Additional Information
Book Details
Abstract
Today there is increasing pressure on the water infrastructure and although unsustainable water extraction and wastewater handling can continue for a while, at some point water needs to be managed in a way that is sustainable in the long-term. We need to handle water utilities “smarter”.
New and effective tools and technologies are becoming available at an affordable cost and these technologies are steadily changing water infrastructure options. The quality and robustness of sensors are increasing rapidly and their reliability makes the automatic handling of critical processes viable. Online and real-time control means safer and more effective operation.
The combination of better sensors and new water treatment technologies is a strong enabler for decentralised and diversified water treatment. Plants can be run with a minimum of personnel attendance. In the future, thousands of sensors in the water utility cycle will handle all the complexity in an effective way.
Smart Water Utilities: Complexity Made Simple provides a framework for Smart Water Utilities based on an M-A-D (Measurement-Analysis-Decision). This enables the organisation and implementation of “Smart” in a water utility by providing an overview of supporting technologies and methods.
The book presents an introduction to methods and tools, providing a perspective of what can and could be achieved. It provides a toolbox for all water challenges and is essential reading for the Water Utility Manager, Engineer and Director and for Consultants, Designers and Researchers.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| CONTENTS | 5 | ||
| FOREWORD 1: A NEW EMERGING PARADIGM | 6 | ||
| MY OVERALL CONCLUSION IS THAT WE NEED THIS BOOK! | 7 | ||
| FOREWORD 2: THE FUTURE IS SMART | 8 | ||
| DOING MORE WITH LESS – SMARTENING-UP OUR WATER SYSTEMS FOR BRIGHTER FUTURE | 8 | ||
| 1: INTRODUCTION | 13 | ||
| TODAY | 14 | ||
| TOMORROW | 17 | ||
| COMPLEXITY MADE SIMPLE | 18 | ||
| THE AUTHORS’ MOTIVATION | 20 | ||
| DEMAND PULL | 21 | ||
| TECHNOLOGY PUSH | 21 | ||
| THIS BOOK IS FOR YOU! | 22 | ||
| A TOOL BOX FOR ALL WATER CHALLENGES | 24 | ||
| 2: APPROACH | 27 | ||
| WHAT IS A SMART WATER UTILITY? | 28 | ||
| MAKING WATER VISIBLE | 28 | ||
| APPLYING SMART THROUGHOUT THE WATER CYCLES | 29 | ||
| SMART WATER | 29 | ||
| SMART WATER TECHNOLOGY LEAVING INFANCY? | 30 | ||
| SMART WATER TECHNOLOGY GROWING UP! | 31 | ||
| THE ROLE OF INSTRUMENTATION, CONTROL AND AUTOMATION (ICA) IN A WATER UTILITY | 32 | ||
| DISEASES IN ICA SYSTEMS | 34 | ||
| OBJECTIVES IN A SMART WATER UTILITY | 36 | ||
| CUSTOMER SERVICE LEVEL | 36 | ||
| Encompassing | 37 | ||
| Specific | 37 | ||
| Adjustable | 37 | ||
| Simulatable | 37 | ||
| Have a clear effect | 37 | ||
| STAKEHOLDER DEMANDS | 38 | ||
| Customer demands | 38 | ||
| Community demands | 39 | ||
| Environmental demands | 40 | ||
| DIFFERENT OBJECTIVES AT DIFFERENT LEVELS | 41 | ||
| Urban water cycle objectives | 41 | ||
| Plants and networks | 41 | ||
| Process and districts | 41 | ||
