Uncertainty in Wastewater Treatment Design and Operation : Addressing Current Practices and Future Directions (STR).

Format:

Book

Author/Creator:

Belia, Evangelia.

Contributor:

Neumann, Marc B.

Benedetti, Lorenzo.

Series:

Scientific and Technical Report

Scientific and Technical Report ; v.21

Language:

English

Subjects (All):

Sewage disposal plants--Design and construction.

Physical Description:

1 online resource (234 pages)

Edition:

1st ed.

Other Title:

Uncertainty in Wastewater Treatment Design and Operation

Place of Publication:

London : IWA Publishing, 2021.

Summary:

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.

Contents:

Cover

Contents

List of Contributors

Preface

Acknowledgements

Introduction to the Scientific and Technical Report

Chapter 1: Key concepts of the STR

1.1 INTRODUCTION

1.2 RISK

1.3 UNCERTAINTY

1.3.1 Classification of uncertainty

1.3.1.1 Nature of uncertainty

1.3.1.2 Level of uncertainty

1.3.1.3 Location of uncertainty

1.3.2 Separating variability and uncertainty

1.3.3 Sources of variability and uncertainty

1.3.4 Uncertainty analysis approaches

1.4 INCORPORATING VARIABILITY AND UNCERTAINTY ANALYSIS IN MODELS

1.4.1 Variability and uncertainty in model steps

1.4.2 Sources of variability and uncertainty in models

1.4.2.1 Model input variability

1.4.2.2 Model input uncertainty

1.4.2.3 Model structure uncertainty

1.4.2.4 Model parameter uncertainty

1.4.2.5 Numerical uncertainty

1.4.2.6 Model output uncertainty

1.4.3 Evaluation methods

1.5 SUMMARY

REFERENCES

Chapter 2: Uncertainty in wastewater treatment - current practice

2.1 INTRODUCTION

2.2 GENERAL APPROACHES FOR ADDRESSING UNCERTAINTY IN WASTEWATER TREATMENT

2.2.1 Design guidelines

2.2.1.1 Overview

2.2.1.2 Design criteria

2.2.1.3 Safety factors

2.2.1.4 Reliability and redundancy standards

2.2.1.5 Development of tight contract documents

2.2.1.6 Staffing and monitoring

2.2.2 Statistical methodologies

2.2.3 Scenario analysis

2.2.4 Mathematical modelling

2.3 ADDRESSING SPECIFIC SOURCES OF UNCERTAINTY AND VARIABILITY IN CURRENT DESIGN PRACTICE

2.3.1 Addressing sources of variability and uncertainty in flow and load determination

2.3.1.1 Use of historical information to develop design flows and loads

2.3.1.2 Use of per capita flows and loads

2.3.1.3 Screening of influent wastewater data

2.3.1.4 Wastewater characteristics when data are not available.

2.3.2 Addressing sources of uncertainty in unit process design

2.3.2.1 Selection of design aerobic solids retention time

2.3.2.2 Selection of design sludge volume index

2.3.2.3 Selection of design denitrification rates

2.3.2.4 Selection of dissolved oxygen concentration in bioreactors

2.3.2.5 Selection of design oxygen transfer efficiency

2.3.3 Addressing uncertainty via effluent permit selection

2.3.3.1 Effluent limits

2.3.3.2 Selection of effluent design criteria

2.3.4 Summary of uncertainty analysis methods in current practice

2.4 IMPLICATIONS OF CURRENT PRACTICE ON DEGREES OF FREEDOM IN ENGINEERING DECISIONS

2.5 SUMMARY

Chapter 3: Incorporating uncertainty analysis into model-based decision making - opportunities and challenges

