Environmental odour management by artificial neural network - A review.

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome.

[1]  V. Belgiorno,et al.  Control of odour emission in wastewater treatment plants by direct and undirected measurement of odour emission capacity. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[2]  Octavian Postolache,et al.  Smart Sensor Network for Air Quality Monitoring Applications , 2009, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[3]  Guohe Huang,et al.  Artificial intelligence for management and control of pollution minimization and mitigation processes , 2003 .

[4]  Kevin L. Goodner,et al.  The dangers of creating false classifications due to noise in electronic nose and similar multivariate analyses , 2001 .

[5]  P. Kanaroglou,et al.  Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. , 2016, Journal of environmental management.

[6]  T Fontes,et al.  Can artificial neural networks be used to predict the origin of ozone episodes? , 2014, The Science of the total environment.

[7]  Rudolf Braun,et al.  Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox , 2005, Environ. Model. Softw..

[8]  Praveena Bhatt,et al.  Bioreactors for treatment of VOCs and odours - a review. , 2010, Journal of environmental management.

[9]  C. V. D. Malsburg,et al.  Frank Rosenblatt: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms , 1986 .

[10]  R Lebrero,et al.  Monitoring techniques for odour abatement assessment. , 2010, Water research.

[11]  Beatrice Lazzerini,et al.  An electronic nose for odour annoyance assessment , 2001 .

[12]  R. Stuetz,et al.  Odor Assessment and Management in Wastewater Treatment Plants: A Review , 2011 .

[13]  John Leis,et al.  Quantification of odours from piggery effluent ponds using an electronic nose and an Artificial Neural Network , 2003 .

[14]  A. Romain,et al.  AN ALTERNATIVE APPROACH OF THE E-NOSE TRAINING PHASE IN ODOUR IMPACT ASSESSMENT , 2012 .

[15]  Brajesh Dubey,et al.  Technological and life cycle assessment of organics processing odour control technologies. , 2015, The Science of the total environment.

[16]  A. D. Wilson,et al.  Review of electronic-nose technologies and algorithms to detect hazardous chemicals in the environment , 2012 .

[17]  C. Guy,et al.  Odour quantification by a sensor array: An application to landfill gas odours from two different municipal waste treatment works , 2007 .

[18]  Rajib Bandyopadhyay,et al.  Calibration transfer between electronic nose systems for rapid in situ measurement of pulp and paper industry emissions. , 2014, Analytica chimica acta.

[19]  Vincenzo Naddeo,et al.  Odour Emissions Characterization from Wastewater Treatment Plants by Different Measurement Methods , 2014 .

[20]  Kunio Kashino,et al.  Modular representation of layered neural networks , 2017, Neural Networks.

[21]  C. Chemel,et al.  Odour-impact assessment around a landfill site from weather-type classification, complaint inventory and numerical simulation. , 2012, Journal of environmental management.

[22]  Ömer Faruk Ertugrul,et al.  A novel type of activation function in artificial neural networks: Trained activation function , 2018, Neural Networks.

[23]  Shapour Mohammadi,et al.  A new test for the significance of neural network inputs , 2018, Neurocomputing.

[24]  Thierry Pagé,et al.  Measurement, prediction, and monitoring of odors in the environment: a critical review , 2014 .

[25]  Shiying Yang,et al.  Use of peroxymonosulfate in wet scrubbing process for efficient odor control , 2016 .

[26]  K. Manimala,et al.  Implementation of Hyperbolic Tangent Activation Function in VLSI , 2014 .

[27]  Yunhe Pan,et al.  Heading toward Artificial Intelligence 2.0 , 2016 .

[28]  José Ignacio Suárez,et al.  On-line classification of pollutants in water using wireless portable electronic noses. , 2016, Chemosphere.

[29]  Sung-Bae Cho,et al.  Radial basis function neural networks: a topical state-of-the-art survey , 2016, Open Comput. Sci..

[30]  Bartosz Szulczynski,et al.  Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks , 2018, Sensors.

[31]  Hanna Piekarska-Boniecka,et al.  Artificial neural networks for modeling ammonia emissions released from sewage sludge composting , 2012 .