| Functional units | 42 | ||
| Components | 43 | ||
| THE THREE MAIN ASPECTS OF WATER UTILITY OBJECTIVES | 43 | ||
| 1. Effectiveness in design | 43 | ||
| 2. Efficiency in operation | 44 | ||
| 3. Total reliability | 44 | ||
| MAPPING THE OBJECTIVE HIERARCHY | 45 | ||
| M-A-D: A NEW MIND-SET FOR SMART WATER UTILITIES | 46 | ||
| TRACKING PROGRESS TOWARDS BECOMING A SMART WATER UTILITY | 46 | ||
| THE M-INDICATOR | 49 | ||
| Evaluating the state of the sensor landscape | 49 | ||
| THE A-INDICATOR | 51 | ||
| Evaluating the state of the analysis capabilities of the organisation | 51 | ||
| 0: No attention | 51 | ||
| 1. Rudimentary attention | 51 | ||
| 2. Narrow attention | 51 | ||
| 3. Daily attention | 51 | ||
| 4. Time variance orientation | 51 | ||
| 5. Multi parameter orientation | 51 | ||
| 6. Simple analytical tools | 51 | ||
| 7. Advanced analytical tools | 52 | ||
| 8. Process models | 52 | ||
| 9. Online models | 52 | ||
| 10. Plant wide online models | 52 | ||
| THE D-INDICATOR: | 52 | ||
| Evaluating the state of the decision capabilities | 52 | ||
| IMPLEMENTATION | 54 | ||
| THE IDEA OF CONTROL | 54 | ||
| TYPES AND SCOPE OF DECISIONS IN WATER UTILITIES | 56 | ||
| BUILDING STRONG DECISION CAPABILITY IN ORGANISATIONS | 58 | ||
| 3: MEASURE | 61 | ||
| MANY WAYS OF SENSING WATER | 62 | ||
| SENSORS: THE BASIS OF “SMART” | 64 | ||
| HYDRAULIC SENSORS | 64 | ||
| KEY DRINKING WATER QUALITY SENSORS | 66 | ||
| KEY WASTEWATER QUALITY SENSORS | 71 | ||
| EMERGING SENSOR TRENDS | 76 | ||
| Microbiology | 76 | ||
| Intelligent sensors | 77 | ||
| Micro pollutants | 77 | ||
| Spectroscopy | 78 | ||
| Wireless communication | 78 | ||
| A DATA AND INFORMATION “LIBRARIAN” | 79 | ||
| SENSOR SELECTION | 80 | ||
| IMPORTANT ASPECTS OF SENSOR SELECTION | 80 | ||
| Cost of ownership | 80 | ||
| The capital cost | 80 | ||
| The operational cost | 81 | ||
| Other costs | 81 | ||
| Total cost of ownership | 81 | ||
| Hassle of ownership | 81 | ||
| Sensor technology | 81 | ||
| Technical specifications | 82 | ||
| Range | 82 | ||
| Sensitivity to other parameters | 82 | ||
| Other technical parameters | 82 | ||
| In summary | 83 | ||
| SIGNAL DYNAMICS | 84 | ||
| ACCURACY AND PRECISION | 84 | ||
| SENSOR SUPPLIER SELECTION | 85 | ||
| Supplier competence level | 85 | ||
| Supplier service level and responsiveness | 86 | ||
| Width of portfolio | 86 | ||
| ELECTRICAL CONTROL SYSTEMS | 87 | ||
| ARCHITECTURE OF CONTROL SYSTEMS | 87 | ||
| THE HUMAN–MACHINE INTERFACES | 89 | ||
| Lack of overview | 90 | ||
| Lack of understanding of the data points | 90 | ||
| Difficult to forecast what will happen in the future | 90 | ||
| Inability to change the system | 90 | ||
| IT SECURITY | 91 | ||
| 4: ANALYSE | 95 | ||
| THE ANALYSIS TOOLBOX | 96 | ||
| SINGLE SIGNAL ANALYSIS | 98 | ||
| SIGNAL FILTERING | 98 | ||
| Real-time exponential low pass filter | 98 | ||
| Moving average filter | 100 | ||
| A high-pass filter | 101 | ||
| DETECTING OUTLIERS AND REPAIRING DATASETS | 102 | ||
| Averaging | 103 | ||
| DETECTION AND DIAGNOSIS FOR EARLY WARNING SYSTEMS | 103 | ||
| STATISTICAL PROCESS CONTROL | 105 | ||
| MATHEMATICAL MODELS | 106 | ||
| INTRODUCTION TO MODELLING | 107 | ||
| A spectrum of models | 109 | ||
| Microscopic and macroscopic | 109 | ||
| System wide models | 110 | ||
| Model limitations and uncertainty | 110 | ||
| Model changes | 111 | ||
| Dealing with uncertainties | 111 | ||
| Verifying models | 112 | ||
| REGRESSION ANALYSIS | 113 | ||
| MULTIVARIATE ANALYSIS | 115 | ||
| HYDRAULIC MODELLING AND THE PROCESS OF OPERATING MODELS | 116 | ||
| 1. Build a model of the network | 117 | ||
| 2. Apply consumptions or production patterns | 117 | ||
| 3. running the model | 117 | ||
| 4. Calibrating the model | 118 | ||
| 5. Testing out scenarios | 118 | ||
| Documentation | 119 | ||
| SEWER SYSTEM MODELLING | 119 | ||
| Combined Sewer Overflow | 119 | ||
| WATER DISTRIBUTION NETWORK MODELLING | 121 | ||
| Modelling the propagation of a pollution in drinking water | 121 | ||
| Water leakage and energy consumption | 121 | ||
| REACTOR HYDRAULIC MODELLING | 122 | ||
| THE ACTIVATED SLUDGE MODEL | 125 | ||
| THE BENCHMARK MODEL | 128 | ||
| PERFORMANCE MEASURES | 130 | ||
| KEY PERFORMANCE INDICATORS | 130 | ||
| Leading and lagging indicators | 130 | ||
| Quantitative and qualitative indicators | 131 | ||
| Input, output and process indicators | 131 | ||
| Different indicators are relevant to different people | 131 | ||
| Aspects | 133 | ||
| BENCHMARKING | 133 | ||
| Renewal of sewer systems | 133 | ||
| Non-revenue water | 134 | ||
| Amount of bacteriological samples | 134 | ||
| 5: DECIDE | 141 | ||
| THE DECISION TOOL BOX | 142 | ||
| STRATEGIC DECISION MAKING | 144 | ||
| TOOLS FOR DEVELOPING STRATEGIES | 146 | ||
| 1. SWOT | 148 | ||
| 2. Porter’s five forces | 148 | ||
| 3. PESTEL analysis | 148 | ||
| 4. Benchmarking | 149 | ||
| 5. Critical success factors | 149 | ||
| 6. USP analysis | 149 | ||
| 7. Balanced score card | 150 | ||
| 8. The triple bottom-line | 150 | ||
| 9. Turnaround management | 150 | ||
| 10. Scenario analysis | 151 | ||
| 11. Value chain analysis | 151 | ||
| 12. Mission and vision statements | 151 | ||
| STRATEGIC PLANNING | 152 | ||
| SMART WATER UTILITY STRATEGY AND PLANNING | 155 | ||
| RELATIONSHIP TO OVERALL STRATEGY | 155 | ||
| RESOURCES | 155 | ||
| INFRASTRUCTURE | 155 | ||
| ASSET MANAGEMENT STRATEGY AND PLANNING | 158 | ||
| DEFINING VALUATION CRITERIA | 160 | ||
| DATA BASED | 161 | ||
| RISK BASED | 161 | ||
| TRANSPARENT AND LOGICAL | 161 | ||
| ALIGNED | 162 | ||
| INCLUDE ALL RELEVANT FACTORS | 162 | ||
| PORTFOLIO PRIORITISATION | 162 | ||
| Sewer system | 164 | ||
| Drinking water pipelines | 164 | ||
| FINDING THE BEST SOLUTION | 164 | ||
| 1. Create a clear understanding of the full problem | 164 | ||
| 2. Creating alternatives | 165 | ||
| 3. Evaluation | 165 | ||
| 4. Implementation | 165 | ||
| MAINTENANCE | 165 | ||
| OPERATIONAL DECISION MAKING | 166 | ||
| SMART MAINTENANCE | 166 | ||
| PLANT WIDE CONTROL | 167 | ||
| Wastewater treatment as a resource recovery | 169 | ||
| Water supply operation | 169 | ||
