MMEA KeyDemo 17.9.2015

Mika Liukkonen

University of Eastern Finland

Process Informatics

AQuaMiner – Monitoring and modelingplatform for water treatment applications

Prosessi-informatiikan tutkimusryhmä

SijaintiItä-Suomen yliopisto, Kuopion kampus

Know-howDatankäsittely ja tiedonlouhinta,mallinnus, ohjelmistot, ennakoivatjärjestelmät, erikoismittaukset

ReferenssialojaEnergiateollisuus, kemianteollisuus,selluntuotanto, veden- ja jäteveden-puhdistus, elektroniikkateollisuus

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Mallinnus

Data

Ohjelmistot

Mittaukset

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015

PROCESSThings to be monitored:efficiency, performance etc.

WASTEWATERThings to be monitored:quality of wastewater

PURIFIED WATERThings to be monitored:quality of purified water

AUTOMATIONSYSTEM

MEASUREMENTSERVER +DATABASE

AQuaMiner: MonitorOnline application:Efficient detection ofwastewater qualityUsing measurementsand models

AQuaMiner: Modeling ToolEnvironment for developingcondition models (expertsoftware) END USER

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Mobile apps,situationalawareness,

condition reports...

INTELLIGENT MONITORING OF WASTEWATERTREATMENT (AQuaMiner)

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015

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• Stora Enso Fine Paper (Oulu)– The activated sludge treatment plant of the pulp mill treats 300

– 500 l/s of waste water on an average.

• HSY Viikinmäki wastewater treatment plant (Helsinki)– Total flow rate 270 000 m³/d

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Applications

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015

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Introduction to UEF KeyDemo: HSY data sourcesPROCESS MEASUREMENTSStandard online measurements

ADVANCED MEASUREMENTSFlock image data

LABORATORY DATAData from laboratoryanalyses

WARNING LIMITSYellow limitsRed limits

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015

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The AQuaMiner platform

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015

• AQuaMiner platform for water treatment applications hasbeen developed

• Presentation of different data sources in one single monitor– Process measurements, special measurements, lab data…

• Warnings and alarms– In-situ and mobile warnings

• Enables more efficient use of measurements– Direct, simple visual monitoring approach– Analytical properties (process history)– Applicability to provide predictive and derivative variables =>

decision support

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Summary

UEF / Prosessi-informatiikka / Mika Liukkonen 17.9.2015