3.1 INTRODUCTION

3.2 INCORPORATION OF SAFETY IN CURRENT MODEL-ASSISTED DESIGN

3.3 OPPORTUNITIES OF EXPLICITLY CONSIDERING UNCERTAINTY AND VARIABILITY

3.4 SCOPE AND LIMITATIONS OF MODELS

3.4.1 Evolution of wastewater treatment modelling

3.4.2 Desirability criteria for models

3.4.3 Example of wastewater treatment plant model limitations

3.5 WHAT DON'T WE KNOW ABOUT DEALING WITH UNCERTAINTY?

3.5.1 How conservative are we with the safety factor approach?

3.5.2 How to move from guidelines with the safety factor approach to probabilistic model-assisted design?

3.5.3 Determination of prior uncertainty ranges

3.5.4 Parameter (uncertainty) estimation in systems with poor identifiability

3.5.5 How to adequately deal with biokinetic model structure uncertainty?

3.5.6 Full-fledged probabilistic model-based design

3.6 HOW CAN WE CURRENTLY ACCOUNT FOR VARIABILITY AND UNCERTAINTY?

3.6.1 Accounting for variability

3.6.1.1 Temporal variability

3.6.1.2 Spatial variability

3.6.2 Accounting for uncertainty.

3.6.2.1 Uncertainty related to design scenarios

3.6.2.2 Uncertainty related to data

3.6.2.3 Uncertainty related to process modelling

3.6.3 Sensitivity analysis

3.7 OPPORTUNITIES OF COMBINING MODELS WITH UNCERTAINTY - EXAMPLE

3.8 SUMMARY

Chapter 4: Available methods for uncertainty analysis in model-based projects - critical review

4.1 INTRODUCTION

4.2 METHODS AND LITERATURE REVIEW RESULTS SUMMARY

4.3 ASSESSMENT OF INPUT AND PARAMETER UNCERTAINTY

4.3.1 Input uncertainty (measurement errors)

4.3.1.1 Overview of statistical techniques used in measurement error detection

4.3.1.2 Error propagation

4.3.1.3 Examples of measurement error detection

4.3.1.4 Multivariate statistical methods

4.3.1.5 Statistical process control and fault detection methods

4.3.1.6 Data reconciliation

4.3.2 Parameter uncertainty

4.3.2.1 Inference vs. confidence regions

4.3.2.2 Application to wastewater treatment models

4.3.2.3 More sophisticated methods

4.4 ASSESSMENT OF MODEL STRUCTURE UNCERTAINTY

4.4.1 Macroscopic vs. microscopic mixing scales

4.4.2 Unquantified model structure uncertainty

4.4.3 Mathematical methods for quantification of model structure uncertainty

4.4.3.1 Monod growth model

4.4.3.2 Non-linear dynamical and chaotic behaviour

4.5 PROPAGATION OF UNCERTAINTY FOR MODEL-BASED DECISIONS

4.5.1 Review of uncertainty propagation methods

4.5.2 Discussion

4.5.2.1 Model calibration

4.5.2.2 Sensitivity analysis

4.5.2.3 Design optimisation

4.5.2.4 Computational demand

4.5.2.5 Method accuracy

4.6 SUMMARY

4.6.1 Input and parameter uncertainty assessment

4.6.2 Model structure uncertainty assessment

4.6.3 Propagation of uncertainty in model-based decision making

REFERENCES.

Chapter 5: The DOUT uncertainty analysis methodology - combining models, statistics and design guidelines