[32]  A. Couvert,et al.  Treatment of odorous sulphur compounds by chemical scrubbing with hydrogen peroxide--stabilisation of the scrubbing solution. , 2006, Environmental science & technology.

[33]  Tomasz Dymerski,et al.  Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants , 2017, Sensors.

[34]  K. Persaud,et al.  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose , 1982, Nature.

[35]  G. d'Imporzano,et al.  Anaerobic digestion coupled with digestate injection reduced odour emissions from soil during manure distribution. , 2018, The Science of the total environment.

[36]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[37]  R. Stuetz,et al.  Review of odour abatement in sewer networks , 2016 .

[38]  Davide Franco,et al.  A review of odour impact criteria in selected countries around the world. , 2017, Chemosphere.

[39]  Steven J. Hoff,et al.  Summary of performance data for technologies to control gaseous, odor, and particulate emissions from livestock operations: Air management practices assessment tool (AMPAT) , 2016, Data in brief.

[40]  Rey-Chue Hwang,et al.  The estimations of ammonia concentration by using neural network SH-SAW sensors , 2011, Expert Syst. Appl..

[41]  C. Kennes,et al.  Neural network models for biological waste-gas treatment systems. , 2011, New biotechnology.

[42]  G. Sahoo,et al.  Application of artificial neural networks to assess pesticide contamination in shallow groundwater. , 2006, Science of the Total Environment.

[43]  Sunil Kumar,et al.  Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents. , 2011, Journal of hazardous materials.

[44]  S. Kanae,et al.  Estimating monthly total nitrogen concentration in streams by using artificial neural network. , 2011, Journal of environmental management.

[45]  Yung-Cheol Byun,et al.  An Adaptive Stopping Criterion for Backpropagation Learning in Feedforward Neural Network , 2014, MUE 2014.

[46]  P Gostelow,et al.  Odour measurements for sewage treatment works. , 2001, Water research.

[47]  Michael Y. Hu,et al.  Effect of data standardization on neural network training , 1996 .

[48]  Seref Naci Engin,et al.  Determination of the relationship between sewage odour and BOD by neural networks , 2005, Environ. Model. Softw..

[49]  Muhammad Moinuddin,et al.  A Novel Kernel for RBF Based Neural Networks , 2014 .

[50]  D. Struppa,et al.  Application of Recurrent Neural Networks for Drought Projections in California , 2017 .

[51]  Andrzej Szczurek,et al.  Relationship between odour intensity assessed by human assessor and TGS sensor array response , 2005 .

[52]  V Belgiorno,et al.  Odour monitoring of small wastewater treatment plant located in sensitive environment. , 2008, Water science and technology : a journal of the International Association on Water Pollution Research.

[53]  V Belgiorno,et al.  Instrumental characterization of odour: a combination of olfactory and analytical methods. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[54]  George Nagy.  Neural networks-then and now , 1991, IEEE Trans. Neural Networks.

[55]  Selena Sironi,et al.  Electronic noses for the continuous monitoring of odours from a wastewater treatment plant at specific receptors: Focus on training methods , 2008 .

[56]  I. Konstantinou,et al.  TiO2 photocatalysis of 2-isopropyl-3-methoxy pyrazine taste and odor compound in aqueous phase: Kinetics, degradation pathways and toxicity evaluation , 2015 .

[57]  G. Payre,et al.  Modified quasi-Newton methods for training neural networks , 1996 .

[58]  Riyanarto Sarno,et al.  Estimating Gas Concentration using Artificial Neural Network for Electronic Nose , 2017 .

[59]  Stefano Giuliani,et al.  Performance Study of E-Nose Measurement Chamber for Environmental Odour Monitoring , 2012 .

[60]  P. Koprinkova,et al.  Data-scaling problems in neural-network training , 1999 .

[61]  M. Omid,et al.  Sensitivity analysis of energy inputs in crop production using artificial neural networks , 2018, Journal of Cleaner Production.

[62]  Achim J. Lilienthal,et al.  Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms , 2013, Adv. Robotics.

