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E-raamat: Uncertainty in Wastewater Treatment Design and Operation: Addressing current practices and future directions (STR)

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Uncertainty in Wastewater Treatment Design and Operation: Addressing current practices and future directions (STR)Suurem pilt
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Scientific and Technical Report No. 21 Uncertainty in Wastewater Treatment Design and Operation aims to facilitate the transition of the wastewater profession to the probabilistic use of simulators with the associated benefits of being better able to take advantage of opportunities and manage risk. There is a paradigm shift taking place in the design and operation of treatment plants in the water industry. The market is currently in transition to use modelling and simulation while still using conventional heuristic guidelines (safety factors). Key reasons for transition include: wastewater treatment simulation software advancements; stricter effluent requirements that cannot be designed for using traditional approaches, and increased pressure for more efficient designs (including energy efficiency, green house gas emission control). There is increasing consensus among wastewater professionals that the performance of plants and the predictive power of their models (degree of uncertainty) is a critical component of plant design and operation. However, models and simulators used by designers and operators do not incorporate methods for the evaluation of uncertainty associated with each design. Thus, engineers often combine safety factors with simulation results in an arbitrary way based on designer ‘experience’. Furthermore, there is not an accepted methodology (outside modelling) that translates uncertainty to assumed opportunity or risk and how it is distributed among consultants/contractors and owners. Uncertainty in Wastewater Treatment Design and Operation documents how uncertainty, opportunity and risk are currently handled in the wastewater treatment practice by consultants, utilities and regulators. The book provides a useful set of terms and definitions relating to uncertainty and promotes an understanding of the issues and terms involved. It identifies the sources of uncertainty in different project phases and presents a critical review of the available methods. Real-world examples are selected to illustrate where and when sources of uncertainty are introduced and how models are implemented and used in design projects and in operational optimisation. Uncertainty in Wastewater Treatment Design and Operation defines the developments required to provide improved procedures and tools to implement uncertainty and risk evaluations in projects. It is a vital reference for utilities, regulators, consultants, and trained management dealing with certainty, opportunity and risk in wastewater treatment.
List of Contributors
xi
Preface xv
Acknowledgements xvii
Introduction to the Scientific and Technical Report xix
Chapter 1 Key concepts of the STR
1(14)
1.1 Introduction
1(1)
1.2 Risk
1(1)
1.3 Uncertainty
2(5)
1.3.1 Classification of uncertainty
2(2)
1.3.2 Separating variability and uncertainty
4(1)
1.3.3 Sources of variability and uncertainty
4(2)
1.3.4 Uncertainty analysis approaches
6(1)
1.4 Incorporating Variability and Uncertainty Analysis in Models
7(6)
1.4.1 Variability and uncertainty in model steps
7(2)
1.4.2 Sources of variability and uncertainty in models
9(3)
1.4.3 Evaluation methods
12(1)
1.5 Summary
13(2)
References
13(2)
Chapter 2 Uncertainty in wastewater treatment - current practice
15(18)
2.1 Introduction
15(1)
2.2 General Approaches for Addressing Uncertainty in Wastewater Treatment
15(6)
2.2.1 Design guidelines
15(5)
2.2.2 Statistical methodologies
20(1)
2.2.3 Scenario analysis
20(1)
2.2.4 Mathematical modelling
20(1)
2.3 Addressing Specific Sources of Uncertainty and Variability in Current Design Practice
21(8)
2.3.1 Addressing sources of variability and uncertainty in flow and load determination
21(2)
2.3.2 Addressing sources of uncertainty in unit process design
23(2)
2.3.3 Addressing uncertainty via effluent permit selection
25(1)
2.3.4 Summary of uncertainty analysis methods in current practice
26(3)
2.4 Implications of Current Practice on Degrees of Freedom in Engineering Decisions
29(1)
2.5 Summary
29(4)
References
30(3)
Chapter 3 Incorporating uncertainty analysis into model-based decision making - opportunities and challenges
33(14)
3.1 Introduction
33(1)
3.2 Incorporation of Safety in Current Model-Assisted Design
33(1)
3.3 Opportunities of Explicitly Considering Uncertainty and Variability
34(1)
3.4 Scope and Limitations of Models
34(2)
3.4.1 Evolution of wastewater treatment modelling
34(1)
3.4.2 Desirability criteria for models
35(1)
3.4.3 Example of wastewater treatment plant model limitations
35(1)
3.5 What Don't We Know about Dealing with Uncertainty?
36(2)
