wastewater treatment plant operation. Environ Model Softw 30:47–56

    Google Scholar 

  11. Xie Y, Yu J, Xie S et al (2019) On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network. Neural Netw 116:1–10

    Article  Google Scholar 

  12. Xie S, Xie Y, Huang T et al (2019) Generalized predictive control for industrial processes based on neuron adaptive splitting and merging RBF neural network. IEEE Trans Ind Electron 66:1192–1202

    Article  Google Scholar 

  13. Pandiyaraju V, Logambigai R, Ganapathy S et al (2020) An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture. Wireless Pers Commun 112:243–259

    Article  Google Scholar 

  14. Thangaramya K, Kulothungan K, Logambiga R et al (2019) Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Comput Netw 151:211–223

    Article  Google Scholar 

  15. Sethukkarasi R, Ganapathy S, Yogesh P, Kannan A (2014) An intelligent neuro fuzzy temporal knowledge representation model for mining temporal patterns. J Intell Fuzzy Syst 26(3):1167–1178

    Article  MATH  Google Scholar 

  16. Han HG, Chen QL, Qiao JF (2011) An efficient self-organizing RBF neural network for water quality prediction. Neural Netw 24(7):717–725

    Article  MATH  Google Scholar 

  17. Yang T, Zhang L, Wang A et al (2013) Fuzzy modeling approach to predictions of chemical oxygen demand in activated sludge processes. Inf Sci 235:55–64

    Article  MathSciNet  MATH  Google Scholar 

  18. Li D, Yang HZ, Liang XF (2013) Prediction analysis of a wastewater treatment system using a Bayesian network. Environ Model Softw 40:140–150

    Article  Google Scholar 

  19. Han H, Qiao J (2013) Hierarchical neural network modeling approach to predict sludge volume index of wastewater treatment process. IEEE Trans Control Syst Technol 21:2423–2431

    Article  Google Scholar 

  20. Guo H, Jeong K, Lim J et al (2015) Prediction of effluent concentration in a wastewater treatment plant using machine learning models. J Environ Sci (China) 32:90–101

    Article  Google Scholar 

  21. Fernandez de Canete J, Del Saz-Orozco P, Baratti R et al (2016) Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network. Expert Syst Appl 63:8–19

    Article  Google Scholar 

  22. Li F, Qiao J, Han H, Yang C (2016) A self-organizing cascade neural network with random weights for nonlinear system modeling. Appl Soft Comput 42:184–193

    Article  Google Scholar 

  23. Zhu JJ, Kang L, Anderson PR (2018) Predicting influent biochemical oxygen demand: balancing energy demand and risk management. Water Res 128:304–313

    Article  Google Scholar 

  24. Zaghloul MS, Hamza RA, Iorhemen OT et al (2018) Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks. Sci Total Environ 645:449–459

    Article  Google Scholar 

  25. Cong Q, Yu W (2018) Integrated soft sensor with wavelet neural network and adaptive weighted fusion for water quality estimation in wastewater treatment process. Meas J Int Meas Confed 124:436–446

    Article  Google Scholar 

  26. Meng X, Rozycki P, Qiao JF, Wilamowski BM (2018) Nonlinear system modeling using RBF networks for industrial application. IEEE Trans Ind Inf 14(3):931–940

    Article  Google Scholar 

  27. Qiao JF, Wang L, Yang CL (2019) Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling. Neural Comput Appl 31:6163–6177

    Article  Google Scholar 

  28. Yang C, Qiao JF, Wang L, Zhu X (2019) Dynamical regularized echo state network for time series prediction. Neural Comput Appl 31:6781–6794

    Article  Google Scholar 

  29. Hunter D, Yu H, Pukish III et al (2012) Selection of proper neural network sizes and architectures—a comparative study. IEEE Trans Ind Inf 8(2):228–240

    Article  Google Scholar 

  30. Goh PY, Tan SC, Cheah WP et al (2019) Adaptive rough radial basis function neural network with prototype outlier removal. Inf Sci 505:127–143

    Article  Google Scholar 

  31. Yoon S, Jeon H, Kum D (2019) Predictive Cruise control using radial basis function network-based vehicle motion prediction and chance constrained model predictive control. IEEE Trans Intell Trans Syst 20(10):3832–3843

    Article  Google Scholar 

  32. de Jesus RJ, Elias I, Cruz DR et al (2017) Uniform stable radial basis function neural network for the prediction in two mechatronic processes. Neurocomputing 227:122–130

    Article  Google Scholar 

  33. Sharifipour M, Bonakdari H, Zaji AH (2018) Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction. Neural Comput Appl 30(3):855–864

