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Raha, Debashis, A model for improving wastewater treatment plant performance from 'command & control' to managing in compliance with ESD principles, Doctor of Philosophy thesis, School of Civil, Mining and Environmental Engineering, University of Wollongong, 2006. http://ro.uow.edu.au/theses/1922
A MODEL FOR IMPROVING WASTEWATER TREATMENT PLANT PERFORMANCE FROM ‘COMMAND & CONTROL’ TO MANAGING IN COMPLIANCE WITH ESD PRINCIPLES
BSc Honours (Chemistry), BE Honours (Biochemical Engineering) (Gold Medallist), BE (Chemical Engineering)(AMIIChE), P.G.Diploma (Quality Management), MBA, ME (Chemical Engineering), ME Honours (Environmental Engineering), Master of Safety, Health & Environmental Management, Master of Environmental Law (Sydney).
I, Debashis Raha, declare that this thesis, submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy, in the School of Civil, Mining & Environmental Engineering, University of Wollongong, is wholly my own work unless otherwise referenced or acknowledged. The document has not been submitted for qualifications at any other academic institution.
Water is the basis of all life, human well-being and economic development. It links together all aspects of life on this planet- human and environmental health, food supply, energy and industry. Wastewater is one of the most important sources of surface and ground water pollution due to the chemical and biological pollutants that it contains and is the world’s greatest killer. Some 1.8 million child deaths each year as a result of diarrhoea, the loss of 443 million school days each year from water-related illness and close to 2.8 billion people in developing countries suffering at any given time from a health problem caused by wastewater. Proper wastewater collection and treatment, not only can avert these problems, but also can meet soaring demand- supply imbalances in fresh water through beneficial reuse of quality- effluent produced at the wastewater treatment plants (WWTPs). Every $1 spent in sanitation and wastewater treatment creates on average another $8 in costs averted and productivity gained in the developing world. In this study, endeavour is made to look for an approach to transform the operations and management of WWTPs from existing ‘command and control’ licensing to managing in compliance with ‘ecologically sustainable development (ESD)’ principle. This incremental improvement is described in three parts:
Part 1 deals with improving effluent quality by reducing concentration of total suspended solids (TSS) and oil & grease (O&G) using predictive artificial neural networks (ANN) models. It is demonstrated that WWTP ANN models are able to predict effluent TSS and O&G concentration, one to three days in advance, with fair degree of reliability for primary sedimentation, chemically assisted primary sedimentation, biological activated sludge and nutrient removal WWTPs, in spite of the very complex physical, chemical and biological processes involved in wastewater treatment. Part 2 explains how a regulatory and economic instrument such as ‘load- based- licensing (LBL)’ can be used to measure ecological and human health impact for 25 major harmful chemicals, present in WWTP effluent on the receiving water in terms of a performance indicator, called ‘pollution unit (PU)’. PU takes into account the level of technology, effectiveness of process and resource management and discharge load of 25 pollutants in WWTP effluent and their harmfulness to human health and ecology, and the sensitivity of the receiving water. PU also can be used to measure the environmental damage cost and licence administration cost in $ for pollution caused by WWTPs. Under ‘polluter pays principle (PPP)’, higher the number of PUs generated by a WWTP, the higher would be the pollution damage cost to be paid by the WWTP.
Praise and thanks to God, the Lord of the heavens and the earth, who has created and guided everything in the best possible way.
I would like to pay tribute to the sublime souls of all the prophets of God and all those believers who have sacrificed their lives, in the course of history, in defence of the humanity, the environment and high moral and ethical values, providing us with the opportunity to live, learn, and prosper. Without them, the forces of greed and injustice would have destroyed the earth. In memory of my late parents who were my best support, I would like to thank my exceptional wife, that I have been blessed with, and my loving family for their incredible encouragement, support and patience throughout the research work, especially for cheering me up in difficult times.
I would like to gratefully thank my supervisor Associate Professor (Dr) M. Sivakumar and Dr
Dharma Hagare, School of Civil, Mining & Environmental Engineering, University of Wollongong, for their continued help, guidance, motivation, encouragement, time and support at all stages of this work. It was my pleasure and one of my best lifetime experiences to know them personally and benefit from their in-depth knowledge. During these research years, their position grows from an academic supervisor to mentor, coach and well- wisher. Their constructive remarks, advice for improvements and continued quest for excellence, were of great help. Finally the author would like to thank the following individuals for their support and contributions to this research project:
Allan Laneila, Ex- Plant Manager, Sydney Water Corporation for sharing knowledge, information and his experience with the operations of wastewater treatment plants. Library staff at the University of Wollongong, Sydney Law School, University of Sydney and the University of Western Sydney for their help, guidance and support with database search and for providing me with books, journal and conference papers, web search and other research and thesis documents, as and when, needed from libraries and institutions across Australia and overseas.
The following is a list of publications related to the research presented in this thesis:
1. Raha, D. and Sivakumar, M., 2006a, Evolution of Ecologically Sustainable Development (ESD) in NSW and Australia, Waste Disposal and Water Management in Australia (accepted).
2. Raha, D., 2006b, Myths around Water- Property Rights, Human Rights or Environmental Rights, The Environmental Engineers, Journal of the Society for Sustainability and Environmental Engineering, Vol. 7, No. 4, Summer.
3. Raha, D. and Sivakumar, M., 2006c, Prediction of Effluent Oil and Grease (O&G) from Coastal Primary Sewage Treatment Plants to Minimise Beach Pollution, The Environmental Engineers, Journal of the Society for Sustainability and Environmental Engineering, Institution of Engineers, Australia (accepted).
4. Raha, D., 2006d, Environmental impact study for Kurnell Desalination Plant, The Water, Journal of the Australian Water Association (AWA), December.
5. Raha, D., 2006f, Paradigm shift in protecting the water environment in NSW, Australian Journal of Water Resources, Institution of Engineers, Australia (accepted).
6. Raha, D., 2006i, Comparative Study of Artificial Network Modelling in Predicting Effluent Oil and Grease (O&G) from Coastal Primary and Chemically Assisted Primary Sewage Treatment Plants, Environmental Forensics, USA (submitted).
7. Raha, D., 2006j, Explor ing Artificial Intelligence for Modelling a Biological Nutrient Removal (BNR) Sewage Treatment Plant (STP) for Forecasting Effluent Suspended Solids, The Indian Chemical Engineer, Indian Institute of Chemical Engineers (submitted).
8. Raha, D., and Sivakumar, M., 2006m, A Comparative Assessment of Performance of Artificial Intelligence Models in Forecasting Performance of an Activated Sludge and a Biological Nutrient Removal Sewage Treatment Plant, Waste Disposal and Water Management in Australia, April 2006, Vol. 33, No. 1.
9. Raha, D., and Sivakumar, M., 2006n, Artificial Intelligence Approach to Modelling a Biological Wastewater Treatment Plant for Forecasting Effluent Suspended Solids, The Environmental Engineers, Journal of the Society for Sustainability and Environmental Engineering, Vol 7 No 1 Autumn 2006, Engineers Australia.
10. Raha, D, 2005b, Sustainable Management of Wastewater Treatment Plant Using ISO14001: 2004 EMS and Neural Network Modelling, The Indian Chemical Engineer, Indian Institute of Chemical Engineers, Vol. 47, No. 3, July- Sept.
11. Raha, D., and Dharmappa, D., 2004, Predicting Effluent Oil and Grease (O&G) for a Primary Sewage Treatment Plant Using Artificial Neural Networks (ANN), Indian Chemical Engineer, Indian Institute of Chemical Engineers, Vol. 46, No. 2, Apr- June.
12. Raha, D., and Dharmappa, D., 2003a, Application of Artificial Neural Networks (ANN) to Forecast Effluent Suspended Solids from Coastal Primary Sewage Treatment Plant, The Environmental Engineers, Journal of the Society of Environmental Engineering, Engineers Australia, Vol. 4, No. 4, Summer.
13. Raha, D., 2003b, Environment and Economic Instrument for Protecting the Water Environment in India, The Indian Chemical Engineer, Indian Institute of Chemical Engineers, Vol. 45, No. 4, Oct- Dec.
14. Raha, D., 2003c, Impact of pollutants detection limit and frequency of analysis on environmental discharge load, The Environmental Engineers, Journal of the Society of Environmental Engineering, Engineers Australia, Vol. 4, No. 2, Winter.
15. Raha, D., 2003d, Polluter Pays through Load Based Licensing: The Impact for a Coastal Sewage Treatment Plant, Chartered Institute of Water & Environmental Management
(CIWEM), UK, Vol. 18, No. 4, November 2004, pp. 222- 225.
16. Raha, D., 2003e, National Pollutant Inventory Report: A Water Utility’s Experience, Waste Disposal and Water Management in Australia, Vol. 30, No. 2, May.
17. Raha, D., 2003f, Application of Artificial Intelligence to Monitor and Control Sewage Treatment Plant and Minimise Water Pollution, The Journal of Environmental Engineering, Indian Institution of Engineers. Winner of two medals: (i) The Nawab Zain Yar Jung Bahadur Memorial Prize; and (ii) The Rekha Nandi and Bhupesh Nandi Prize of The Institution of Engineers (India) at the Indian Engineering Congress 2006, Guwahati (India)
18. Raha, D., 2002b, Load Based Licensing- A practical example, The Environmental Engineers, Journal of the Society of Environmental Engineering, Engineers Australia, Vol.
3, No. 1, Autumn.
19. Raha, D., 2002c, Process Management of National Pollutant Inventory (NPI) Reporting by
a Water Utility, The Environmental Engineers, Journal of the Society of Environmental Engineering, Engineers Australia, Vol. 3, No. 4, Summer.
1. Raha, D. and Sivakumar, M., 2006g, Performance of Artificial Intelligence Models in Forecasting Effluent Total Suspended Solids for Primary, Chemically Assisted Primary, Activated Sludge and Advanced Biological Nutrient Removal Sewage Treatment Plants, Indian Chemical Engineering Congress ChemCon 2006, Ankleswar, Gujarat, India, 27-30 Dec.
2. Raha, D., Sivakumar, M., and Raha, M.C., 2006h, Measuring Water Pollution from Chemical industries in Economic and Environmental Terms, Indian Chemical Engineering Congress ChemCon 2006, Ankleswar, Gujarat, India, 27-30 Dec.
3. Raha, D., 2005a, Engineering and Management Approach To Sustainable Wastewater Treatment, National Environmental Engineering Conference, Sydney, Australia.
4. Raha, D. and Dharmappa, D., 2003j, Predicting the performance of primary sewage treatment plant using artificial neural network (ANN), Proceedings of the Second National Environmental Research Conference, Marysville, Melbourne, Australia.
5. Raha, D., and Dharmappa, D., 1998, Use of artificial neural network (ANN) for predicting the performance of the activated sludge process, Proceedings of the Second International Environmental Management Conference, Wollongong, Australia.
6. Raha, D., 2003h, An Integrated Approach to Measuring Environmental Performance of Water Utilities: A Case Study at Sydney Water Corporation, Proceedings of the Envirotech 2004, Sydney, Australia.
7. Raha, D., 2003i, Load- Based- Licensing (LBL) in protecting and restoring the water environment, Proceedings of the First International Eco- restoration Conference, New Delhi, India.
8. Raha, D., 2003j, Implementing polluter pays through Load- Based- Licensing (LBL) in water utilities, Proceedings of the OZWater & OZWaste Conference, Perth, Australia.
