Modelling effluent quality based on a real-time optical monitoring of the wastewater treatment process

被引:10
作者
Tomperi, Jani [1 ]
Koivuranta, Elisa [2 ]
Kuokkanen, Anna [3 ]
Leiviska, Kauko [1 ]
机构
[1] Univ Oulu, Control Engn, POB 4300, FI-90014 Oulu, Finland
[2] Univ Oulu, Fibre & Particle Engn, Oulu, Finland
[3] HSY Helsinki Reg Environm Serv Author, Helsinki, Finland
关键词
Activated sludge process; BOD; COD; suspended solids; variable selection methods; IMAGE-ANALYSIS; SELECTION; BULKING; SYSTEMS;
D O I
10.1080/09593330.2016.1181674
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A novel optical monitoring device was used for imaging an activated sludge process in situ during a period of over one year. In this study, the dependencies between the results of image analysis and the process measurements were studied, and the optical monitoring results were utilized to predict the important quality parameters for the wastewater treatment process efficiency: suspended solids, biological oxygen demand, chemical oxygen demand, total nitrogen and total phosphorous in biologically treated wastewater. The optimal subsets of variables for each model were searched using five variable selection methods. It was shown that online optical analysis results have clear dependencies on some process variables and the purification result. The model based on optical monitoring and process variables from the early stage of the treatment process can be used to predict the levels of important quality parameters, and to show the quality of the biologically treated wastewater hours in advance. This study confirms that the optical monitoring method is a valuable tool for monitoring a wastewater treatment process and receiving new information in real time. Combined with predictive modelling, it has the potential to be used in process control, keeping the process in a stable operating condition and avoiding environmental risks.
引用
收藏
页码:1 / 13
页数:13
相关论文
共 18 条
  • [1] Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression
    Amaral, AL
    Ferreira, EC
    [J]. ANALYTICA CHIMICA ACTA, 2005, 544 (1-2) : 246 - 253
  • [2] [Anonymous], 1991, Handbook of Genetic Algorithms
  • [3] The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
    Araújo, MCU
    Saldanha, TCB
    Galvao, RKH
    Yoneyama, T
    Chame, HC
    Visani, V
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2001, 57 (02) : 65 - 73
  • [4] A survey of cross-validation procedures for model selection
    Arlot, Sylvain
    Celisse, Alain
    [J]. STATISTICS SURVEYS, 2010, 4 : 40 - 79
  • [5] Predicting the onset of filamentous bulking in biological wastewater treatment systems by exploiting image analysis information
    Banadda, EN
    Smets, IY
    Jenné, R
    Van Impe, JF
    [J]. BIOPROCESS AND BIOSYSTEMS ENGINEERING, 2005, 27 (05) : 339 - 348
  • [6] On techniques for the measurement of the mass fractal dimension of aggregates
    Bushell, GC
    Yan, YD
    Woodfield, D
    Raper, J
    Amal, R
    [J]. ADVANCES IN COLLOID AND INTERFACE SCIENCE, 2002, 95 (01) : 1 - 50
  • [7] Guyon I, 2003, J MACH LEARN RES, V3, P1157, DOI DOI 10.1162/153244303322753616
  • [8] Integration of intelligent systems in development of smart adaptive systems
    Juuso, EK
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2004, 35 (03) : 307 - 337
  • [9] On-line optical monitoring of activated sludge floc morphology
    Koivuranta, E.
    Stoor, T.
    Hattuniemi, J.
    Niinimaki, J.
    [J]. JOURNAL OF WATER PROCESS ENGINEERING, 2015, 5 : 28 - 34
  • [10] Optical monitoring of activated sludge flocs in bulking and non-bulking conditions
    Koivuranta, E.
    Keskitalo, J.
    Haapala, A.
    Stoor, T.
    Saren, M.
    Niinimaki, J.
    [J]. ENVIRONMENTAL TECHNOLOGY, 2013, 34 (05) : 679 - 686