| System structure | 170 | ||
| CONTINUAL IMPROVEMENT | 170 | ||
| FACILITATING CONTINUAL IMPROVEMENT THROUGH THE MEDIATION PROCESS” | 171 | ||
| UNPLANNED DECISION MAKING | 174 | ||
| Early warning systems | 174 | ||
| Handling emergency situations | 175 | ||
| Setting up a smart system for emergency situations | 175 | ||
| AUTOMATIC DECISION MAKING: CONTROL | 177 | ||
| WHY AUTOMATIC PROCESS CONTROL? | 177 | ||
| Time scales | 178 | ||
| Modelling for control | 179 | ||
| Open loop and closed loop | 179 | ||
| Disturbances – the reason for control | 180 | ||
| INTRODUCTION TO THE PID CONTROLLER | 180 | ||
| Feedforward control | 183 | ||
| ADVANCED CONTROL METHODS | 183 | ||
| Cascaded control | 183 | ||
| Nonlinear control | 183 | ||
| Model-based control | 184 | ||
| ACTUATORS | 186 | ||
| On-off control | 186 | ||
| Design for control and efficiency | 188 | ||
| CONTROLLER TUNING | 188 | ||
| AUTOTUNING | 190 | ||
| 6: CASE STUDIES | 193 | ||
| REAL-TIME MONITORING OF GANGES RIVER BASIN DURING KUMBH MELA CEREMONY | 194 | ||
| DESCRIPTION OF INSTALLED BASE | 195 | ||
| SMART SOFTWARE SPOTLIGHTS | 196 | ||
| 1. Real-time data validation by vali::tool | 197 | ||
| 2. Event detection software | 197 | ||
| 3. Offline data analysis and evaluation | 198 | ||
| LESSONS LEARNED | 199 | ||
| A GLIMPSE INTO SMART WATER DATA | 200 | ||
| UNDERSTANDING COMBINED SEWER OVERFLOWS (CSOS) | 202 | ||
| CSO MANAGEMENT | 202 | ||
| SITE PRESENTATION | 203 | ||
| SEWER SYSTEM MODELLING | 203 | ||
| MODEL CALIBRATION | 205 | ||
| SIMULATION RESULTS | 206 | ||
| ADVANCED PROCESS CONTROL IN DECENTRALISED MBR WASTEWATER TREATMENT PLANT | 208 | ||
| THE CASE | 208 | ||
| PLANT DESCRIPTION | 209 | ||
| NITROGEN CONTROL | 209 | ||
| PHOSPHORUS CONTROL | 212 | ||
| RESULTS | 213 | ||
| PARADIGM SHIFT IN SENSOR USAGE: FROM MEASURING TOOL TO PROCESS UNDERSTANDING AND INTELLIGENT CONTROL | 214 | ||
| AVEDØRE WASTEWATER TREATMENT PLANT | 215 | ||
| IMPORTANCE OF RELEVANT NECESSARY CONTROL ROUTINES | 216 | ||
| Nitrate sensor | 216 | ||
| Ammonia and phosphate sensors | 216 | ||
| Suggestions for improvement | 217 | ||
| SENSOR DATA LEADING TO PROCESS UNDERSTANDING AND DISCOVERY OF NEW CONTROL ALGORITHMS | 217 | ||
| PROCESS UNDERSTANDING LEADING TO IMPROVED AERATION AND REDUCED NUMBER OF SENSORS | 219 | ||
| REFERENCES | 220 | ||
| MODEL-SUPPORTED DESIGN, TESTING, AND IMPLEMENTATION OF PROCESS CONTROL STRATEGIES | 221 | ||
| PROBLEM STATEMENT | 221 | ||
| OBSTACLES TO ADVANCED PROCESS CONTROL | 221 | ||
| CONTROLLER ISSUES | 222 | ||
| NON-TECHNICAL OBSTACLES | 223 | ||
| Example 1 | 223 | ||
| Example 2 | 224 | ||
| SOLUTIONS | 224 | ||
| INTEGRATION OF WORK FLOWS | 225 | ||
| CONCLUDING REMARKS | 226 | ||
| REFERENCES | 226 | ||
| THE RISK OF NOT MEASURING | 227 | ||
| OIL EXPLORATION IN THE NIGER DELTA | 227 | ||
| THE BODO CASE | 227 | ||
| AUTOMATIC LEAKAGE DETECTION | 228 | ||
| THE COST OF NOT DETECTING | 229 | ||
| REFERENCES | 230 | ||
| MODELLING INTEGRATED WASTEWATER SYSTEMS FOR DESIGN AND OPERATION IN EINDHOVEN (NETHERLANDS) | 231 | ||