1.2.1 ANN modelling of WWTPs for improving effluent quality
1.2.2 ESD and PPP to minimise environmental impacts from WWTP s
1.2.3 Integrated management systems to manage WWTPs in compliance with ESD
1.4 Scope and structure of thesis
Chapter 2: Artificial Neural Networks
2.2 Biological or Natural neural networks
2.3.1 Brief historical overview of ANN development
2.3.2 ANN and other forms of computing
2.4 Back-propagation networks (BPN)
2.5 Operation of ANN
Learning or Training by ANN
Elements of an ANN Model
2.6.1 Weights (w ji )
2.6.2 Threshold (q j )
2.6.3 Transfer function (f i )
2.6.4 Error function
2.6.5 Learning coefficient (LCoef)
2.6.6 Momentum
2.6.7 Learning speed
2.6.8 Epoch size
Rationale for choosing ANN for wastewater treatment modelling
Constraints with ANN modelling
Chapter 3: Review of Literature on ANN in Wastewater Treatment. 41
3.2 Review of previous research on ANN modelling in wastewater treatment
3.2.1 ANN models for pilot laboratory- scale wastewater treatment
3.2.2 Application of ANN in activated sludge process WWTPs
3.2.3 Application of ANN for modeling wastewater treatment in lagoons
2. 2.4 Application of ANN in wastewater treatment plant with on- line sensors
Application of ANN in primary sedimentation wastewater treatment
Application of ANN in BNR WWTP
3.2.7 Application of ANN in flow prediction for WWTP
3.2.8 ANN predicting beach water quality during storm event
3.2.9 ANN model in ultrafiltration of wastewater effluent
3.2.10 ANN model predicting sludge bulking in wastewater treatment
3.2.11 ANN modelling in miscellaneous wastewater treatment processes
2.2.12 Comparative performance of ANN and other wastewater modelling
Methodology for ANN model development
4.2 Wastewater treatment plant (WWTP) modelled by ANN
4.2.1 Northern WWTP
4.2.2 Central WWTP
4.2.3 Southern WWTP
4.2.4 Western
Summary of treatment processes at WWTPs
Environmental protection legislation regulating WWTPs
Important process variables for WWTP modelling
Sampling frequency and analytical methods
4.9.1 Northern WWTP
4.9.2 Central WWTP
4.9.3 Southern WWTP
4.9.4 Western WWTP
General observation from data analysis for WWTPs
Chapter 5: ANN Model Development for WWTP
5.3 ANN model design process
5.3.1 Source data analysis
5.3.2 Data pre- processing
5.3.3 Time series plot
5.3.4 Scaling/ normalisation
5.3.5 Determination of model input parameters
5.3.6 Choice of time- lags for input variables
5.3.7 Training and test set
5.3.8 ANN geometry and topology
5.3.9 No of layers in ANN
5.4.1 Performance criteria of ANN models
5.4.2 Sensitivity analysis
5.4.3 Verification for right input parameters in ANN model
5.4.4 Updating ANN model with new data
ANN model parameters for WWTPs
6.2 Methodology for WWTP ANN model building
Illustration for WWTP ANN model building methodology
Predicting effluent O&G for primary and CAPS WW TP
6.3.2 Impact of O&G on beach and bathing water
6.3.3 ANN models for PS and CAPS WWTP for forecasting effluent O&G
6.3.4 Results and discussion
6.3.5 Process control strategy when model predicts higher effluent O&G
ANN models predicting effluent TSS for PS, CAPS, ASP and BNR WWTP
6.4.1 ANN application in wastewater treatment plant for effluent TSS prediction
6.4.2 WWTP ANN model for effluent TSS prediction
ANN models predicting Northern WWTP effluent TSS
6.5.1 ANN model predicting NWWTP effluent TSS one-day in advance
6.5.2 NWWTP ANN models predicting effluent TSS two-days in advance
6.5.3 NWWTP ANN models predicting effluent TSS three-days in advance
6.5.4 Sensitivity analysis for NWWTP ANN models predicting effluent TSS
6.5.5 Process control strategy to avoid higher NWWTP effluent TSS
ANN Models for Central WWTP predicting effluent TSS
6.6.1 CWWTP ANN models predicting effluent TSS one-day in advance
6.6.2 CWWTP ANN models predicting effluent TSS two-days in advance
6.6.3 ANN models for predicting effluent TSS three days in advance for the
Sensitivity analysis for CWWTP ANN models predicting effluent TSS
Process control strategy to avoid higher effluent TSS from CWWTP
6.7. ANN Models for predicting Southern WWTP effluent TSS
SWWTP ANN models predicting effluent TSS one-day in advance
SWWTP ANN models predicting effluent TSS two-days in advance
SWWTP ANN models predicting effluent TSS three-days in advance
Sensitivity analysis for SWWTP ANN models predicting effluent TSS
Process control strategy to avoid higher SWWTP effluent TSS
Western WWTP ANN models predicting effluent TSS
WWWTP ANN models predicting effluent TSS one-day in advance
WWWTP ANN models predicting effluent TSS two-days in advance
WWWTP ANN models predicting effluent TSS three-days in advance
Sensitivity analysis for WWWTP ANN models predicting effluent TSS
Process control strategy to avoid higher effluent TSS from WWWTP
Chapter 7: Evolution of ESD in NSW and Australia
7.2 Brief history of ESD in Australia and globally
7.3 ESD in the NSW and in Commonwealth legislation
7.4 Judicial hierarchy and decision- making in NSW and Australia
7.4.1 Doctrine of precedence in environmental judgement
7.4.2 Hierarchy of court in Australia on environmental jurisdiction
7.4.3 The ‘doctrine of precedence’ or principle of ‘stare decisis’
7.4.4 Influence of international environmental law on ESD in Australia
Important judicial decisions on ESD
7.6 Environmental legislations to complement and support ESD
7.7 Non- State actors in promoting ESD in NSW
Chapter 8: Measuring Pollution from of WWTPs in Environmental and Economic Terms
8.2 Demerits of discharge concentration-based licensing
8.3 Merits of load-based licensing (LBL)
8.4 Mitigating environmental impact with LBL (PPP)
8.5 Important elements in LBL
8.5.2 Assessable Load of Pollutants
8.5.3 Bubble licensing
8.5.4 Emissions trading scheme:
8.5.5 Annual load limit
8.5.6 Load calculation protocol
8.5.7 Harmfulness of pollutants
8.5.8 Sensitivity of receiving water environment
8.5.9 Fee rate threshold
8.6 Application of Polluter Pays Principle (LBL) to WWTPs
8.7 Mechanism in LBL to deter rogue WWTP Operators
8.8 Measuring environmental impact from WWTP in NPU
Measuring environmental impact from WWTP in $ terms
8.9 Pollution load fees (PLF)
8.11 Limitations of LBL
Chapter 9: Integrated Management Systems for Managing Wastewater Treatment Plant
Benefits from ISO14001 EMS implementation
Progressing beyond ISO14001 EMS in managing WWTP
Sustainable development principle and the IMS
9.5 Benefits of integration of quality, environment and health & safety
9.6 Integrating quality, environment and health & safety management systems
9.7 Issues and limitations in integrating quality, environment and safety
9.8 Guidance for integrating quality, environmental and OHS management systems
9.9 IMS documentation
9.10 Performance and ESD Indicators
10.2 ANN Modelling
10.3 ESD and Polluter Pays Principle (PPP)
10.4 Integrated management systems (IMS) for ESD
10.5 Recommendations for future
ANN modelling for WWTP
10.5.2 ESD and Polluter Pays Principle for WWTPs
10.5.3 ESD implementation in WWTP through IMS
10.6 Future challenges in ESD implementation in WWTPs
Appendix A: Flow, rainfall and influent and effluent quality
Figure 1.1 An Overview of the Research Process, Thesis Structure and Publications
Figure 2.1 Typical structure of a Biological Neuron
Figure 2.2 Classification of Artificial Neural Network
Figure 2.3 Structure of a three- layer feed forward back-propagation network
Figure 2.4 Data flow in back-propagation training phase of ANN
Figure 2.5 A Typical error surface/ curve/ trajectories in training
Figure 2.6 Transfer functions
Figure 4.1 Process flow diagram of Northern WWTP
Figure 4.3 Process flow diagram of SWWTP
Figure 4.4. Process flow diagram of the Western WWTP
Figure 5.1 Back- propagation training process
Figure 5.2 ANN model development process
Figure 6.1 Significant input parameters for O&G two days in adva nce
Figure 6.2 Significant input parameters for O&G two days in advance
Figure 6.3 Observed Vs. Predicted effluent O&G (mg/L) two days in advance
Figure 6.4 Three- layer feed- forward (TLFF) ANN model for NWWTP and CWWTP
predicting O&G
Figure 6.5 Observed & one-day advance predicted NWWTP effluent O&G (mg/L)
(Model 050903f 1 )
Figure 6.6 Observed & predicted two-days advance NWWTP effluent O&G (mg/L)
(Model 050903h 2 )
Figure 6.7 Observed & predicted NWWTP effluent O&G (mg/L) three days in advance
(Model 050903L 3 )
Figure 6.8 Significant input parameters predicting NWWTP effluent O&G
Figure 6.9 Observed & one-day advance predicted CWWTP effluent
(Model 171103O3 4 )
Figure 6.10 Observed & two-days advance predicted CWWTP effluent O&G
Figure 6.11 Observed & three-days advance predicted CWWTP effluent O&G
Figure 6.12 Significant input parameters predicting CWWTP effluent O&G
Figure 6.13 Conceptual SCADA & IICATS control applying NWWTP ANN model
Figure 6.14 Process control sequence for high level alarm for NWWTP effluent O&G 129
Figure 6.15 Conceptual SCADA & IICATS control applying CWWTP ANN model
Figure 6.16 Process control sequence for high level alarm for CWWTP effluent O&G 131
Figure 6.17 TLFF back-propagation ANN model predicting WWTP effluent TSS
Figure 6.18: Observed & one-day advance predicted NWWTP effluent TSS
Figure 6.19 Observed & two-days advance predicted NWWTP effluent TSS
Figure 6.20 Observed & three-days advance predicted NWWTP effluent TSS
Figure 6.21 Significant parameters predicting NWWTP effluent TSS
Figure 6.22 Conceptual SCADA & IICATS control applying NWWTP ANN model
Figure 6.23 Process control sequence for High Level Alarm for NWWWTP effluent
Figure 6.24 Observed vs. predicted CWWTP effluent TSS one-day in advance
Figure 6.25 Observed vs. predicted CWWTP effluent TSS two-days in advance
Figure 6.26 Observed vs. predicted CWWTP effluent TSS three-days in advance
Figure 6.27 Relative significance of parameters predicting CWWTP effluent TSS
Figure 6.28 Conceptual SCADA & IICATS control applying CWWTP ANN model
Figure 6.29 Process control sequence for High Level Alarm for CWWTP effluent TSS147
Figure 6.30 Observed vs. predicted SWWTP effluent TSS one-day in advance
Figure 6.31 Observed vs. predicted SWWTP effluent TSS two-days in advance
Figure 6.32 Observed vs. predicted SWWTP effluent TSS three-days in advance
Figure 6.33 Relative significance of parameters predicting SWWTP effluent TSS
Figure 6.34 Conceptual SCADA & IICATS control applying SWWTP ANN model
Figure 6.35 Process control sequence for High Level Alarm for SWWTP effluent TSS152
Figure 6.36 Observed vs. predicted WWWTP effluent TSS one-day in advance
Figure 6.37 Observed vs. predicted WWWTP effluent TSS two-days in advance
Figure 6.38 Observed vs. predicted WWWTP effluent TSS three-days in advance
Figure 6.39 Relative significance of parameters predicting WWWTP effluent TSS
Figure 6.40 Conceptual SCADA & IICATS control applying WWWTP ANN model 156
Figure 6.41 Sequence of process control actions for High Level Alarm for effluent TSS
for the WWWTP
Figure 7.1 An Overview of the Court hierarchies in NSW and Australia
Figure 8.1 Relationships between pollutant loads and fees
Figure 8.4 An Overview of the process of determining PU and the cost of pollution
Figure 8.5 Process flow chart for calculating NPU and Pollution Fees
Figure 8.6 Environmental impact (NPU) per 1000 ML effluent
Figure 8.7 Combined Administration (AF) and Load Fee (LBF)
Figure 8.8 Pollution Load Fees (PLF) for WWTPs
Figure 9.1 ISO14001: 2004 EMS Model
Figure 9.2 Integrated Quality- Environmental- OHS Management System (IMS)
Figure 9.3 P-D-C-A Cycle of the EMS or IMS
Figure 9.4 IMS process based on P-D-C-A cycle
Figure 9.5 Certified IMS as a tool for continual improvement
Figure 9.5 List of legislations applicable to a major water utility in NSW
Figure A1 Inflow to Northern WWTP
Figure A2 Rainfall in the Northern WWTP Catchments
Figure A3 Influent wastewater TSS concentration for Northern WWTP
Figure A4 Influent wastewater O&G concentration for Northern WWTP
Figure A5 Effluent TSS concentration for Northern WWTP
Figure A6 Effluent O&G concentration for Northern WWTP
Figure A7 Wastewater inflow to Central WWTP
Figure A8 Rainfall in the Central WWTP Catchments
Figure A9 Influent Wastewater TSS concentration for Central WWTP
Figure A10 Effluent TSS concentration for Central WWTP
Figure A11 Effluent O&G concentration for Central WWTP
Figure A12 Wastewater inflow to Southern WWTP
Figure A13 Rainfall in the Southern WWTP Catchments
Figure A14 Influent TSS concentration for Southern WWTP
Figure A15 Effluent TSS concentration for Southern WWTP
Figure A16 Wastewater inflow to WWWTP
Figure A17 Rainfall in the Western WWTP catchment
Figure A18 Effluent TSS from Western WWTP
Table 1.1 Wastewater treatment unit processes at the WWTPs
Table 1.2 Monitored and measured process parameters for WWTPs
Table 4.1 Summary of constituent unit processes at the WWTPs
Table 4.2 Typical Licence Discharge Limits for TSS and O&G at the WWTPs
Table 4.3 Sampling and analytical methods for WWTP process variables
Table 4.4 Summary of training and testing data collection period
Table 4.5 Summaries of Flow, Rainfall and Effluent TSS and O&G for Northern WWTP
Table 4.6 Summaries of Flow, Rainfall and Effluent TSS and O&G for Central WWTP
Note: * Licence limit exceedance
Table 4.7 Summaries of Flow, Rainfall and Effluent TSS for Southern WWTP
Table 4.8 Summaries of Flow, Rainfall and Effluent TSS for WWWTP
Table 4.9 Regulatory performances of NWWTP, CWWTP, SWWTP and WWWTP
Table 5.1 ANN model parameters chosen in the study
Table 6.1 Illustrative example of generic ANN model development procedure
Table 6.2 ANN model architecture for NWWTP and CWWTP predicting O&G
Table 6.3 Comparative performance of ANN models for effluent O&G
Table 6.4 ANN models predicting NWWTP effluent O&G one- day in advance
Table 6.5 ANN models predicting NWWTP effluent O&G two days in advance
Table 6.6 ANN models for predicting NWWTP effluent O&G three-days in advance . 125
Table 6.7 ANN Models predicting CWWTP effluent O&G one day in advance
Table 6.8 ANN models predicting CWWTP effluent O&G two days in advance
Table 6.9 ANN models predicting CWWTP effluent O&G three days in advance
Table 6.10 ANN model architecture for PS, CAPS, ASP and BNR WWTP
Table 6.11 Effluent TSS prediction performance of WWTP ANN models
Table 6.12 Better performing NWWTP ANN models predicting effluent TSS one day in
Table 6.13 Better performing NWWTP ANN models predicting effluent TSS two-days
Table 6.14 Better performing NWWTP ANN models predicting effluent TSS three-days
Table 6.15 Better performing CWWTP ANN Models predicting effluent TSS one-day
Table 6.16 CWWTP ANN models predicting effluent TSS two-days in advance
Table 6.17 CWWTP ANN models predicting effluent TSS three days in advance
Table 6.18 SWWTP ANN models predicting effluent TSS one-day in advance
Table 6.19 SWWTP ANN models predicting effluent TSS two-days in advance
Table 6.20 SWWTP ANN models predicting effluent TSS three days in advance
Table 6.21 WWWTP ANN models predicting effluent TSS one-day in advance
Table 6.22 WWWTP ANN models predicting effluent TSS two-days in advance
Table 6.23 WWWTP ANN models predicting effluent TSS three days in advance
Table 7.1 European Union’s General Principles on ESD
Table 7.2 ESD in the key NSW and Commonwealth environmental legislation
Table 7.2 International treaties and relevant Australian Commonwealth legislation on
Table 7.3 Important judicial decisions promoting ESD in NSW and Australia
Table 8.1 Summary of legislative and environmental management aspect of LBL
Table 8.2 Assessable LBL pollutants and their environmental & health impacts
Table 8.3 Analytical detection limits for pollutants for WWTPs
Table 8.4 Minimum frequency of analysis for pollutants for WWTPs
Table 8.5 Pollutant weightings
for assessable pollutants
(POEO General Regulation
Table 8.6 Discharges from WWTPs into classes of waters
Table 8.7 Fee rate threshold (FRT) for assessable pollutants for WWTPs
Table 8.8 Concentrations of Assessable Pollutants in Effluent from WWTPs
Table 8.9a Load of Assessable Pollutants in Effluent from WWTPs
Table 8.9b Load of Assessable Pollutants in Effluent from WWTPs
Table 8.10 Minimum environmental impact (NPU) from discharge of one tonne of
Table 8.11a Environmental impact (NPU) from discharge of pollutants in efflue nt
Table 8.11b Environmental impact (NPU) from discharge of pollutants in effluent
Table 9.1 Key EMS benefits
Table 9.2 Benefits of certifying to ISO14001EMS
Table 9. 3 An overview of integration of Quality, Environment and Health & Safety
(HS) Management Systems
Figure 9.4. Hierarchy of documentation in IMS
Table 9.4a List of Manuals, UPGs, SAPs and SOPs in the WWTP IMS
Table 9.4b List of SAPs and SOPs for WWTP IMS
Table 9.4c List of SAPs and SOPs for WWTP IMS
Table 9.5 ESD Indicator for a wastewater treatment plant
Table 9.6 Performance Indicator for a wastewater treatment plant
Average absolute percent error (a performance measure for ANN model)
Administrative Decisions and Judicial Review
Administrative fee units (for LBL)
Australian & New Zealand Environment & Conservation Council
Agriculture & Resource Management Council of Australia & New Zealand
Back- propagation networks, a type of ANN architecture
Where a number of discharge sources are nominally grouped, with
discharge limits applying to the group as a whole rather than individually. Command and control: a form of environmental protection licensing for
WWTPs Chemically assisted primary sedimentation
Critical zoning factor (sensitivity of receiving waters)
Central wastewater treatment plant (a CAPS WWTP)
Data acquisition and control systems (a form of instrumentation and
control) Department of Environment and Conservation, NSW
Discharge load (kg/ yr)
Environmental Planning and Biodiversity Conservation Act (Commonwealth of Australia ) 1999
Fee rate threshold (used in LBL)
Giga litres
Intermittently decanted aerated lagoon
Intergovernmental Agreement on the Environment (Australia)
IICATS
Integrated instrumentation control and telemetry systems (a form of remote
process instrumentation and control) International Law Association
Integrated management systems (e.g. quality, environment and OHS
management systems combined together) Where polluters are required to include pollution costs caused by their
discharges in their normal financial operations and decision- making. International Organisation for Standardisation
Market- based- instrument, an economics tool in environmental protection
NO x — N Non-point source
Nitrate- Nitrogen and Nitrite- Nitrogen combined concentration Discharges arising from multiple activities over a broad area, for example, storm water run-off, irrigation run-off.
Normalised cumulative delta (a learning rule used in ANN modelling)
Nephelometric Turbidity Unit, a unit for measuring turbidity of water
New South Wales, a state in Australia
Northern wastewater treatment plant (a PS WWTP)
Office of Water, UK
Oxidation- reduction potential
Plan-Do-Check-Act, a tool in management systems
Processing element in ANN
Pollutant fee unit (for LBL or PPP)
Pollutant load fees (for LBL or PPP)
Protection of the Environment Operations (POEO) Act 1997 (NSW)
Individual place of discharge, such as a discharge pipe
Primary Sedimentation (gravity assisted)
Pollution units (relating LBL or PPP)
Pollutant Weightings (relating LBL or PPP)
Return activated sludge suspended solids
Return activated sludge volatile suspended solids
Real- time control
Supervisory control and data acquisition systems (a form of process
instrumentation and control) Sustainable development
Standard Incident Management Procedure
Simplified process model
Solids Retention Time (same as MCRT and SA)
Settled sewage or primary effluent
Settled sludge volume, mL/L
Sewage Treatment Plant (applied synonymously with WWTP)
Southern wastewater treatment plant (an ASP WWTP)
Unit process guidelines
Western Wastewater treatment plant, a BNR WWTP
Water is the basis of all life, human well-being and economic development. It links together all aspects of life on this planet- human and environmental health, food supply, energy and industry. Although water is the most widely occurring substance on earth, only 0.65 % available as freshwater (ground water- 0.62% and surface- 0.03%), 2.15% is locked up in glaciers and permanent snow cover, while the remainder 97.2% is salt (ocean) water (Aplin, G., 1998). In the 20 th century, population has increased by a factor of about three, whereas water withdrawals have increased by a factor of about seven. Due to ever increasing industrialisation, urbanisation and other anthropogenic activities, all aquatic resources including rivers, lakes, wetlands, coastal waters and groundwater, are shrinking in their extent and the water quality is getting highly degraded. For example, around 2 million tonnes of waste is disposed into receiving waters daily (Aplin, G., 1998) across the world. Dirty water is both the world’s greatest killer and biggest single pollution problem. Currently, one- third of the world’s population are in medium to high water stress and this ratio is expected to grow by more than half by 2025 (UNESCO, 2003). Human induced climate change due to global warming primarily because of accumulation of ‘green house gases (GHG)’ resulting from combustion of fossil fuels, large-scale deforestation and the rapid expansion of irrigated agriculture, which has been estimated to raise the mean surface temperature by 2.5 0 C and sea level by 0.5 m by 2100. Its estimated impacts are to increase the number of deaths due to greater frequency and severity of heat waves, severe drought and floods and spread of infectious diseases due to alteration in life cycle dynamics of vectors and infectious parasites (WHO, 1997). The problems of pollution, population growth and climate change, which are causing tremendous changes in the quality and quantity of available freshwater, will radically affect how we live our lives and will shape our future. The quality of water is critical to the health of the environment, people and the economy and has major bearing on human activities, including domestic, irrigation and industrial water supply, commercial fishing, recreation and tourism. For humanity, ‘water crisis’ is both a symptom and a cause of poverty among a large percentage of the world’s population and is the one that lies at the heart of our survival and that of our planet Earth. Humankind has entered a crucial moment in time with respect to water; no longer a resource that we can take for granted, water has become a key global challenge, the resource that best exemplifies many of the earth's global imbalances and defines the terms of sustainable development. The Mar del Plata conference (1977), the Rio and Dublin conference (1992), the
Millennium Development Goals (MDG) in 2000, the Hague Ministerial Declaration of 2000, the Dublin + 10 conference in Bonn (2001), Rio + 10 in Johannesburg (2002) and the 3 rd World Water Forum in Kyoto (2003) have all recognised and declared that fresh water (often referred as ‘blue gold’) is a finite and vulnerable resource, essential to sustain life, development and the environment. The United Nations’ has declared 2005- 14 as the ‘Decade for Action- Water for Life’, a decade that emphasizes the central role that water plays in sustainable development. Australia is the driest continent on earth in terms of total precipitation and runoff, and both aspects vary widely in spatial and temporal dimensions. One- quarter of the continent contributes to about four- fifths of the total runoff and about 85% of the meager precipitation is lost in evapotranspiration (Aplin, G., 1998) and for the bulk of the continent, evaporation exceeds rainfall on a yearly basis (Nevill et. al., 2005). Most Australian river systems are currently over- allocated, 40% show clear sign of degradation such as hosting the world’s longest algal bloom in 1991 (Poh- Ling, 2005) and it is estimated that within 20 years, drinking water in Adelaide will fall below WHO salinity standards in two days out of five (Lyster, 2005). There has been an exponential increase in wastes and emissions from anthropogenic activities due to increase in population, changing life style and consumption pattern of goods and services, and the resulting environmental problems have gained global character, e.g. global warming and discharge of municipal and hazardous wastes into the natural receiving environments at levels far above carrying capacities of earth. Wastewater is one of the most important sources of surface and ground water pollution and poor sanitary condition around the world due to the chemical and biological pollutants that it contains. Water pollution imposes a range of costs on the community. Examples include increased morbidity and mortality, reduced quality of life, direct health expenditures, lost earnings, reduced productivity for a range of industries, reduced recreational opportunities, preventive expenditures, reduced biodiversity and ecological damage. Poor sanitation in the developing world and effluent discharges and sewage overflows from wastewater treatment plants (WWTP) and sewerage reticulation networks in the developed world are the major source of water pollution. In recent decades, there is an ever- increasing awareness in the society about the ecology, environmental protection and sustainable development issues. In this thesis, the transformation of WWTPs from existing ‘command and control’ licensing to managing in compliance with the ESD, has been discussed in three parts and they are:
Part 1 Artificial Neural Networks (ANN) Modeling- to improve the concentration of most common pollutants, that is, total suspended solids (TSS) and oil & grease (O&G) in effluent from WWTPs using ANN models under ‘command and control licensing’; Part 2 ESD and PPP: (a) to explore the history of evolution of ecologically sustainable development (ESD) principles in NSW and Australia; and (b) to improve the effluent quality
and to minimise the environmental and human health impacts from effluent discharges from WWTPs using ‘polluter pays principle (PPP)’ under ‘load- based- licensing’; Part 3 Integrated Management Systems (IMS)- to develop a framework to integrate ISO9001 quality (QMS), ISO14000 environmental (EMS) and AS4801 health and safety (HS) management systems at the WWTPs to deliver improved environmental, economic and social performance (triple bottom line) in compliance with the ESD principles.
