Prediction of effluent concentration in a wastewater treatment plant using machine learning models.

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R2), Nash-Sutcliff efficiency (NSE), relative efficiency criteria (drel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.

[1]  Shie-Yui Liong,et al.  FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1 , 2002 .

[2]  S. Burnley,et al.  Energy implications of the thermal recovery of biodegradable municipal waste materials in the United Kingdom. , 2011, Waste management.

[3]  Paolo Pavan,et al.  The performances of digesters treating the organic fraction of municipal solid wastes differently sorted , 1990 .

[4]  Ammaiyappan Selvam,et al.  Bioelectricity production from acidic food waste leachate using microbial fuel cells: Effect of microbial inocula , 2013 .

[5]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[6]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[7]  Tsong-Lin Lee.  Back-propagation neural network for long-term tidal predictions , 2004 .

[8]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[9]  Shie-Yui Liong,et al.  An ANN application for water quality forecasting. , 2008, Marine pollution bulletin.

[10]  T. Ellis,et al.  Comparison of Temperature‐Phased and Two‐Phase Anaerobic Co‐Digestion of Primary Sludge and Municipal Solid Waste , 2001, Water environment research : a research publication of the Water Environment Federation.

[11]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[12]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[13]  Ioannis K. Nikolos,et al.  Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation , 2011 .

[14]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

[15]  Franco Cecchi,et al.  Comparison of co‐digestion performance of two differently collected organic fractions of municipal solid waste with sewage sludges , 1988 .

[16]  Gui Hwan Han,et al.  Volumetric scale-up of a three stage fermentation system for food waste treatment. , 2008, Bioresource technology.

[17]  Hung-Suck Park,et al.  Methane production potential of leachate generated from Korean food waste recycling facilities: a lab-scale study. , 2009, Waste management.

[18]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[19]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[20]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[21]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[22]  Gérard Dreyfus,et al.  Neural networks - methodology and applications , 2005 .

[23]  Ali Yuzir,et al.  Anaerobic Digestion of Paper Mill Wastewater , 2012 .

[24]  Malte Hermansson,et al.  Effects of Ionic Strength on Bacterial Adhesion and Stability of Flocs in a Wastewater Activated Sludge System , 1994, Applied and environmental microbiology.

[25]  C. Brepols,et al.  Modelling of a membrane bioreactor system for municipal wastewater treatment , 2003 .

[26]  J. Mata-Álvarez,et al.  Biomethanization of the Organic Fraction of Municipal Solid Wastes , 2015 .

[27]  Chounghyun Seong,et al.  Predicting the impacts of climate change on nonpoint source pollutant loads from agricultural small watershed using artificial neural network. , 2010, Journal of environmental sciences.

[28]  Hang-Sik Shin,et al.  Acidogenesis of Lipids-Containing Wastewater in Anaerobic Sequencing Batch Reactor , 2009 .

[29]  Ali Yazici,et al.  A neural network-based approach for calculating dissolved oxygen profiles in reservoirs , 2003, Neural Computing & Applications.

[30]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[31]  Roman M. Balabin,et al.  Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. , 2011, The Analyst.

[32]  M. McKee,et al.  SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES 1 , 2006 .

[33]  Krist V. Gernaey,et al.  Activated sludge wastewater treatment plant modelling and simulation: state of the art , 2004, Environ. Model. Softw..

[34]  Grietje Zeeman,et al.  Solids removal in upflow anaerobic reactors, a review. , 2003, Bioresource technology.

[35]  Kevin J. Kennedy,et al.  Technical Feasibility of Anaerobic Co-Digestion of Sewage Sludge and Municipal Solid Waste , 1998 .

[36]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[37]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[38]  M. Mori,et al.  Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis , 2002 .

[39]  Alejandro Rivas,et al.  Model-based optimisation of Wastewater Treatment Plants design , 2008, Environ. Model. Softw..

[40]  S. Chae,et al.  Nutrient removal using anaerobically fermented leachate of food waste in the BNR process. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[41]  Mac McKee,et al.  Applicability of statistical learning algorithms in groundwater quality modeling , 2005 .

[42]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[43]  Maged M. Hamed,et al.  Prediction of wastewater treatment plant performance using artificial neural networks , 2004, Environ. Model. Softw..

[44]  Stefano Marsili-Libelli,et al.  A modified Activated Sludge Model No. 3 ASM3) with two-step nitrification-denitrification , 2007, Environ. Model. Softw..

[45]  S. Soyupak,et al.  Case studies on the use of neural networks in eutrophication modeling , 2000 .

[46]  K. Chau,et al.  Neural network and genetic programming for modelling coastal algal blooms , 2006 .

[47]  Jan A. Oleszkiewicz,et al.  Anaerobic co‐composting of municipal solid waste and waste sludge at hlgh total solids levels , 1992 .

[48]  S. Ledakowicz,et al.  Anaerobic co-digestion of sewage sludge and organic fraction of municipal solid wastes , 2003 .

[49]  Robert Michael Lewis,et al.  A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds , 2002, SIAM J. Optim..

[50]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[51]  Y. Pachepsky,et al.  Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. , 2011, Water research.

[52]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[53]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[54]  Luis A. Bastidas,et al.  Multiobjective analysis of chaotic dynamic systems with sparse learning machines , 2006 .

[55]  P. Ramos,et al.  Biodegradable municipal solid waste: characterization and potential use as animal feedstuffs. , 2005, Waste management.

[56]  Taeshik Shon,et al.  A hybrid machine learning approach to network anomaly detection , 2007, Inf. Sci..

[57]  Seo Jin Ki,et al.  Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan Reservoir, Korea. , 2009, The Science of the total environment.

[58]  Han Yan,et al.  Adaptive neuro fuzzy inference system for classification of water quality status. , 2010, Journal of environmental sciences.

[59]  Hung-Suck Park,et al.  Methane production from food waste leachate in laboratory-scale simulated landfill. , 2010, Waste management.

[60]  Weng Tat Chan,et al.  Derivation of Pareto front with genetic algorithm and neural network , 2001 .

[61]  Arup Kumar Sarma,et al.  Artificial neural network model for synthetic streamflow generation , 2007 .

[62]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[63]  Yoon-Seok Timothy Hong,et al.  Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis. , 2003, Water research.

[64]  Farouq S Mjalli,et al.  Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. , 2007, Journal of environmental management.

[65]  Hung-Suck Park,et al.  Anaerobic co‐digestion of food waste leachate and piggery wastewater for methane production: statistical optimization of key process parameters , 2012 .

[66]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .