intelligent analysers for control and optimization of wastewater treatment plants

MMEA Final SeminarHelsinki 26 November 2015

Intelligent Analysers for Control and Optimisation of

Wastewater Treatment PlantsEsko Juuso

Control Engineering Group, Faculty of

Technology

University of Oulu, Finland


Measurements Applications• Basis: Measurements

– Microwave, image analysis, LED, ...– Sampling, dilution, cleaning, uncertainty– Towards online and process

• Pipelined data analysis – Scaling functions, recursive updates

• Intelligent analysers (soft sensors) Decision support & operating conditions Modelling with specific submodels

• Integration to automation & risk analysis

• Smart services

0 10 20 30 40 50 60 70 80 90 100-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Susp

ende

d so

lids

Measuredk-fold cv


Monitoring and control• Image analysis of floc morphology• Data analysis: nonlinear scaling indices, limits• Intelligent indicators

– Scaled values [-2, 2]– Trend indices

• Plant performance: long windows • Control + DSS: short windows

• Traffic lights – Levels &Trends

• Cases: wastewater treatment (WWT) plants– Pulp mill WWT (Stora Enso, Oulu)– Municipal WWT (HSY),

• Automation systems Online (Valmet)

UEF


Measurements Control

• Data analysis – Limits

• Trend analysis– Changes– Trajectories

• Uncertainty

Detection of operating conditions- system adaptation

- fault diagnosis, maintenance, - performance, quality

Dynamic simulation- controller design, prediction

Intelligent analysers- sensor fusion

- software sensors- trends

Intelligent control- adaptation

- model-based

Measurements- on-line analysers

- DSPIntelligent actuators

- model-based

Intelligent analyser(Software sensor)

Controller

Stricter requirements

Capacity increaseFluctuations

Intelligent + MPC


• A generalised norm

• Special cases. Min ... Max

– p=1, Arithmetic mean– p=2, Standard deviation, rms value

• Variable specific Pipelined analysis• Recursive updates

Data analysis: generalised norms

p is a real numberSeparately for each variable x j


Generalised moments Limits• Skewness (k=3)

– Positive– Symmetric– Negative

• Generalised moment

03

03 03

kX

k

p

p

k

MXE

)(

Define orders p for central values

- Linguistic levels translated into numbers- Levels represented by natural language

Levels of adaptation with time:- Recalculate

norms- Update orders of

the norms

Scaled values [-2, 2]


Shortage of nutrientsToo much nutrients

High oxygenLow oxygen

High temperatureLow temperature

High flowLow flow

Very good

Low reduction

Settling problems

Very good

Warnings

Process states & Limits Traffic lights

All variables with scaled values [-2, 2]


Trends calculated from scaled values [-2, 2]

k

nkij

L

k

nkij

S

Tj

LS

kXn

kXn

kI )(1

1)(1

1)(

Change of trend index

Trend index


Trends in treatment results Traffic lights

Lowreduction

Settlingproblems

Very good

Very good


Dynamic models

Water treatment

Fuzzy LE blocks

BioMass

Load

- Load- Nutrients- Oxygen- Temperature

Condition ofthe biomass

All variables with scaled values [-2, 2]


Measurements Automation Risk Management

Measurements

Intelligent analysers

Risk Management

EnvironmentWeatherHydrological forecasts

Processes Data processing chain• Data quality• Uncertainty

handling• Anomaly detection

Open data (weather)


Wastewater Treatment

ControlDecision makingControl

Decision makingProcess controlDecision making

ModellingRisk identification

Risk analysisHigh-level control

Diagnostics

Performance analysisEnviromental measurementsAdaptation

Intelligent analyser(Software sensor)Intelligent analyser

(Software sensor)Intelligent analyser(Software sensor)

Measurementtechnology

Process

Measurements

Modelling

Open data

Data Management- Data Sources- Processing - Testbed

Weather observations & forecasting

Quality control & anomaly detections

Data operator & data sources

EE Services Concepts Monitoring and management framework

for early risk management

EE Management- Monitoring methods- Decision Support

Environmental management

Monitoring methods and tools

Decision support systems

Remote Sensing- Radar- Lidar- Airborne

Measurements• - Solution studies• - High performance • on-line monitoring Fouling and

contamination

Wastewater measurements

Atmospheric boundary layer sensing with radar,

lidar & airborne instruments

Low-cost sensors in water analytics

Probabilistic nowcasting

Process insight, weather, water balanceIntelligent analysers: traffic lights, trendsNew measurements

Globalmarkets

Situation awareness


Smart Applications in Industrial Internet

Thing

Thing

Thing

Act!

Store, analyse, refine,

etc.

Local integrated applications!

Utilise!

Local processingEfficient local useLess data transfer

Digitalisation & Clouds

Things

Services

ControlDecision makingControl

Decision makingProcess controlDecision making

ModellingRisk identification

Risk analysisHigh-level control

Diagnostics

Performance analysisEnviromental measurementsAdaptation

Intelligent analyser(Software sensor)Intelligent analyser

(Software sensor)Intelligent analyser(Software sensor)

Measurementtechnology

Process

Measurements

Modelling

Open data

GSPC & Trends

I2oS3IoT

Experts


Conclusions• Measurements real-time use

– Sampling Laboratory analysis (uncertainty)• Compact solutions for intelligent analysers• Combine measurements + trend analysis +

GSPC• Monitoring Control & Optimisation• Forecasting + Risk identification• Situation awareness• Smart services

EnvironmentWWTP

Active sludgeOpen data

Participatory observations