Until recently, the “end- of- pipe” (i.e. treatment and disposal of pollutants after they are produced) approach has been the main strategy for managing environmental problems. There are numerous wastewater treatment technologies and the most predominant ones that cover the bulk of the ‘end- of- pipe’ wastewater treatment across the world, range from simple primary sedimentation (PS) to more complex chemically assisted primary sedimentation (CAPS), to advanced biological activated sludge process (ASP ) and finally the most advanced tertiary biological nutrient removal (BNR) treatment technology. In order to meet the soaring demand- supply imbalances for watery, water conservation, demand management and beneficial recycle and reuse of effluent from wastewater treatment plants (WWTPs) are the most promising options. The existing environmental protection licensing (EPL) system for wastewater treatment plants around the world are based on the ‘command and control technique’, focussing primarily on controlling the concentration of pollutants in discharges applying ‘emission standard’ and ‘technology standard’. Moreover, pollution of beaches and loss of recreational water amenity by effluent from coastal wastewater treatment plants (WWTP) has been a problem worldwide. Effluent total suspended solids (TSS) and oil and grease (O&G) of sewage origin have been identified as major components causing beach pollution, both in Sydney and elsewhere. TSS has been defined as solid materials that are retained on 2 µm nominal pore size glass- fiber filter paper, which has been subsequently dried at a constant temperature of 103- 105 0 C. O&G is defined as any material recovered from sewage or effluent that is soluble in a mixture of 80% n-hexane and 20% methyl- tert-butyl- ether. O&G forms surface slicks and thus reduces sunlight penetration and surface reaeration of water, making water aesthetically unattractive and affects the marine ecosystem. TSS and O&G are: (i) the most common environmental, public health and regulatory performance indicators for WWTPs worldwide; and (ii) surrogate indicators of biochemical oxygen demand (BOD), chemical oxygen demand (COD), heavy metals and fat- soluble harmful organics such as pesticides and PCBs, and microbiological water quality e.g.
faecal coliform and streptococci count of effluent. Therefore, in the first part of this thesis, research effort is directed towards improving the TSS and O&G concentration in the effluent from WWTPs to cater for: (i) complying with the concentration limits for TSS and O&G in the WWTP licence, regulated by the environmental protection authority (EPA); (ii) facilitate effluent recycle and reuse through improvement in effluent quality; (iii) improve recreational water qualities in beaches and streams; and (iv) reducing pollution fees for WWTP under the ‘polluter pays’ based load- based- licensing (LBL) in NSW, where WWTP is to pay an amount of pollution fees which could be as high as A$ 518 per tonne of O&G and A$ 273 per tonne of TSS discharged into the receiving waters (Raha, 2006f). Therefore, from the environmental, human health and economic perspectives, minimising the discharge of effluent TSS and O&G concentration from WWTP is considered the best option. Wastewater treatment processes, consisting of a sequence of complex physical, chemical and biochemical processes, and their dynamics are non- linear and usually time- varying. This is , due to a number factors, for example, the influent wastewater flow and composition follow dynamic patterns that are related to watershed and service area characteristics such as city scale, life style and demography, and local hydrologic and meteorological conditions. As a result, in waste water treatment, each process is governed by complex non- linear relationships between numerous physical, chemical, biological and operational parameters. Other problems associated with WWTPs are: (i) hydraulic and pollutants load variations constitute major portion of the operating life of a WWTP and most of the observed non- compliance with the environmental protection regulations are due to these load transients; (ii) chemical and biological analysis of some of the pollutants in the effluent, such as BOD, can take as much as 5 days; and (iii) lack of reliable on-line sensors, their fouling, maintenance and calibration. Effective control of the dynamic behaviour of a unit process, or of the entire treatment plant, depends on three factors:
(i) the ability to observe the state of the process and its response to various perturbations (i.e. monitoring); (ii) the ability to relate causes (inputs and controls) to effects (outputs, responses); and (iii) the capacity to act by manipulating the causes (control inputs) to correct undesirable effects or bring about more desirable effects. With these objectives, there has been a shift of focus in wastewater treatment from plant design and construction to plant operation, process control and operational optimisation in recent times, and modelling of WWTP has received considerable attention lately. In recent times, various approaches to model WWTPs such as statistical methods, knowledge- based system (KBS), conventional mechanistic models, mathematical kinetic models and expert systems (ES) have been tried, but with very limited success. The statistical techniques are limited in that they always require the assumption of a certain functional form for relating dependent variables to independent variables. When the assumption of the functional form is incorrect, the model is flawed. The most widely used statistical model
in water and wastewater modelling, is the ARMA (autoregressive moving average) model, which requires the data to be stationary and to follow normal distribution. The KBS approach requires a thorough understanding of physical, chemical and biological factors in sewage treatment and their interactions and this is a very difficult, if not impossible task. ES consists of a set of rules, defined by a domain expert, that are linked with the historic database of the treatment system, are usually in the form of <IF- THEN-ELSE> statements, rather than expressed by general mathematical relationships. Shortcoming of this approach is that the ES is as good as the expert that wrote the rules; frequently the number of rules and their generality are too rudimentary for the description of complex phenomena. There is also the question of the availability of experts on certain systems, or, worse, different experts may have divergent opinions or experience on certain phenomena and are hard to reconcile in the ES. ES is very elaborate and extremely expensive to develop. The current derivation in conventional mechanistic models for sewage treatment is always based on the assumption of “steady state”. Consequently, the mechanistic model equations seldom reflect their capabilities involving simulation and prediction of a dynamic waster water treatment process. Models that are deterministic, such as the mathematical kinetic model like the IWA Activated Sludge Model No. 1 (ASM1) and Model No. 2 (ASM2), relying on a large number of differential equations, stoichiometric parameters and kinetic coefficients to describe the wastewater treatment process, e.g. ASM1 requires around 20 parameters and AMS2 over 60 parameters. Moreover, these kinetic parameters are influenced by changes in sewage quality, number of microorganisms involved, water temperature, etc. Among these, quite a few parameters are very difficult and time consuming to estimate, partly due to the limitation of available measurement technique and a large number are not routinely measured even by large wastewater utilities. This weakens the very foundation upon which deterministic models are built and their application in real- time control of WWTPs. As the above approaches has achieved very limited success and as a result, process control in the wastewater treatment industry is not model based, but rather relies upon a set of loosely defined heuristics in combination with the expert knowledge of plant operators. Artificial neural networks (ANN) avoid several of the disadvantages of the other techniques, by learning from plant historical data; no human expert, no specific knowledge, and no developed model are needed; the resulting network is fairly robust against process noise or instrumentation bias. WWTP specific behaviour is automatically learned, both as expert rules and in the process model. ANN is an artificial intelligence modelling technique, which has the ability to map the relationship between influent and effluent parameters, resulting in a process model that is based on full- scale historical operational data of a WWTP. ANN model form is determined from the
data themselves, thereby eliminating the need to choose an appropriate functional form of the relationships a priori. Among the most commonly used modeling techniques in wastewater treatment such as multivariate regression, time- series (e.g. Box- Jenkins), knowledge based systems, mechanistic models and the ANN, the prediction performance of ANN models have also been proven to be better. ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. Based on the literature review for applications of ANN in wastewater treatment, the following unexplored areas have been identified. Application of ANN for primary and chemically assisted primary wastewater treatment plants (WWTP) has not yet been investigated. However, it is important to note that wastewater treatment in major populous coastal cities are dependent on these treatment technologies, for example, 80% of the total 1400 ML/day average dry weather flow (ADWF) of wastewater in the Sydney region, serving more than 20% of total Australian population, are treated in the PS and CAPS WWTP (Sydney Water, 2003). Previous studies did not consider the impact of variation in raw sewage flowing into a WWTP over every day of the week (Sunday to Monday) and its influence on effluent quality, depending upon the people’s life style, demography, local hydrologic and meteorologic conditions in the WWTP catchments. No research in modelling to predict effluent oil and grease from WWTPs, has been documented in the published literature; however, O&G is one of the major cause of poor recreational water quality and pollution in beaches (Water Board, 1995; CWW, 1994; Sydney Water Board, 1987) in Sydney, Australia and across the world. In previous studies, the prediction of effluent quality by ANN models has been confined to maximum one-day advance prediction. One day is considered as relatively short for WWTP operators to take preventive action at the treatment plant and networks to avoid: (i) poor effluent quality; (ii) regulatory licence breach; and (iii) above all the impact on water environment and human health. ANN prediction studies so far are limited to individual unit processes, such as activated sludge process (ASP) or sequential batch reactor; previous research studies did not consider the whole WWTP, as one system (comprising of screening, grit removal, primary sedimentation, activated sludge, sequential batch reactor, disinfection unit processes), that, under the influence of varying sets of inputs, will respond by producing different sets of outputs. None of the ANN modelling studies for activated sludge process (ASP) and BNR, has considered all the inputs (process variables, influent and effluent quality, physical and meteorological parameters) such as: (i) raw sewage flow; (ii) TSS- raw, settle sewage and effluent; (iii) pH- raw, settle sewage, mixed liquor and effluent; (iv) COD- settle sewage; (v)
temperature; (vi) rainfall; (vii) MLSS; (viii) MLVSS; (ix) SVI; (x) RASSS; (xi) RASVSS; (xii) DO; (xiii) RAS recycle rate; (xiv) clarifier sludge blanket depth (SBD)- which are considered as the variables to be monitored for best practice operations of activated sludge process.