3.5.1 How conservative are we with the safety factor approach?
36(1)
3.5.2 How to move from guidelines with the safety factor approach to probabilistic model-assisted design?
36(1)
3.5.3 Determination of prior uncertainty ranges
37(1)
3.5.4 Parameter (uncertainty) estimation in systems with poor identifiability
37(1)
3.5.5 How to adequately deal with biokinetic model structure uncertainty?
38(1)
3.5.6 Full-fledged probabilistic model-based design
38(1)
3.6 How Can We Currently Account for Variability and Uncertainty?
38(5)
3.6.1 Accounting for variability
38(2)
3.6.2 Accounting for uncertainty
40(2)
3.6.3 Sensitivity analysis
42(1)
3.7 Opportunities of Combining Models with Uncertainty - Example
43(1)
3.8 Summary
44(3)
References
44(3)
Chapter 4 Available methods for uncertainty analysis in model-based projects - critical review
47(24)
4.1 Introduction
47(1)
4.2 Methods and Literature Review Results Summary
48(1)
4.3 Assessment of Input and Parameter Uncertainty
49(6)
4.3.1 Input uncertainty (measurement errors)
49(4)
4.3.2 Parameter uncertainty
53(2)
4.4 Assessment of Model Structure Uncertainty
55(3)
4.4.1 Macroscopic vs. microscopic mixing scales
55(1)
4.4.2 Unqualified model structure uncertainty
56(1)
4.4.3 Mathematical methods for quantification of model structure uncertainty
57(1)
4.5 Propagation of Uncertainty for Model-Based Decisions
58(6)
4.5.1 Review of uncertainty propagation methods
58(5)
4.5.2 Discussion
63(1)
4.6 Summary
64(7)
4.6.1 Input and parameter uncertainty assessment
64(1)
4.6.2 Model structure uncertainty assessment
65(1)
4.6.3 Propagation of uncertainty in model-based decision making
65(1)
References
66(5)
Chapter 5 The DOUT uncertainty analysis methodology - combining models, statistics and design guidelines
71(24)
5.1 Introduction
71(1)
5.2 The Inclusion of Uncertainty Analysis in a Model-Based Project
71(2)
5.2.1 General tasks
71(1)
5.2.2 Linking process modelling steps and uncertainty methodology tasks
72(1)
5.3 Bridging Design Guidelines and Steady-State Design with Dynamic Stochastic Modelling
73(18)
5.3.1 Define project objectives
74(1)
5.3.2 Select configurations to be evaluated
74(3)
5.3.3 Identify sources of variability and uncertainty to be evaluated
77(5)
5.3.4 Prioritise and reduce sources of uncertainty
82(1)
5.3.5 Describe sources of variability and uncertainty explicitly
82(1)
5.3.6 Model set-up and model structure uncertainty
83(1)
5.3.7 Propagation of uncertainty and variability using Monte Carlo simulation
83(5)
5.3.8 Synthesise evaluation metrics (output analysis)
88(2)
5.3.9 Communicate results
90(1)
5.4 Summary
91(4)
References
91(4)
Chapter 6 Case studies
95(16)
6.1 Introduction
95(1)
6.2 Steady-State Uncertainty Analysis Example: Operation of the Durham WRRF
95(6)
6.2.1 Project objectives
95(1)
6.2.2 Conventional design approach using safety factors
96(1)
6.2.3 Probabilistic design approach
97(2)
6.2.4 Results and discussion
99(2)
6.3 Dynamic Uncertainty Analysis Example: Design Upgrade for the Eindhoven WRRF
101(7)
6.3.1 Project objectives
101(1)
6.3.2 Generation and screening of steady-state pre-designs
102(2)
6.3.3 Variability and uncertainty propagation
104(1)
6.3.4 Quantification of probability of non-compliance (PONC)
105(3)
6.3.5 Total cost estimates
108(1)
6.4 Summary
108(3)
References
108(3)
Chapter 7 The bigger picture
111(12)
7.1 Introduction
111(1)
7.2 Engineering Project Phases
112(6)
7.2.1 Overview
112(3)
7.2.2 Regulatory phase
115(1)
7.2.3 Planning phase
115(1)
7.2.4 Preliminary (conceptual) design
115(1)
7.2.5 Detailed design, construction, and start-up
116(1)
7.2.6 Operations
117(1)
7.3 Stakeholders
118(2)
7.3.1 Overview
118(1)
7.3.2 Regulators
118(1)
7.3.3 Utilities - owners and operators
118(2)