5.1 INTRODUCTION

5.2 THE INCLUSION OF UNCERTAINTY ANALYSIS IN A MODEL-BASED PROJECT

5.2.1 General tasks

5.2.2 Linking process modelling steps and uncertainty methodology tasks

5.3 BRIDGING DESIGN GUIDELINES AND STEADY-STATE DESIGN WITH DYNAMIC STOCHASTIC MODELLING

5.3.1 Define project objectives

5.3.2 Select configurations to be evaluated

5.3.2.1 Generation of a set of pre-designs with different levels of safety

5.3.2.2 Screening of pre-designs

5.3.2.3 Preliminary evaluation of pre-designs with dynamic data

5.3.3 Identify sources of variability and uncertainty to be evaluated

5.3.3.1 Input variability and uncertainty

5.3.3.2 Model structure and parametric uncertainty

5.3.3.3 Model numerical uncertainty

5.3.4 Prioritise and reduce sources of uncertainty

5.3.5 Describe sources of variability and uncertainty explicitly

5.3.5.1 Influent variability and generation of input time series

5.3.5.2 Parameter uncertainty

5.3.6 Model set-up and model structure uncertainty

5.3.7 Propagation of uncertainty and variability using Monte Carlo simulation

5.3.7.1 Monte Carlo simulations

5.3.7.2 One-dimensional Monte Carlo simulation

5.3.7.3 Two-dimensional Monte Carlo simulation

5.3.7.4 Pragmatic Monte Carlo method

5.3.7.5 Effluent constituents cumulative distribution generation

5.3.8 Synthesise evaluation metrics (output analysis)

5.3.8.1 Calculation of PONC

5.3.8.2 Calculation of total cost

5.3.9 Communicate results

5.4 SUMMARY

Chapter 6: Case studies

6.1 INTRODUCTION

6.2 STEADY-STATE UNCERTAINTY ANALYSIS EXAMPLE: OPERATION OF THE DURHAM WRRF

6.2.1 Project objectives

6.2.2 Conventional design approach using safety factors.

6.2.3 Probabilistic design approach

6.2.4 Results and discussion

6.3 DYNAMIC UNCERTAINTY ANALYSIS EXAMPLE: DESIGN UPGRADE FOR THE EINDHOVEN WRRF

6.3.1 Project objectives

6.3.2 Generation and screening of steady-state pre-designs

6.3.3 Variability and uncertainty propagation

6.3.3.1 Influent variability

6.3.3.2 Model parameter uncertainty

6.3.4 Quantification of probability of non-compliance (PONC)

6.3.5 Total cost estimates

6.4 SUMMARY

Chapter 7: The bigger picture

7.1 INTRODUCTION

7.2 ENGINEERING PROJECT PHASES

7.2.1 Overview

7.2.2 Regulatory phase

7.2.3 Planning phase

7.2.4 Preliminary (conceptual) design

7.2.5 Detailed design, construction, and start-up

7.2.6 Operations

7.3 STAKEHOLDERS

7.3.1 Overview

7.3.2 Regulators

7.3.3 Utilities - owners and operators

7.3.4 Engineers

7.3.5 Public

7.4 CONTRACT DELIVERY METHODS

7.4.1 Overview

7.4.2 Examples of delivery methods

7.4.3 Stakeholder involvement as a function of contract type

7.5 SUMMARY

Chapter 8: Perspectives

8.1 INTRODUCTION

8.2 SOCIOECONOMICS AND APPLIED MATHEMATICS

8.2.1 Socioeconomics

8.2.2 Applied mathematics and statistics

8.3 ACCOUNTING FOR UNCERTAINTY IN PROJECTS

8.3.1 Regulatory phase

8.3.2 Planning phase

8.3.3 Preliminary design

8.3.4 Detailed design

8.3.5 Operation

8.4 ALTERNATIVE WAYS OF HANDLING UNCERTAINTY

8.5 OUTLOOK

Appendix A: Terms and definitions - application and discussion

A.1 INTRODUCTION

A.2 MODELLING

A.3 STATISTICS

A.4 UNCERTAINTY

A.5 DISCUSSION OF TERMS OFTEN CONFOUNDED WITH UNCERTAINTY

A.5.1 Precision and variability

A.5.1.1 Quantification of precision and variability

A.5.2 Accuracy and uncertainty

A.5.2.1 Quantification of accuracy and uncertainty

A.5.3 Error and residual.

A.5.4 Trueness and bias.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781780401034

1780401035

OCLC:

1321796425