1.2.2 ESD and PPP to minimise environmental impacts from WWTPs
Since 1970s, the pollution control lic ensing under ‘command and control’ has contributed significantly in protecting the water environment from pollution by effluent discharges from WWTPs. However, this is effective in controlling only the acute impacts on the environment but not the chronic and cumulative environmental impacts. Command and control based licensing: (i) do not encourage conservation, recycle or re-use of water; (ii) weak in stimulating ongoing improvement in environmental performance beyond mere compliance with the required minimum level of performance; (iii) the fees are a fixed and unavoidable cost to industry; (iv) fees were not directly linked to pollutant loads and were very small compared to the costs of abating pollution; (v) good environmental performers were disadvantaged to the extent that they commit more resources to abatement measures than their competitors; and (vi) the cost of environmental harm caused by pollution was borne by the wider community (externality), and there were no attempt to internalise the costs of polluting activities. Therefore, a more holistic and integrated approach is required to address water pollution from wastewater treatment plants. Therefore in part 2 of this thesis, research endeavour is put into: (i) exploring the evolution of ‘ecologically sustainable development (ESD)’ principles in NSW and Australia starting from Stockholm Conference in 1972 to Rio Conference in 1992 and to Johannesburg Summit in 2002, in order to understand the concept of sustainable management of wastewater treatment plants much more clearly; and (ii) the application of ‘polluter pays principle (PPP)’ in wastewater treatment plant to address the chronic and cumulative environmental impact from its effluent discharges into receiving .waters. To date there are mounting evidence of unprecedented environmental degradation. This urgency has lead the emergence of modern international and national environmental law, which would seem to be based upon two principles- ‘the principle of the permanent sovereignty of states’ and ‘the principle of sustainable development’. Sustainable development is defined as: (i) “development that meets the needs of the present without compromising the ability of future generations to meet their own needs; or (ii) “improving the quality of human life while living within the carrying capacity of supporting ecosystems”. However, Australia has adopted the principle of ecologically sustainable development (ESD), based on: (i) precautionary principle; (ii) intergenerational equity; (iii) biological diversity and ecological integrity; and (iv) improved valuation of the environment.
The polluter- pays principle (PPP) means ‘pollute less, pay less; pollute more, pay more’ and thus ensuring that polluters pay the full cost of their polluting activities including pollution prevention, control and pollution damage costs. These costs include abatement costs, environmental protection regulation compliance and administration costs, and external pollution costs (that is, the costs of environmental damage resulting from discharges). PPP is the way for implementing the principles of ecologically sustainable development (ESD) through implementing improved pricing and valuation of environmental resources and conservation of biodiversity and ecological integrity. Current environmental protection legislation around the world normally regulates the concentration of pollutants in effluent from industries and businesses, for example, WWTP. The concentration limits for pollutants discharged in the effluent can only prevent acute impacts on the environment. However, limiting the load (kg/year) of pollutants discharged into waters can also control chronic and cumulative impacts of pollutants on receiving water environment. LBL takes into account the level of technology, effectiveness of process and resource management and discharge load (DL) of polluters (fee- rate-threshold, FRT); the pollutants’ harmfulness to human health, ecology and aquatic environments (pollutant weightings, PWs) and the sensitivity of the receiving waters (critical zoning factor, CZ). It reflects chronic, cumulative and acute impacts of pollutants on human health, ecology and the environment. The pollution fee under LBL will tend to reflect the marginal environmental damage cost of pollution over time. Thus LBL shifts the environmental costs of pollution from the community to those who pollute and it helps to protect, maintain and enhance the natural environment. Application of LBL in wastewater treatment plants is explored in great depth in chapter 8 of the thesis.
So far this thesis has dealt with technical aspects of environmental performance improvement of wastewater treatment plant through: (i) improving effluent quality using ANN modelling; and (ii) application of load- based- licensing (LBL) to calculate the environmental damage cost of pollution. To manage a wastewater treatment plant in compliance with ESD principles, it needs to encompass the leadership (policy and commitment), strategic and operational management of its processes and activities. ISO 14000 environmental management systems (EMS) have already established itself as a very useful tool for managing pollution prevention, compliance with environmental regulation and continuous improvement of its processes and activities for enhanced environmental performance of WWTPs. In the final part of this thesis, an integrated framework for ISO 9000 quality management systems (QMS), ISO 14000 EMS and AS 4801 occupational health and safety (OHS) management systems (MS) for WWTPs is developed.
ISO 9001 QMS has its focus on customers’ satisfaction and lowest life- cycle - cost of operation. ISO 14000 EMS focuses on environmental conservation, protection and enhancement. AS 4801 OHSMS focuses on safety, health and welfare and wellbeing of employees, contractors, service providers and the society in general. Therefore integrating the QMS, EMS and OHSMS together, it is expected that the wastewater treatment plant will deliver a well- balanced environmental, economic and social outcome (triple bottom line), which is at the heart of sustainable management of wastewater treatment plants.
The aim of this study is to progress the operation and management of a wastewater treatment plant (WWTP) from pollutant concentration limits- based ‘command and control’ licensing through load- based licensing to achieving ESD applying integrated quality, environment and
OHS management systems. This transformation has been planned in three distinct parts of exploratory research. In part 1, it is endeavoured to improve the effluent quality (TSS and O&G) from a WWTP applying artificial neural networks (ANN) model. In part 2, evolution of ESD in NSW and Australia, and the application of ‘polluter pays principle (PPP)’ through load based licensing (LBL) are researched. Finally, in part 3, an integrated quality- environment- OHS management systems framework for a WWTP is developed for managing its operations and processes in compliance with the ESD principles. Specific objectives of part 1 involving ANN modelling of WWTP are:
- to explore the feasibility and evaluate the capability of ANNs to model primary, chemically assisted primary sedimentation (CAPS), activated sludge and biological nutrie nt removal (BNR) wastewater treatment plants.
- to predict effluent O&G one, two and three days in advance from a primary and CAPS WWTPs using ANN models.
- to predict effluent TSS from primary, CAPS, ASP and BNR WWTPs, one, two and three days in advance.
- to compare the performance of ANN models for predicting effluent TSS, one, two and three days in advance for a primary, CAPS, ASP and BNR WWTP.
- to determine the significant input parameters which influence the prediction of effluent TSS and O&G for a primary, CAPS, ASP and BNR WWTP. Part 2 dealing with ESD and PPP, the objectives are:
- to explore the history of evolution of ecologically sustainable development (ESD) in NSW and Australia.
- to develop a holistic environmental performance indicator for WWTPs, called ‘pollution unit (PU)’, based on risk management principles.
- to develop the concept of pollution damage cost in economic ($) terms for receiving water environment from effluent discharges from WWTPs.
- to determine and compare the environmental performance of a primary, CAPS, ASP and BNR WWTP. Finally in part 3, the specific objectives are:
- to explore the merits and demerits of ISO 14000 environmental management systems (EMS) implemented in industries and businesses around the world, based on literature review.
- to develop a framework of an integrated quality- environment- OHS management systems framework for WWTPs.
Research work for this thesis is focussed on four wastewater treatment plants (WWTP) with four different, yet most popular process technologies and their effluent are discharging into waters of varying sensitivity. The four WWTPs are: (i) Northern WWTP, a primary sedimentation plant; (ii) Central WWTP, a chemically assisted primary sedimentation (CAPS) WWTP; (iii) Southern WWTP, a biological activated sludge process (ASP) WWTP; and (iv) Western WWTP, a biological nutrient removal (BNR) WWTP. For ANN modelling, each WWTP is considered as a system, comprising of a number of unit processes (Table 1.1). Process variables that are measured daily and for which data were collected for this study are presented in Table 1.2. Brief process description and process flow diagrams of Northern, Central, Southern and Western WWTPs are presented Figures 4.2, 4.4, 4.6 and 4.8 respectively. Part 1 of this research is restricted to ANN model development for: (i) predicting effluent O&G, one, two or three days in advance, for the Northern and Central WWTPs; and (ii) predicting effluent TSS, one, two or three days in advance, for the Northern, Central, Southern and Western WWTPs. ANN provides a means of computation inspired by the structure and operation of the brain and central nervous system. The goal of ANN is to map a set of input patterns onto a corresponding set of output patterns by first learning from a series of past examples defining sets of input and output for the given system. The network then applies what it has learned, to a new input pattern to predict the appropriate output. They require minimal specific knowledge of the intrinsic processes of the system under study. ANNs may be treated as universal function approximators, that are capable of finding relationships between potentially high dimensional, highly non- linear data sets with its ability to generalize, and the optimal ANN model form is determined from the data themselves, thereby eliminating the need to choose an appropriate functional form of the relationships a priori. ANNs operate as a parallel computer, which consists of a number
of processing elements (PEs) that are interconnected and are analogous to the biological neurons
in the brain. In feed-forward networks, the PEs is arranged in layers: an input layer, one or more hidden layers, and an output layer. Various activities carried out to accomplish the research objectives as stated in section 1.3 are:
- Literature reviews on: (i) the application of artificial neural networks (ANN) in wastewater and water treatment; (ii) operations of primary, chemically assisted primary, biological activated sludge process and biological nutrient removal treatment plants and treatment process parameters that influence and impact effluent TSS and O&G from these plants.
- Planning and implementation of sampling, monitoring and measurement of treatment plant inputs, process and outputs parameters.
- Quality assurance (QA) of collected data for data integrity and validation.
- Varying ANN model configuration, internal parameters and training and test data sets to identify the simplest and best performing ANN models which could predict effluent TSS and O&G, one, two and three days in advance with least error.
- Analyse and compare performance of best performing ANN models in predicting effluent TSS for primary, CAPS, ASP and BNR wastewater treatment plants.
- Carrying out sensitivity analysis for input parameters for each ANN models to determine the significant model inputs that influence the prediction of effluent TSS and O&G. In part 2 of this research on evolution of ESD in NSW and application of polluter pays principle, the scope of the current study are: (i) review of literature on history of sustainable development internationally and in Australia; (ii) literature review on ‘polluter pays principle’, market- based environmental instrument and load- based licensing; and (iii) development of water pollution measurement indicator, that is, ‘pollution unit (PU)’; PU needs to measure the environmental pollution from any WWTP taking into account the level of technology, effectiveness of process and resource management and discharge load (DL) of pollutants, the pollutants’ harmfulness to human health, ecology and aquatic environments and the sensitivity of the receiving waters; it needs to reflect chronic, cumulative and acute impacts of pollutants on human health, ecology and the environment; and (iv) determination of PU for Northern, Central, Southern and Western WWTPs and their comparative assessment; and (v) calculation of pollution fees for environmental pollution due to effluent discharge from Northern, Central, Southern and Western WWTPs. The final phase 3 of the thesis has its scopping for: (i) literature review on the implementation of ISO 14000 EMS; (ii) review of ISO 9001 quality management systems (QMS), AS 4801 occupational health and safety management systems (OHSMS) and AS 4360 Risk Management Standards; and (iii) developing a framework for integrated quality- environment- OHS management systems for wastewater treatment plants.
Sodium metabisulphite dechlorination
Daily raw sewage inflow
Daily treatment bypass Flow
Rainfall in the WWTP catchment
Coagulant (Ferric chloride) dosing rate
Flocculant (Polymer) dosing rate
Dissolved oxygen concentration (DO)
Return activated sludge suspended solids (RASSS)
Return activated sludge volatile suspended solids (RASVSS)
Sludge blanket depth (SBD)
Disinfectant chlorine dosing rate
Sodium metabisulphite dosing rate
Literature review on ANN, WWTP operations, ESD,
LBL & Management systems
Ch4 Materials & methods
Experimentation, data collection,
checking & validation for Northern,
Central, Southern and Western WWTP
Part 1 ANN Modeling of
Ch2 Literature review on ANN
application in water &
Ch3 ANN modeling
Ch5 ANN modeling
methodology for WWTPs
Part 2 ESD & PPP for WWTPs
Ch8 Measuring pollution from WWTPs in
environmental & economic terms
Four Journal papers: Raha, D., 2003b,
2003c, 2003d & 2002b.