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

[64]  H. Troy Nagle,et al.  Performance of the Levenberg–Marquardt neural network training method in electronic nose applications , 2005 .

[65]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[66]  S A Parsons,et al.  Developments in odour control and waste gas treatment biotechnology: a review. , 2001, Biotechnology advances.

[67]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[68]  Ronald Davis,et al.  Neural networks and deep learning , 2017 .

[69]  Moustafa Elshafei,et al.  RBF neural network inferential sensor for process emission monitoring , 2013 .

[70]  Manoj Kumar,et al.  An Introduction to Neural Network Methods for Differential Equations , 2015 .

[71]  Aggelos K. Katsaggelos,et al.  Deep fully-connected networks for video compressive sensing , 2016, Digit. Signal Process..

[72]  H. Chris Tseng,et al.  Modular neural networks with applications to pattern profiling problems , 2009, Neurocomputing.

[73]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[74]  Hiranmay Saha,et al.  Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform , 2015 .

[75]  D. Kendall,et al.  Odor Threshold Determinations of 53 Odorant Chemicals , 1969 .

[76]  Vincenzo Naddeo,et al.  Odour Impact Assessment Handbook , 2012 .

[77]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[78]  Tahseen Ahmed Jilani,et al.  A Comparison of First and Second Order Training Algorithms for Artificial Neural Networks , 2007, International Conference on Computational Intelligence.

[79]  Dongsheng Chen,et al.  A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling. , 2012, Journal of environmental management.

[80]  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.

[81]  Amirreza Talaiekhozani,et al.  An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems. , 2016, Journal of environmental management.

[82]  Robert A Kosiński,et al.  Artificial Neural Networks-Modern Systems for Safety Control. , 1998, International journal of occupational safety and ergonomics : JOSE.

[83]  Feilong Cao,et al.  Approximation by network operators with logistic activation functions , 2015, Appl. Math. Comput..

[84]  Wenjing Lu,et al.  Odor impact assessment of trace sulfur compounds from working faces of landfills in Beijing, China. , 2018, Journal of environmental management.

[85]  J. Kallas,et al.  Catalytic TiO2 oxidation of ethanethiol for environmentally begnin air pollution control of sulphur compounds , 2006 .

[86]  Loveneet Kaur,et al.  Applications of Artificial Neural Networks: A Review , 2016 .

[87]  Simon X. Yang,et al.  Pork farm odour modelling using multiple-component multiple-factor analysis and neural networks , 2005, Appl. Soft Comput..

[88]  Vincenzo Naddeo,et al.  A NOVEL TOOL FOR ESTIMATING THE ODOUR EMISSIONS OF COMPOSTING PLANTS IN AIR POLLUTION MANAGEMENT , 2009 .

[89]  E. Doğan,et al.  Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. , 2009, Journal of environmental management.

[90]  Zhigang Zeng,et al.  Complete stability of delayed recurrent neural networks with Gaussian activation functions , 2017, Neural Networks.

[91]  J. Jager,et al.  Odour monitoring by the combination of sensores and neural networks , 1999, Environ. Model. Softw..

[92]  Manoj Kumar,et al.  History of Neural Networks , 2015 .

[93]  Rajib Bandyopadhyay,et al.  Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview. , 2015, Talanta.

[94]  J E Haugen,et al.  Electronic nose and artificial neural network. , 1998, Meat science.

[95]  Ujjaini Sarkar,et al.  Community modelling: a tool for correlating estimates of exposure with perception of odour from municipal solid waste (MSW) landfills. , 2003, Journal of environmental management.

[96]  S. Chellam,et al.  Disinfection by-product formation following chlorination of drinking water: artificial neural network models and changes in speciation with treatment. , 2010, The Science of the total environment.

[97]  C. Kennes,et al.  Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks. , 2017, Journal of hazardous materials.

[98]  Fevzullah Temurtas,et al.  A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems , 2006 .

[99]  J. Namieśnik,et al.  Technologies for deodorization of malodorous gases , 2019, Environmental Science and Pollution Research.