7.3.4 Engineers
120(1)
7.3.5 Public
120(1)
7.4 Contract Delivery Methods
120(2)
7.4.1 Overview
120(1)
7.4.2 Examples of delivery methods
120(1)
7.4.3 Stakeholder involvement as a function of contract type
121(1)
7.5 Summary
122(1)
References
122(1)
Chapter 8 Perspectives
123(6)
8.1 Introduction
123(1)
8.2 Socioeconomics and Applied Mathematics
124(1)
8.2.1 Socioeconomics
124(1)
8.2.2 Applied mathematics and statistics
124(1)
8.3 Accounting for Uncertainty in Projects
125(2)
8.3.1 Regulatory phase
125(1)
8.3.2 Planning phase
125(1)
8.3.3 Preliminary design
125(1)
8.3.4 Detailed design
126(1)
8.3.5 Operation
126(1)
8.4 Alternative Ways of Handling Uncertainty
127(1)
8.5 Outlook
127(2)
References
128(1)
Appendix A Terms and definitions - application and discussion
129(16)
A.1 Introduction
129(1)
A.2 Modelling
130(3)
A.3 Statistics
133(2)
A.4 Uncertainty
135(1)
A.5 Discussion of Terms Often Confounded with Uncertainty
136(9)
A.5.1 Precision and variability
136(1)
A.5.2 Accuracy and uncertainty
136(1)
A.5.3 Error and residual
137(1)
A.5.4 Trueness and bias
137(1)
A.5.5 Note on true values
138(1)
A.5.6 Note on repetitions
139(1)
A.5.7 Bias, variability and uncertainty: a graphical example
139(1)
A.5.8 Link between measurement, modelling and prediction
140(1)
A.5.9 Qualitative model performance criteria
140(1)
A.5.10 Reliability and redundancy
141(1)
A.5.11 Robustness and resiliency
142(1)
References
143(2)
Appendix B Methods for uncertainty analysis
145(6)
B.1 Uncertainty Frameworks
145(1)
B.1.1 Frequentist
145(1)
B.1.2 Bayesian
145(1)
B.2 Monte Carlo Simulation
146(5)
B.2.1 Random sampling and LHS
147(1)
B.2.2 Introducing correlations between parameters
148(1)
References
149(2)
Appendix C Existing methods for uncertainty analysis in WWT model-based projects - Complete literature search results
151(24)
C.1 Introduction
151(1)
C.2 Assessment of Input and Parameter Uncertainty
152(1)
C.3 Assessment of Model Structure Uncertainty
153(3)
C.4 Propagation of Uncertainty for Model-based Decisions
156(6)
C.5 Uncertainty in Wastewater Treatment Plant Operational Control Data and Methods of Addressing in Online Control
162(5)
C.6 Uncertainty in the Fate of Pollutants in the Environment and Resulting in Regulatory (WWTP Effluent Standards) Issues
167(4)
C.7 Updated Literature 2011-2019
171(4)
Appendix D Application of uncertainty analysis methods - knowledge from other fields
175(20)
D.1 Introduction
175(1)
D.2 Review of Uncertainty Analysis Methods in Chemical Engineering
175(10)
D.2.1 Comparison of chemical engineering with wastewater treatment
175(2)
D.2.2 Uncertainty methods used in chemical engineering
177(8)
D.2.3 Applicability to WWT
185(1)
D.3 Review of Uncertainty Analysis Methods in Hydrogeological (Groundwater) Engineering
185(10)
D.3.1 Comparison of hydrogeological engineering with WWT
185(2)
D.3.2 Uncertainty methods used in hydrogeological engineering
187(2)
D.3.3 Applicability to wastewater treatment
189(1)
References
190(5)
Appendix E Current practices in different countries
195(11)
E.2 Current Practice in North America
195(4)
E.2.1 Planning phase
195(1)
E.2.2 Design-bid-build contracts
195(2)
E.2.3 Design-build contracts
197(1)
E.2.4 Design-build-operate contracts
198(1)
E.3 Current Practice in Other Countries
199(7)
E.3.1 Questionnaire
199(1)
E.3.2 United Kingdom
200(1)
E.3.3 The Netherlands
201(1)
E.3.4 Switzerland
202(1)
E.3.5 Czech Republic
203(1)
E.3.6 South Korea
204(1)
E.3.7 South America
205(1)
References 206(1)
Index 207
Editors: Evangelina Belia, Marc B. Neumann, Lorenzo Benedetti, Bruce Johnson, Sudhir Murthy, Stefan Weijers and Peter A. Vanrolleghem (IWA Task Group on Design and Operations Uncertainty - DOUTGroup)
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