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.

Questions/Discussion

Russell Ford, PhD, PE, BCEE –russell.ford@jacobs.com

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