Four Conference papers: Raha&
Sivakumar 2006h; Raha, D., 2003h,
2003i & 2003j.
Ten Seminars: Raha, D., 2006b1, 2006e,
Part 2 ESD & PPP for
Ch7 Evolution of ESD in
Four Journal papers:
Raha & Sivakumar
2006a; Raha, D. 2006b,
2006d & 2002c.
One Seminar: Raha, D.
2006k, 2006L, 2006o, 2005c, 2003k,
2002d & 2002a.
Part 1 ANN Modeling of WWTPs
Ch6 Results & discussion on WWTP ANN models
Nine Journal papers: Raha & Sivakumar 2006c,
2006m, 2006n; Raha, D., 2006i, 2006j, 2003f,
1997; Raha & Dharmappa 2004, 2003a.
Three Conference papers: Raha & Sivakumar
2006g; Raha & Dharmappa 2003j, 1998.
Three Seminars: Raha, D., 2005a1, 2004b & 2001.
Part 3 Integrated Management Systems for WWTPs Ch9 Managing WWTP with an 'Integrated management systems'. One Journal paper: Raha, D. 2005b. One Conference paper: Raha, D., 2005a. One Seminar paper: Raha, D., 2005e.
Ch10 Conclusion &
The human brain is the most complex computing device known to man. The brain’s powerful
thinking, remembering, and problem-solving capabilities have inspired many scientists to attempt computer modelling of its operation. Problems tackled by humans are immensely
parallel, requiring concurrent processing of information to provide a solution. The parallel processing nature of the brain, rather than speed, is the important feature, which makes our brain so efficient in recognising patterns and simplifying complex tasks (Beale, 1990). The brain has a novel way of processing information, utilising vast numbers of neurons whose individual processing capabilities are quite limited. These neurons are linked by and transmit information via electrochemical pathways. The resulting neural network is able to learn by adjusting the strength of the pathways. As a result, parallel computation can occur and memory is represented by the strength of the connections between neurons. The field of artificial intelligence (AI) was developed to harness the power of computing with the goal of emulating a highly efficient computer- the human brain. One group of researchers has sought to create a computer model
that matches the functionality of the brain in a very fundamental manner; the result has been
neural computing. In this chapter, a brief history of evolution of the artificial neural networks (ANN), originally based on the principle of biological or natural neural networks (NNN), is
presented. Operations of ANN including its learning process, its distinction from other forms of computing models and the essential elements of ANN model are also discussed. The process of learning by ANN models, its particular usefulness in wastewater treatment modelling and the constraints associated with it, are also elaborated.
Biological or natural neural networks (NNNs) are basically the information processing structure of nature, which enables the learning of complicated relationships and patterns from potentially incomplete information sources. A complex array of interconnected elements known as neurons, are responsible for the processing of information. Neurons are the communication links of the brain and behave essentially as microprocessors. Each neuron receives the combined output of many other neurons through input paths called dendrites The functions of a neuron are to
receive, integrate and transmit information. The length of neuron varies greatly from about 0.01
mm for human brain to about 1 mm for neurons of the limbs. A typical neuron, as shown in
Figure 2.1, is comprised of a cell body (soma) that is connected to adjacent neurons via axons.
An axon is a long thin fibre that transmits signals away from the soma to other neuron. Axons
can branch off in many directions, forming a dendritic tree for multiple communication links; a
feature that enables the parallel processing of information by the brain.
The Soma is often referred to as the cell body and it contains both the cell nucleus and “chemical machinery” common to most cells. The soma receives electrochemical inputs from adjacent neurons. It is responsible for the summation of such inputs before deciding whether to ‘fire’ its own electrical signal (Weiten, 1998). If summation is greater than a threshold value, then the neuron generates an action potential. When neuron produces a potential, a signal is then transmitted to nearby neurons. Axons are analogous to electrical cables, responsible for the transmission of the electrical activity from one neuron to the next (Baev, 1998). On the end of an axon is cluster of terminal buttons. Chemicals termed as “neurotransmitters” are secreted from the terminal buttons. In neural biology, a synapse is a junction where information is transmitted from one neuron to another. A single neuron may have 10 3 to 10 4 synapses, receiving information from thousands of neurons. Two neurons are not in direct contact with one another. Instead, they are separated by the synaptic cleft, a microscopic gap between the terminal button of one neuron and the cell membrane of another neuron (Schalkoff, 1997). The neuron sending the signal is termed the ‘presynaptic neuron’, while the neuron receiving the signal is termed the ‘postsynaptic neuron’. Synaptic vesicles are the small sacs in the terminal button storing chemicals. The vesicle is fused to the membrane of the presynaptic cell. During signal transmission, the vesicle’s contents spill into the synaptic cleft. The secreted chemicals (neurotransmitters) diffuse across the synaptic cleft to post synaptic membrane, eventually binding with the postsynaptic membrane at ‘receptor sites’. Such sites are “tuned” to respond to selected neurotransmitters. The reactions occurring in cell membranes result in the generation of postsynaptic potential (PSP). Once the PSP has been generated, the enzymes metabolise (convert) neurotransmitters bound to receptor sites into inactive forms, which can then be “sponged” up by presynaptic neuron. NNNs consist of tens of billions of densely interconnected
nerve cells (neurons) with trillions of interconnections, is able to learn quickly from experience and is generally superior to any existing machine in tasks involving recognition, learning and control (Hsu et al, 1995). The structure and operation of natural neural networks is addressed by numerous authors (Vemuri 1988; Hetcht-Nielsen, 1988, Hertz et al., 1991). The cerebral cortex or the human brain is an example of a natural neural network. The structure of human brain is extremely complex. Whilst the function of a single neuron is relatively well understood; the collective role of neurons within the conglomeration of cerebrum elements is less clear and is a subject of avid postulations (Bebis and Georgiopolous, 1994).
Artificial neural network (ANN) models, also known by other names such as connectionist models, parallel distributed processing (PDP) and neuromorphic systems, is a branch of artificial intelligence (AI). They are mathematical models of theorised mind and brain activities, which attempt to exploit the massively parallel local processing and distributed storage structure of the human brain and the central nervous system (Haykin 1994). Artificial neural networks are loosely based on the structure of natural neural networks but only exhibit a very small portion of their capabilities. A neural network is characterized by its architecture that represents the pattern of connection between nodes, its method of determining the connection weights, and the activation function (Fausett, 1994). Like natural or biological neural network (NNN), they consist of interconnected processing elements (neurons) and satisfy the “locality constraint”, which means that processing elements are only allowed to receive information supplied locally. As a result, the input to a processing element can only be directly affected by a node connected to its input path. In ANN, processing elements (PE) or nodes are equivalent to neurons in NNN. Processing elements are usually analog, non-linear and possess a small local memory and are slow compared with advanced digital circuitry. Individual processing elements are usually arranged in layers. Two of these layers, the input buffer (layer) and the output buffer, are connected to the environment. Data is presented to the network at the input buffer and the response to the input is presented at the output buffer. The layers in-between the input buffer and the output buffer are called hidden layers. Hidden layers enable the network to cater to non- linearities. At each node (PE) in a layer the information is received, stored, processed and communicated further to nodes in the next layer (ASCE 2000a). Each neuron is connected to every other neuron in adjacent layers by a connection weight, which determines the strength of the relationships between two connected neurons. The output from a neuron is multiplied by the connection weight before being introduced as input to the neuron in the next layer. Nodes in the various layers are either fully or partially interconnected. Each connection has associated with it
a particular adaptation coefficient or “weight” representing the synaptic strength of neural
connections. Different values of weights represent connections of varying strength. A zero weight represents the absence of a connection and a negative weight represents an inhibitory relationship between two PEs. These weights are adjusted using a learning rule. Alone, each neuron can perform only the simplest of operations. When assembled into an interconnected network, or architecture, however, the neurons become part of a powerful modelling system.
Neural research has usually been pursued in conjunction with researches in psychology and neuro- physiology. The first formulation of the theory on neural computing can be credited to McCulloch and Pitts (1943) with the proposed model of a neuron, the basic processing element of ANN and thereafter active research in ANN followed (Hebb, 1949; Rosenblatt, 1958; Widrow and Hoff, 1960). Rosenblatt (1962) at the Cornell University published the
“perceptron” concept in neural computing. Minsky and Papert (1969) of MIT, caused a pessimism and thereby, caused a dark age over the next two decades in the application of ANN. Later when John Hopfield showed that the hierarchical ANN could overcome the perceptron’s problem (Hopfield, 1982), the investigation in this area was reactivated. Thereafter, Rumelhart
et al. (1986a) of Stanford University, presented the generalised delta rule, or back- propagation
algorithm (BPA), and demonstrated its capability in training a multi- layer ANN. Soon, researchers recognised the capability of BPA in overcoming many limitations of the earlie r algorithms, and the modern renaissance in ANN followed. Artificial neural computing is implemented in the so-called artificial neural systems (also denoted as artificial neural networks). ANNs are defined as “massively parallel interconnected networks of simple elements and their hierarchical organizations which are intended to interact with the objectives of the real world in the same way as biological nervous systems do” (Kohonen, 1988). Because of their performance and effectiveness in problem-solving applications, ANNs have fascinated researchers in a variety of engineering and scientific fields, such as dynamic systems modelling, control, artificial intelligence and pattern recognition, and in general applications that require extremely complex algorithms, or that are impractical to solve otherwise.
Artificial neural networks differ from conventional computing and other forms of artificial intelligence such as expert systems in a number of ways. These differences are:
ß The knowledge of artificial neural networks is distributed and is stored in the
represented by the connection weights. On the other hand, the knowledge of expert systems is represented by its rules.
ß ANN process information in a parallel manner whereas von Neumann computers process information sequentially.
ß ANNs are very robust or fault tolerant. If some processing elements or the links between them are damaged or disabled, the overall performance of the network is not degraded significantly. This property arises from the fact that information is distributed and not contained in one place. In comparison, traditional computers are completely disabled by minimal damage to memory.
ß In order to program conventional von Neumann computers, an algorithm is required. However, for many complex problems such algorithms do not exist. Even if an algorithm is known, it is often too computationally intensive to generate solutions in an acceptable time frame. Similarly, expert systems have to be supplied with an explicit set of rules in order to make decisions. These rules must be laboriously abstracted, entered and checked for incompatibilities with the existing rule base. ANN, on the other hand, generates their own rules internally by learning from examples.
ß ANN has the ability to produce continuously graded inputs and outputs. This can be used to represent the intensity of an input feature or the certainty of a classification. It is very difficult or even impossible to represent such gradation in rule -based expert systems.
ß Unlike conventional computers and rule -based expert systems, the performance of ANN may not be affected significantly when presented with noisy input data.
ß Unlike rule -based expert systems, ANN can be inserted into existing technology with ease. They can be designed quickly and the direct hardware implementation of ANN is cost- effective. As a result, artificial neural networks can be used for incremental system improvement and upgrade. So far there have been a plethora of ANN models. There are two main factors that categorise network types: (1) how the data flows through the network and (2) the learning mechanism by which the network gets the correct answer. They can be historically classified as shown in Fig 2.2 (Mehra et al., 1992). However, back- propagation networks (BPN) is the most important one that contributed to the development of ANN. BPN will be discussed in detail in the next section.