| REFERENCES | 236 | ||
| LESSONS LEARNT FROM IMPLEMENTING AMMONIUM CONTROLLERS AT THREE FULL-SCALE PLANTS | 237 | ||
| REMEMBER THE COST–BENEFIT ANALYSIS! | 238 | ||
| IT TAKES TIME TO PERFORM FULL-SCALE STUDIES OF CONTROL STRATEGIES | 239 | ||
| IT SHOULD BE ALLOWED TO TAKE TIME! | 240 | ||
| DATA ANALYSIS SHOULD BE PERFORMED WITH CARE AND CAUTION | 240 | ||
| DESCRIPTION OF THE WSP SYSTEM | 241 | ||
| CFD ANALYSIS OF SLUDGE AND HYDRAULIC PERFORMANCE OF A WASTE STABILISATION POND | 241 | ||
| TRACER EXPERIMENT | 242 | ||
| SLUDGE MEASUREMENT | 243 | ||
| CFD MODELLING | 244 | ||
| REFERENCES | 245 | ||
| PREDICTIVE CONTROL OF A WATER SUPPLY SYSTEM IN THE NETHERLANDS | 246 | ||
| 7: TRENDS | 251 | ||
| 1. DECENTRALISATION | 253 | ||
| 2. WATER RE-USE | 254 | ||
| 3. UTILITY INTEGRATION | 255 | ||
| 4. SYMBIOSIS | 256 | ||
| 5. COMMUNITY INVOLVEMENT | 258 | ||
| Private consumers | 258 | ||
| Industrial consumers | 259 | ||
| Agriculture | 259 | ||
| Local authorities | 259 | ||
| Nature | 259 | ||
| 6. CLIMATE ADAPTATION | 259 | ||
| 7. WATER SCARCITY | 261 | ||
| 8. WATER AND ENERGY | 262 | ||
| 9. MICRO-POLLUTANTS | 264 | ||
| 10. WATER PRICING | 265 | ||
| 8: NEW PERSPECTIVES | 269 | ||
| SMARTENING THE ENERGY–WATER NEXUS | 270 | ||
| MEASURING SMART GRID PERFORMANCE | 270 | ||
| SMART WATER | 271 | ||
| THE OPERATIONAL NEED FOR “SMART OP ERATION” | 272 | ||
| CONVERTING NEED INTO REALITY – BACK TO BASICS | 272 | ||
| What does this actually look like in reality though? | 272 | ||
| IN REAL TERMS? | 273 | ||
| THE FUTURE AND WHAT IT MEANS | 273 | ||
| BUT, WHAT DOES THIS MEAN FOR THE OPERATOR? | 273 | ||
| AND THE BUSINESS? | 274 | ||
| WHY NOT NOW? | 274 | ||
| FUTURE INNOVATION IN THE FIELD OF WATER TECHNOLOGY | 275 | ||
| A UNIQUE R&D COLLABORATION | 275 | ||
| TREATING SEPARATED WASTE STREAMS | 276 | ||
| RECOVERING RESOURCES FROM WASTEWATER | 276 | ||
| ENABLING THE USE OF NEW WATER SOURCES | 276 | ||
| INTENSIFYING THE USE OF UNDERGROUND ASSETS | 277 | ||
| MEETING WATER CUSTOMER DEMANDS USING (THE RIGHT) DATA | 278 | ||
| THE VALUE OF REAL-TIME CUSTOMER DATA | 278 | ||
| THE VALUE OF REAL-TIME UTILITY DATA | 278 | ||
| TRADITIONAL VS . SMART WATER SOLUTIONS | 279 | ||
| BRIDGING THE INFORMATION GAP | 280 | ||
| LOOKING TOWARD THE FUTURE | 280 | ||
| REFERENCES | 280 | ||
| PERFORMANCE OF WATER UTILITIES BEYOND COMPLIANCE | 281 | ||
| THE PERCEPTION OF WATER | 284 | ||
| NEW MODELS | 284 | ||
| PIONEERING NEW TECHNOLOGY IN THE WATER INDUSTRY | 286 | ||
| URBAN METABOLISM AND SMART WATER SYSTEMS | 289 | ||
| BUTTERFLIES AND A NEW LEADERSHIP PHILOSOPHY | 291 | ||
| THREE FUNCTIONS – ONE GOAL | 291 | ||
| WHEN FLOW, FLEX AND FORM WORK AS ONE | 292 | ||
| CREATING THE CONTEXT AND FLOW – SERVANT LEADERSHIP | 293 | ||
| CREATING THE CONTENT AND FORM – PERSONAL LEADERSHIP | 294 | ||
| CREATING THE CONNECTION AND FLEX-FACILITATOR | 295 | ||
| FROM A SINGLE-CELL ORGANISATION TO A SELF-ORGANISING ORGANISM | 295 | ||
| IMPLEMENTATION | 296 | ||
| REFERENCE | 296 | ||
| 9: FINAL REMARKS | 299 | ||
| ACKNOWLEDGEMENTS | 304 |