    Article  Google Scholar 

  34. Li H, Wang Y, Xu X et al (2019) Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network. Appl Soft Comput 83:105620

    Article  Google Scholar 

  35. Alexandridis A, Chondrodima E, Sarimveis H (2016) Cooperative learning for radial basis function networks using particle swarm optimization. Appl Soft Comput 49:485–497

    Article  Google Scholar 

  36. Emamgholizadeh S, Kashi H, Marofpoor I et al (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11(3):645–656

    Article  Google Scholar 

  37. Izonin I, Tkachenko R, Kryvinska N et al (2019) Committee of SGTM neural-like structures with RBF kernel for insurance cost prediction task. 2019 IEEE 2nd Ukraine conference on electrical and computer engineering, UKRCON 2019—Proceedings. p 1037−1040.

  38. Tkachenko R, Tkachenko P, Izonin I et al (2019) Committee of the combined RBF-SGTM neural-like structures for prediction tasks. Lect Notes Comput Sci 11673:267–277

    Article  Google Scholar 

  39. Tkachenko R, Kutucu H, Izonin I et al (2018) Non-iterative neural-like predictor for solar energy in Libya. ICTERI 1:35–45

    Google Scholar 

  40. Que Q, Belkin M (2020) Back to the future: radial basis function network revisited. IEEE Trans Pattern Anal Mach Intell 42:1856–1867

    Article  Google Scholar 

  41. Qiao JF, Meng X, Li W (2018) An incremental neuronal-activity-based RBF neural network for nonlinear system modeling. Neurocomputing 302:1–11

    Article  Google Scholar 

  42. Kadirkamanathan V, Niranjan M (1993) A function estimation approach to sequential learning with neural networks. Neural Comput 5(6):954–975

    Article  Google Scholar 

  43. Yingwei L, SundararajanN SP (1997) A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Comput 9(2):461–478

    Article  MATH  Google Scholar 

  44. Huang GB, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern Part B Cybern 34(6):2284–2292

    Article  Google Scholar 

  45. Huang GB, Saratchandran P, Sundararajan N (2005) A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Trans Neural Netw 16(1):57–67

    Article  Google Scholar 

  46. Chen H, Gong Y, Hong X et al (2015) A fast adaptive tunable RBF network for nonstationary systems. IEEE Trans Cybern 46(12):2683–2692

    Article  Google Scholar 

  47. Qasem SN, Shamsuddin SM, Zain AM (2012) Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowl Based Syst 27:475–497

    Article  Google Scholar 

  48. Alexandridis A, Chondrodima E, Sarimveis H (2013) Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization. IEEE Trans Neural Netw Learn Syst 24:219–230

    Article  Google Scholar 

  49. Han HG, Lu W, Hou Y et al (2018) An adaptive-PSO-based self-organizing RBF neural network. IEEE Trans Neural Netw Learn Syst 29:104–117

    Article  MathSciNet  Google Scholar 

  50. Yu H, Reiner PD, Xie T et al (2014) An incremental design of radial basis function networks. IEEE Trans Neural Netw Learn Syst 25:1793–1803

    Article  Google Scholar 

  51. Wu X, Rózycki P, Wilamowski BM (2015) A hybrid constructive algorithm for single-layer feedforward networks learning. IEEE Trans Neural Netw Learn Syst 26:1659–1668

    Article  MathSciNet  Google Scholar 

  52. Qian X, Huang H, Chen X et al (2017) Generalized hybrid constructive learning algorithm for multioutput RBF networks. IEEE Trans Cybern 47:3634–3648

    Google Scholar 

  53. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  MATH  Google Scholar 

  54. Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892

    Article  Google Scholar 

  55. Vuković N, Miljković Z (2013) A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation. Neural Netw 46:210–226

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation of China under Grants 61903012, 61533002 and 61890930-5, the National Key Research and Development Project under Grant 2019YFC1906004-2, and the Beijing Natural Science Foundation under Grant 4204088.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Xi Meng, Yin Zhang & Junfei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing, China

    Xi Meng, Yin Zhang & Junfei Qiao

  3. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, China

    Xi Meng, Yin Zhang & Junfei Qiao

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  1. Xi Meng
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  2. Yin Zhang
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Correspondence to Xi Meng.

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Meng, X., Zhang, Y. & Qiao, J. An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process. Neural Comput & Applic 33, 11401–11414 (2021). https://doi.org/10.1007/s00521-020-05659-z

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  • DOI: https://doi.org/10.1007/s00521-020-05659-z

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