There are different categories of artificial neural networks based on the way information flows through the network (e.g. feed-forward or feedback) and the learning rules used. The success story of ANN in modelling could be attributed largely to the application of BPN for training a multi- layer neural networks (El- Din et al. 2002; Lipmann, 1987) which focus on finding a repeated, recognizable and predictable patterns between the causes and the effects from the past operations records, and bypass the modelling of actual physical, chemical and biological processes of wastewater treatment. Backpropagation network, also called multi-layer feed- forward neural network (MLFN), has a colourful history. Apparently, it was originally introduced by Bryson and Ho in 1969 (Brysen et al., 1969) and independently rediscovered by Werbos in 1974 (Werbos, 1974), by Parker in the mid 1980’s (Parker, 1985, 1986, 1987) and by Rumelhart et al. in 1985 (Rumelhart et al. 1985, 1986). However, the credit for developing backpropagation into a usable technique, as well as promulgation of the architecture to a large audience, rest entirely with Rumelhart et al. (Rumelhart, et al. 1987; Hecht- Nielsen, R., 1987). Feed-forward networks have the ability to learn and to generalize from examples to produce meaningful solutions to problems even when the input data contain errors or are incomplete to some extent (Tresp et al. 1995). Most of the environmental, water and wastewater applications of ANN as revealed in the literature review, have used feed-forward networks for function approximation and they normally use back-propagation training algorithm (Maier and Dandy 1999). For modelling, the feed-forward ANN applying BPN, does not require a description of how the process occurs in either the micro or macro environments, and only requires knowledge of important factors that govern the process (Zhang and Stanley, 1999). The algorithm uses a gradient search technique to minimize a cost function equal to the mean square difference between the desired and the actual net outputs. It requires a continuous differentiable non- linearity (SinH or TanH transfer function) to be used as the transfer function by the neurons; without non- linear transfer function, the hidden layers would not make ANN more powerful than just plain perceptrons (Hamed et al., 2004). Back-propagation networks (BPN) consist of a minimum of three layers; an input layer, a hidden layer and output layer as shown in Figure 2.3. There is no theoretical limit on the number of hidden layers but typically there will be one or two. Each layer is fully connected to the succeeding layer. Nodes within a layer are not interconnected, and nodes in nonadjacent layers are not connected. Thus no communication is permitted between the processing elements (PEs) within a layer, but the processing elements in each layer may send their output to the processing elements in the succeeding layers. All connections are ‘feed forward’’ that is, they allow information transfer only from an earlier layer to the next consecutive layers. During learning, the information is also propagated back through the network and used to update the connection weights.
Back propagation involves two phases: a feed forward phase in which the external input information at the input nodes is propagated forward to compute the output information signal at the output nodes/ PEs, and a backward phase in which modifications to the connection weights are made based on the differences between the computed and observed information signals at the output nodes/ PEs. The error back propagation algorithm uses the mean square error over the training data as the objective function. At the beginning of a training process, the connection weights are assigned random values. The learning algorithm modifies the connection weights in each iteration until the successful completion of the training. When the iterative process has converged, the collection of connection weights captures and stores the knowledge and the information present in the examples used in the training process. Such a trained network is then ready to be used. When presented with a new input pattern, a feed forward network computation results in an output pattern, which is the result of the generalisation and synthesis of what the ANN has learned and stored in its connection weights. Back-propagation networks are capable of adjusting the weights in the hidden layer(s) by assigning part of the blame for erroneous outputs to all processing elements. The back-propagation network is a mapping network that approximates mathematical functions or mappings from an input space to an output space using examples of the mapping’s action. Mapping networks are variants of the methods of statistical regression analysis (Hetcht-Nielson, 1990). They can be designed and trained to map many complex patterns and have the ability to generalise with the aid of the hidden layer nodes, which perform non-linear feature extractions (Maen et al., 1990). The BPN may also be viewed as performing simple curve fitting operations in high dimensional space. In addition, learning is seen to be synonymous with producing a best fit surface in a high dimensional space to a finite set of data points (the training set) and generalising is seen to be equivalent to interpolating the test set on this fitting surface. During training, the mean square difference between the actual and the desired output is calculated as a function of the weights. A different error is obtained for each combination of weights, creating a mean squared error surface sitting above the weight space (Figure 2.5). This error surface has a number of global minima because of the large number of combinatorial permutations of the weights that leave the network input/ output function unchanged. The aim of the training procedure is to find a set of weights that will minimise the error function. Generally, the error function will not be reduced to zero, as the network is unable to perform exact mappings.
Trained backpropagation networks tend to give reasonable answers when presented with inputs that they have never seen. Typically, a new input will lead to an output similar to the correct output for input vectors used in training that are similar to the new input being presented. This generalisation property makes it possible to train a network on a representative set of input/ target pairs and get good results without training the network on all possible input/output pairs.
A summary description of the network operation (Figure 2.4) is used to illustrate how the BPN
can be used. After an input has been applied as a stimulus to the first layer of the network PEs,
it is propagated and processed through each layer until an output is generated from the output
layer. This output is compared to the desired output and an error is computed for each output unit. The error is then transmitted backwards from the output layer to the hidden layer that contributes to the output. However, each PE in the hidden layer receives only a portion of the total error, based on the relative contribution, the PE made to the original output. This process is repeated, layer-by- layer, until each PE in the network has received an error that describes its relative contribution to the total error. Based on the received error, connection weights are then adjusted by each PE until the error value reaches the convergence threshold value. The training procedure is shown in Figure 2.4 (Boger, 1992; Freeman, 1992).
Adjust all connection weights
The significance of the training process is that, as the network trains, the PEs in the intermediate layers organise themselves such that different PEs learn to organise different features of the total input space. After training, when presented with an arbitrary input pattern that is noisy or incomplete, the PEs in the hidden layers of the network will respond with an active output if the new input contains a pattern that resembles the feature the individual units learned to recognise during training. Conversely, hidden layer PEs have a tendency to inhibit their outputs if the input pattern does not contain the feature that they were trained to recognise (Freeman, 1992).
The operation of artificial neural networks is described extensively by a number of authors (Baughman et. al. 1995; Fausett, 1994; Chester, 1993; Masters 1993; Burke and Ignizio, 1992; Daniel, 1991; Neuralware, Inc, 1993; Vemuri, 1988; Lippmann, 1987; Jones et al., 1987; Rumelhart et al. 1986a, 1986b). The propagation of data through the network starts with the presentation of an input stimulus at the input buffer. The data then progresses through, and is operated on by, the network until an output stimulus is produced at the output buffer. Individual processing elements receive inputs from many other processing elements via weighted input connections as shown in Figure 2.3. These weighted inputs are summed and an optional threshold value is added or subtracted producing a single activation level for the processing element. The activation level constitutes the argument of a transfer function, which produces the output. This output is passed to the weighted input connections of many other processing elements.
The input to processing element (PE) j, the summation function:
The output from the PE j:
……………………………………………………………………… ………….(2.2)
x i : the input from node i, where i= 0, 1, 2, ….,n; n: the number of nodes in the previous layer; w ji : the connection weight between nodes i and j; f( ): the transfer function; y j = the output of node j; q j = the threshold for node j.
The performance of processing elements can be affected by changing transfer functions and adding new parameters or functions such as threshold or gains. Processing at each node occurs independently of the processing at all other nodes. At the same time, the processing done at each node affects the network as a whole as the output of one node becomes the input to many other nodes. In a similar way to natural neural networks, ANN learns by altering the connection strength between the processing elements. This is done by adjusting the weights on presentation of a set of training data using a learning rule. Once the learning phase is complete, the weights may be “frozen” and the network is ready to process “real” data. Because the artificial neuron is a composition of the summation and the activation functions as shown in equations 2.1 and 2.2, it can be concluded that the artificial neuron is also a function as a result of composition of known functions. The basic task of the connection weights (w ji ) and threshold (q j ) are to transform the signal functions connecting into and transmitting out of
an artificial neuron (Zhang et al., 2004). For simplicity, let us assume that the neuron starts with only one input and finishes with one output; then there are three adjustable components in the neuron: the input connection weight w ji , the threshold q j and the output connection weight w o . Therefore, the output function for this
neuron becomes:
f (x) = w y …………………………………………………………………… ………….(2.3)
When ANN is in the training phase, w ji , q j and w o are adjusted. The adjustment of w ji and q j
affect the inputs to the current neuron, and adjustment of w o affect the output of it, and subsequently the input to the next neuron. The other key component of the input and output mapping is the activation or transfer function itself. There are two major types of commonly used activation functions: (i) monotonic function e.g. symmetric logistic function, which maps
one- to- one relationships; and (ii) non- monotonic function e.g. Gaussian function, which maps many- to- one relationships.
2.5.1 Learning or Training by ANN
Learning is the process in which weights are adjusted in response to training data provided at the input buffer and depending on the learning rule, at the output buffer. The aim of learning is to teach the network to map a correct output vector for every input vector by developing appropriate connections in the model. When the nodes of input layer receive information from an external source, they become “activated” and emit signals to the next layer, which in turn emits outputs to the neighbour layer. Each connection between two nodes is associated with a weighting factor (w ji ) that adjusts the signal strength. Depending on the strength of the interconnections, signals reaching each node can excite or inhibit the node. In addition to the weighting factors, two other important factors govern the node outputs in the hidden and output layers. One is the bias vector (? j ), each element of which acts as an internal threshold to control activation of the corresponding node. For instance, the jth node sums the weighted inputs (e.g. w ji x i ) and then calculates the total activation, I j , by adding the internal threshold i.e., I j =
+ ? j . If ? j is large and positive, the node has a high internal threshold to inhibit node-
firing. Conversely, if ? j is zero or negative, the node has a low internal threshold, which excites node- firing. The other factor governing a node output is the activation function (sigmoid or TanH). The amount of learning that is required for a network to reach a satisfactory convergence depends on the number of PEs, the number of hidden layers and the number of data values (Daniel et al, 1993). The learning process allows the network to adapt its response with time in order to produce the desired output. Weights are updated in accordance with a learning rule using an on-line or off-line algorithm. When using an off-line algorithm, all the training data needs to be collected before learning can commence. On-line algorithms take into account training data as it becomes available. One type of on-line algorithm utilises all the data that has been presented to it whereas the other type only makes use of the latest training data. The error measures and error trajectories, as shown in Figure 2.6, are generally used to guide and assess training (Schalkoff, 1997). Over training occurs when the network learns the relationships for the particular training set rather than establishing a more general relationship. Learning procedures can be divided into five main categories, namely unsupervised learning, supervised learning, graded learning, hybrid learning and non-adaptive learning. The current study for ANN modelling of primary, chemically assisted primary, secondary and tertiary biological wastewater treatment plants is based on learning from the historical records of input and output pairs of process parameters of the same plants, and hence, the model so developed,
rely exclusively on the supervised learning from the historical plant data. For modelling, the supervised learning does not require a description of how the process occurs in either the micro or macro environments, and only requires knowledge of important factors that govern the process.
2.5.1.1 Supervised learning In supervised learning, the network is presented with an input stimulus as well as the desired response to that stimulus. Supervised learning incorporates an external teacher, so that each output neuron is told what its desired response to input signals ought to be. The aim of supervised learning is to determine a set of weights, which minimizes the error between the desired and computed output variable values. The outputs produced are compared with the target outputs, which are known in advance, and the generalization ability of network is measured by the root mean squared error (RMSE), E.
……………………………………………………………….……(2.4)
where t i is the desired (actually observed/ historical) output, o i is the output predicted by ANN and e is the epoch size. The errors (RMSE) generated are then propagated backwards in a
certain manner through the network for adjustment of the present connection weights using two factors, namely, a learning factor and a momentum factor. Initially, the weights are assigned arbitrary values. The weights are then updated systematically using a learning rule. The weight update equation typically takes the form:
where s is the training sample presented to the network, h is the learning rate, and m is the
momentum value. The number of training samples presented to the network between weight updates is called the epoch size. This procedure is repeated for each training example in the training set; a cycle (an epoch) represents one pass over the whole training set; multiple epochs are required until a satisfactory data mapping is achieved. The weight updates were done after each epoch and the network was saved at the point of minimum training error (maximum generalization). There are several methods for finding the weight increment, Dw ji , of which the gradient descent
method is the most common. As each combination of weights produces a different error, an error surface or trajectories exists as a function of the weights space (Figure 2.5). The gradient descent method results in weights being changed in the direction of steepest descent down the error surface. As a result, the weight update is carried out in the direction that yields the
maximum error reduction. The size of the step taken down the error surface is determined by a learning rate, h.
Dw ji = h ¶
w …………………………………………………………………………………(2.6)
The weight changes are obtained by performing gradient descent in weight space. Each weight is changed in proportion to the value of the partial derivative of the error function with respect to that weight. Consequently, weight changes are made down the gradient of the error surface. The weights are adjusted in two stages. During the first stage, the weights between the hidden layer and the output layer are adjusted. In the second stage, the weights between the hidden layer and the input layer are adjusted. An iterative method is used which propagates the error terms needed for weight adjustment back from the output layer to the input later via the hidden layer(s). As training patterns are presented to the network the weights are adjusted gradually in order to approximate the correct input/ output mapping. Normally all ANN models converges quite rapidly because the network may reach a local minimum in the error surface very quickly. As the learn count is increased, no further learning takes place, but the network jumps either side of the local minimum, resulting slight increases or decreases in the RMS error producing oscillations in the error surface while not producing the best results. Decreasing the learning rate and the momentum rate can reduce the magnitude of these oscillations. If these values are chosen to be small enough, the bottom of the valley in the error surface (the global minimum) may be reached (Maier et al 1994).
In summary, with respect to the actual mechanism of learning, the pattern are first presented to the network individually either in sequence or in random order. In the input layer, there is one neuron for each input parameter. The model inputs that make up the pattern are scaled by these neurons from their numeric range into a smaller and more efficient range according to predefined scaling function. The resulting output from each input- layer neuron is multiplied by the appropriate connection weights and is transferred to each of the hidden- layer neurons. Each of the hidden- layer neurons then sums all of the inputs that it receives from the input layer and adds a bias term to it (Equation 2.1). This sum is mapped into an output value according to a predefined activation function (Equation 2.2). The outputs from each of the hidden- layer neurons are multiplied by the appropriate connection weights and the resulting signals are transferred to the next layer for multi-hidden layer architecture or to the output layer for a 3- layer architecture (Figure 2.3). In the output layer, there is one neuron for each output parameter. Each of these neurons sums the weighted signals form the previous hidden layer. The sum is mapped into an output value according to a predefined activation function. The output signal from each neuron in the output layer is then processed by the inverse of the scaling function used in the input layer, in order to obtain an output value in the appropriate numeric range. This value, which is the model predicted value, is compared to the correct value for the given patterns and the connection weights are modified to decrease the sum of squared error according to preselected learning algorithm. The entire process is repeated until the ANN produces a sufficiently small error on a previously unseen data set (test set). The supervised learning aims at minimizing the RMSE between the observed and the predicted output in the output layer, through two phases. In the forward phase, the external input information signals at the input neurons are propagated forward to compute the output information signal at the output neuron. In the backward phase, modifications to the connection strengths are made, based on the basis of the difference in the predicted and observed information signa ls at the output neuron (Hamed et al., 2004).
2.5.1.2 Different learning rules in supervised learning Broadly speaking, there are two kinds of learning in ANNs: (i) Parameter learning and (ii) Structure learning. Parameter learning is concerned with updating the connecting weights in ANNs, and structure learning focuses on the change in the network structure, including the number of neurons and their connection types (Tay et al. 1999). The learning rule specifies how connection weights are changed during the learning process. NeuralWorks Professional II/ Plus (1994) supports the generalised delta (GD), the normalised cumulative delta (NCD) rule, the delta- bar- delta (DBD) algorithm, the extended DBD (EDBD) algorithm, the QuickProp (QP) algorithm, and the MaxProp (MP) algorithm. Normalized cumulative delta rule has been applied in this research for modelling.
Normalised cumulative delta (NCD) rule is a first- order method, whose convergence speed is linear. It is a variation of the GD rule; the only difference is that a number of training samples (equal to epoch size) are presented to the network before the weight updates are carried out. The error term used in the weight update equation is equal to the sum of the errors of the training samples presented to the network over one epoch, divided by the square root of the epoch size (Maier et al, 1998b). When weights are updated after the presentation of each training sample, the search path taken in weight space is stochastic, which increases the chances of escaping local minima in the error surface (Hassoun, 1995). In contrast, when larger epoch sizes are used, the search is forced to move into the direction of the true gradient after each weight update. In this research NCD learning rule with epoch size of 16 (software default) has been adopted.
2.6 Elements of an ANN Model
Connection weights have the function of amplifying, attenuating or changing the sign of the input signal. A zero weight represents the absence of a connection and a negative weight represents an inhibitory relationship between two nodes. In general, the output of node i is multiplied by the weight of the connection between nodes i and j to produce the input signal to node j. Hence connection weight represents the strength of the connection between two nodes. Weights are stored in the local memory of nodes and also hold the long-term memory of the network.
A threshold or bias acts like another processing element that has a constant output. The effect
the threshold is to add a constant value to the summed input (Equation 2.1). The purpose of this
is to scale the input to a useful range.
Transfer functions are mathematical formulae that give the output of a processing element as a function of its input signal. Transfer functions can take a variety of forms including threshold functions, hard limiters and continuos functions (eg. sigmoid, hyperbolic tangent). ANN modelling allows one to specify a transfer function (e.g. Linear, Sigmoid, TanH and Sine) that is used for all layers in the network. Each node in the ANN uses the transfer function to transform the weighted sum of the inputs into an output response (Equation 2.3). The transfer function can be a linear/ non-linear function that transfers internally the sum of the product of
input and connection weights to each PE to a potential output value. The transfer function is a mathematical formula that gives the output of a processing element as a function of its input signal. The sigmoid function is a bounded, monotonic, non- decreasing function that provides a graded, nonlinear response. This function enables a network to map any nonlinear process. The popularity of the sigmoid function is partially attributed to the simplicity of its derivative that has been used during the training process. Without non- linear transfer function such as tan- sigmoid (TanH) activation function, the hidden layers would not make ANN more powerful than just plain perceptrons (Hamed et al., 2004; Singh and Datta, 2004; Lee et al., 2002; Zhao et al., 1997; Gallant, 1993). The size of the steps taken in weight space during training and hence learning speed, is proportional to the derivative of the transfer function. If the gain of the transfer function (i.e. the slope of the almost linearly varying portion) is larger, the derivative of the transfer function, and hence the steps taken in weight space, are larger, resulting in increased training speed. The non- sigmoidal or linear transfer functions performed best when the data were noiseless and contained highly non- linear relationships. The sigmoid is a smooth version of a [0,1] step function whereas the hyperbolic tangent is a smooth version of a [-1,+1] step function as shown in Figure 2.6. Using sigmoidal (SinH or TanH) transfer functions in the hidden layers and linear transfer functions in the output layer can be an advantage when it is necessary to extrapolate beyond the range of training data (Kaastra et al., 1995; Karunanithi et al., 1994). The type of transfer function used affects the size of the steps taken in weight space, as weight updates are proportional to the derivative of the transfer function. With sigmoid function, a small change in the weights will usually produce a change in the outputs, which make it possible to assess the changes in weights (Hamed et al., 2004). The gain of the hyperbolic tangent transfer function is greater than that of the sigmoidal transfer function, and as a result, hyperbolic tangent transfer function learns quicker than sigmoidal function. Kalman et al. (1992) argued that the tanH transfer function should be used, which is in agreement with the empirical results obtained by Maier et al (1998a), Morshed and Kaluarachchi (1998) and in this study. Baughman et al (1995) has also concluded that the TanH transfer function outperforms the sigmoid transfer function. Two features of TanH are: (i) the slope of the TanH function is much greater than the slope of the sigmoid function; therefore, it can better distinguish between small variations in the input variable and can generate a much more non- linear response; (ii) the TanH function has a negative response for a negative input value and a positive response to a positive input value, while the SinH function always has a positive response.
……………(2.7)
1 st . Derivative:
f ¢(z) = f (z)[1 - f (z)] …
Hyperbolic tangent transfer function
f ¢( z) = [1 + f (z)] *[1 - f (z)] .………………
…(2.8)
The error function is the function that is minimised during training. Fundamentally, there are three different kinds of error functions e.g. quadratic or mean squared error (MSE), cubic and quartic error functions; however, there are minor deviations from these basic forms. During training, each weight is changed in proportion to the size and direction of the gradient of the error surface. This gradient is the partial derivative of the global error function with respect to the weight considered. Main error functions are:
Quadratic Error Function or MSE:
Cubic error function
Quartic error function
……………………………………………… (2.12)
4 ………………………………………………(2.13)
MSE function is most commonly used because: (i) it can be calculated easily; (ii) it penalises large errors; (iii) its partial derivative with respect to the weights can be calculated easily; and (iv) it lies close to the heart of the normal distribution (Masters, 1993). In order to obtain optimal results, the errors should be independent and normally distributed (Maier et al., 2000). The prediction performance and generalization ability of ANN models are evaluated by root mean square error (RMSE) (West et al., 2003; El-din et al., 2002; Choi et al. 2001; Pigram et al., 2001; Joo et al., 2000; Belanche et al., 1999; Hamoda et al., 1999; Maier et al. 1998b; Yabunaka, et al., 1997; Rodriguez, et al., 1997; Mirsepassi et al., 1997; Serodes et al., 1996;).
There must be ‘learning coefficient’ for each of the hidden layer(s) and the output layer. The amount a particular connection weight is changed, is proportional to the learning rate, ?. The
learning rate is used to increase the chance of avoiding the training process from being trapped in a local minimum instead of the global minimum (Hamed et al., 2004). The learning rate affects the size of the steps taken in weight space. The learning factor (ranges from 0 to 1) represents the step size by which the weights are updated and is problem-specific. For noisy data, it is better to keep it below 0.1 (Gallant, 1993). Learning coefficient of 0.1 has been found to be effective in this study. The theory of back- propagation requires the use of learning rates, which approximate zero (Neuralware Inc., 1991). However, very small learning rates slow down learning, especially on long, shallow portions of the error surface. On the other hand, if the learning rates are too big, the network may go through large oscillations during training or may never converge. Ideally, large learning rates should be used on long, shallow portions of the error surface (Figure 2.5) and smaller values of ? should be used on steep sections and near local minima. Care should be taken that the learning rate used is not too large, as this might result in divergent behaviour.
It is used in configuring the learning and recall schedules for the hidden and output layers. The momentum term may be considered to increase the effective step size in shallow regions of the error surface (Hassoun, 1995) and can speed up the convergence and learning process by several orders of magnitude (Syu and Chen, 1998; Masters, 1993). In order to speed convergence of the back-propagation algorithm, a momentum factor (also ranges from 0 to 1) can sometimes be used. The idea is to keep weight changes on a faster and more even path by adding fractions of previous weight changes (Equation 2.5). The momentum coefficient is an extra weight added onto the weight factors that accelerate the rate at which the weight factors are adjusted. The momentum coefficient helps move the minimization routine out of local minima. The inclusion of the momentum term has the effect of adding a proportion of the previous weight change to the current weight change during training. The proportion of the previous weight change added is equal to the momentum, µ. This term ensures that general trends are reinforced and oscillatory behaviour is dampened (NeuralWare Inc., 1993). A positive momentum value provides a built-