Using Digital Twins to Solve
Operational Challenges
Russell Ford, PhD, PE, BCEE
Global Director – Drinking Water and Reuse Solutions
▪ “A virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics. Digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system...” (Siemens, 2019)
▪ Three types− Layout – a multi-dimensional representation of the assets (3D plus costs, schedule,
etc.)
− Process – a “flight simulator” for facilities and infrastructure
− Data-Driven – includes large quantities of data with analytics to improve system understanding and predict performance
What is a Digital Twin?
https://www.plm.automation.siemens.com/global/en/our-story/glossary/digital-twin/24465
Why Are People Interested in Digital Twins?
▪ Still balancing the same drivers
Quality/Performance
Standardize approaches
Higher performance for lower cost
Cost
Provide certainty early in project cycle (no surprises)
Schedule
Accelerate where possible
• Utilities have mountains of data
and are looking to maximize value
• Speed up decision making
• Do more with less
Will Digital Twins Become Important to the Water Industry?
▪ By 2021, Gartner predicts that half of large industrial companies will use digital twins, driving 10% improvements in system effectiveness.
▪ Multiple sources have projected Digital Twin applications (across all industries) will grow at greater than 30% annually in the coming 5 years.
Source: https://www.grandviewresearch.com/industry-analysis/digital-twin-market
Benefits of a Digital Twin of a Complex SystemSimulate all aspects of a new or existing process system allows for more in depth knowledge and exploration which leads to cutting-edge solutions and more informed decision making.
− Test hypothesis in a safe, low cost environment
− Improved system understanding, and communication, by many stakeholders
− More robust solutions
− Reduce operational risks
− Reduce start-up risk and schedule
− Increase facility performance efficiency
Digital Twins in the Water Market
Consider How Digital Twins Could Touch All Parts of the Water Cycle
Analytics and Optimization can be wrapped into
the solutions to further enhance capabilities
Leveraging Data Analytics Further Enhances Digital Twins
▪ Outlier detection – identify anomalous data− Clustering: k Nearest Neighbors (kNN)
− Probabalisitic: Stochastic Outlier Selection (SOS)
− Neural Network: Single/Multi-Objective Generative Adversarial Active Learning
▪ Infilling− SCADA data – backfill tagged outliers, missing data
− Lab data – use seasonality models, autocorrelation, cross correlation to interpolate and sub-sample between lab measurements
▪ Process Deviations− Utilize short term digital twin forecast models to determine
anomalous process patterns
Layout Digital Twin Project Example
Layout Digital Twins
▪ Facilitates development of informed designs for drinking water, wastewater, and industrial water treatment and conveyance facilities
▪ Unit process general arrangement drawings and design criteria are used to quickly and accurately generate detailed outputs:− Capital and Life Cycle Cost Estimates
− 3D models of Unit Processes
− Environmental Impact Estimates
Basis Drawing
Scaled Preview
CAD
Construction
Layout Digital Twins Enhance Communication and Decision-Making
Schedule, Cost, Operations and Maintenance
▪ A complete layout digital twin leverages the data available on projects throughout the project life cycle
12
Process Digital Twin Project Examples
Process Digital Twins Can Simulate Performance Dynamically
Process
• Track components
– Treatment processes
– Separation
– Reactions
• Linkage
‒ External process platforms
FLUID DYNAMICS
PROCESS I
&
C
COMPLETE DYNAMIC PROCESS MODEL
Fluid Dynamics
• Move fluids through system
– Pipes
– Pumps
– Valves
– Storage
– Channels
Instrumentation & Controls
• Drives system operation
– Measuring devices
– Transmitters
– Control Algorithms
– Controls Tuning
• Linkage with Control Software
– External control software
OPTIMIZATION
New AWTP
Expand Water Reclamation Plant
New WW Conveyance
Pipeline to Reservoir
Phase 1 provides 30 MGD of purified water for reservoir augmentation
City of San Diego Pure Water Program will supply 1/3 of the City’s drinking water by 2035
Pure Water System involves complex dynamic interactions between multiple facilities
…
• Influent Pumping
• Primary Sed
• EQ/Int. Pumping
• 2-stage Bio
• Secondary
• Tertiary Filtration
• PWF Pumping
• Chlorination
• RW Pumping
• Ozone
• BAC Filtration
• Microfiltration
• Reverse Osmosis
• UV/AOP
• Pure Water Pumping
Pure Water Facility
Water Reclamation PlantMiramar Reservoir
Recycled Water System
Morena Pump Station
PS-1
PS-2
0
10
20
30
40
50
0 4 8 12 16 20 24
Flo
w (
MG
D)
Water Quality Criteria
Constant Flow Demand
RW Demand and Storage Capacity
Internal Waste/Recycle Flows
Raw Sewage Flows
Treatment & Storage Capacity
Creating a dynamic simulation model to address the challenges and provide confidence in the design
• Integrates hydraulics, process and controls based on design drawings, equipment information, and control narratives
• Confirm hydraulic design and validate control strategies
• Additional models for WRP and RW system
• Demonstrate interactions between various systems
• Develop overall system flow control strategy
• Refine initial setpoints and tuning parameters
• Control system testing and operator training
Pure Water Facility Digital Twin
Pure Water Facility - Microfiltration
Water Quality and Process Simulation
Objectives:
1. Evaluate equipment performance and control loops with varying water quality
2. Test control logic for recycled water system blending
3. Evaluate recycle flow water quality
TDS spike also causes an increase in RO feed pressure
RW Measured TDS
Tertiary effluent TDS
Pure Water TDS
RO Feed Pressure increase ~10psi
TDS Spike
Facility flow control strategy
Challenge: maintain a constant flow at RO using wet wells to attenuate fluctuations in MF and BAC flows
Consistent RO Flow
Frequent MF Reverse Filtration
Intermittent BAC Backwashes/Bumps
Operator Training ▪ Connect SCADA HMI to simulation model instead of plant
▪ Pre-program several different scenarios to train operators
▪ Can run faster than 1:1 time-steps to expedite training
▪ Customizable interface resembling HMI screens
TrainerTrainee
Control System Testing
▪ Connect simulation model to programmed control system logic
▪ Test logic on model before commissioning
▪ Accelerates startup by having initial tuning parameters and strategies in place
PLC
PLC INPUTSFlowsLevels
Pressures
Hydraulic simulation
Control logic
Information shared every time step (0.25-1 sec)
PLC OUTPUTSPump Speeds
Valve PositionsOnline Trains
Summary
▪ Pure Water System is highly complex interaction between multiple facilities
▪ Digital twin improves our understanding of the system and how to operate it
▪ Evaluate control strategies and efficiently test scenarios
▪ Optimize process control to reduce operating cost
▪ Can be evolved with as system comes online
▪ You can’t break a digital twin
▪ Melbourne, Australia
▪ Peak demand 600 MLD (158 MGD)
▪ Originally constructed 1980
▪ Upgrade to post-filtration UV disinfection system
Winneke Water Treatment Plant (WTP)
UV System design
▪ UV sized for PEAK flow− Peak instantaneous flow versus maximum daily
production
▪ Modifications to existing control strategy
50 ML/d fluctuation
around set-point,
100 ML/d change in
less than 10 minutes
Winneke WTP
• Challenges
– Lack of flow stability requires larger UV system, increasing capital and operational costs
– Difficult and unique control strategies required to stabilize filtered water flow
• Approach
– Model existing filtered water flow control strategy
– Verify proposed control strategy
Hydraulics
Controls
Analysis
Historical Filtered Water flow fluctuations vs Model prediction
Model Calibration
Controls Analysis
▪ Evaluated new controls with calibrated scenarios
▪ New controls address flow fluctuations − Filter flow set point independent of
Settled Water (SW) Channel Level controls
− Backwash Supply Tank (BWST) refill method
Filter Water FlowNew controls vs Old controls
• Stabilized filtered water flow to clear water reservoir
• Zero downtime at start up
• Currently flow fluctuations < 10 MLD
• Previous flow fluctuations up to 50 MLD
Start Up
Post-upgrade
Eliminated flow peaks
Smaller UV System needed =
CAPITAL COST SAVINGS of $2.5 million
• Dynamic Simulation provided:
• Dynamically Factory Tested new PLC code
• Stabilized filtered water flow to clear water reservoir
• Reduced capacity needed for new UV system
• Capital cost savings of $ 2.5 million
Winnike WTP Conclusions
Data-Driven Digital Twin Project Example
Melbourne Water Project Example
• Machine learning can be used in a practical application to optimize coagulant
dosing in drinking water treatment.
• Samples:
– Conventional drinking water treatment plant consisting of clarification followed by rapid gravity filtration.
– Three source waters were combined in different ratios to produce blended samples of varying water
quality. In addition, samples were collected from the clarified water and filtered water channel to
characterize the actual plant performance.
• Jar Tests
– Conducted to create dataset to understand particle removal
Data and Machine Learning
▪ UV Absorbance− The UV absorbance was measured for each jar test and each treatment plant sample using a
multi-wavelength UV VIS spectrophotometer. Absorbance was measured at 2.5 nm increments from 220 to 730 nm.
▪ Zeta Potential− measured for selected number of settled water samples including both jar tests and plant
samples
− measured for varying coagulant dose and pH to estimate the point of zero charge
▪ Machine Learning− Coagulation model inputs limited to include raw water characteristics, coagulant dose and
coagulant pH to allow the model to be used in a predictive, feed forward control scheme.
− The model was trained to predict two variables 1) based on differences between the raw and dosed filtered water, and 2) based on differences between the raw and dosed settled water.
Results
▪ Optimization− A multi-objective optimization algorithm was applied to the model output to select the optimal
combination of coagulant dose and coagulation pH, that maximizes organic and suspended solid removal, and minimizes treatment cost.
Conclusions and Wrap-Up
What Part of the “Digital Twin” Concept is New?
▪ Not New− Application of process and layout digital twins in water and
wastewater facilities for 20+ years
▪ New− The creation of more sophisticated process models that incorporate
hydraulics, controls, and process performance
− Layout understanding is increased with more data and centralized repositories for 3D arrangements, schedules, cost, O&M manuals, etc.
− Internet of Things, increased numbers of sensors and increased data are creating both challenge and opportunity in the industry
▪ Data analytics, and Machine Learning is not just a “boutique” science left for other industries any longer
Digital Twin Thoughts
▪ Water industry is witnessing a contraction in skilled operations labor while regulations and treatment technologies are becoming more complex.
▪ The next generation of treatment plant operators grew up with Xbox, PlayStation, smart phones, etc.
Digital Twin Thoughts
▪ Process digital twins can be set up as “advisory” for operations support, or with various levels of direct control allowed− Truly a next generation beyond advanced controls
− And, it can require fewer sensors (to buy and maintain) than advanced controls since it looks at the whole system and not just pieces.
▪ The next technology leap in the water industry will be the application of digital twins (layout, process, and/or data-driven) to enhance operations and maintenance, reduce risk, lower costs, and improve water quality all in a predictable manner.
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