Modellingand Control of Waterand ...

Anna Mikola TkT D Sc (Tech)

Modelling and Control of Water andwastewater treatment processes

WAT - E2130

Lecture outline

Course team introductionParticipants’ introductionLearning outcomesContent and assessment of thecourse- Lectures & exercises- Modelling project work- Excursion- Exams

Introduction lecture:Introduction to modellingand simulation

Hands-on SUMOFirst introduction exercise

Lecturer Anna Mikola• Working experience:

• 3 years at Nopon Oy• Researcher at HUT/Aalto• 18 years with a consultant

(Kiuru&Rautiainen Oy,Ramboll Finland)

• Post-doctoral researcher atAalto 2013-2018

• Lived 5 years in Berlin, 4children

• Visiting researcher in INSAToulouse in 2017

• Professor of Practice since2018

14.4.20203

• M.Sc. From HUT Water lab1999

• Exchange year in France atENCR 1994-1995

• D. Sc. (Tech.) Spring 2013Dissertation: The effect offlow equalizationa andprefermentation on BNR

Lecturer for the control partMichela Mulas

Professor at the FederalUniversity of Ceará,Department of TeleinformaticsEngineering in Fortaleza, Brazil

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The course teamCourse assistant: Sara Saukkonen

Lab staff (for IT issues): Heikki Särkkä andAntti Louhio

Guest lecturers:Dr. Ewa Zaborowska GTUKristian Sahlstedt PöyryTeemu Koskinen RambollHenri Haimi FCGPasi Puranen Hyxo

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Participants’ introduction1 minute each containing e.g.- Background?- Experience with process

modeling and control- Expectations for this

course?

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Learning outcomesUpon completion, the student should be able to:• Understand the overall process train as well as selected process performance, the

definition the main disturbances for the process operation and the identification of theprocess dynamics

• Be able to describe the modelling and control techniques: state-of-the-art models,basic controllers and their practical application to full scale processes

• Recognise the instrumentation available in the plants: actuators, on-linesensors/analyzers, structure of the automation system and their representation on thepiping and instrumentation diagram

• Optimise plant operation in terms of resources consumption and effluent qualityimprovement

• Analyse and understand the on-line and off-line data available at the treatment plants• Design the automation system for the treatment plants by means of simulator software

• Understand the role of a process modeler or control system designer in the treatmentprocess construction or operation teamId

entit

y

Skill

Kno

wle

dge

Why process modelling and controlcourse?• Models are important tools in process development, design,

optimization and operation.• Modelling allows to understand more complex systems and

process trains• Process operation and control are crucial parts of well

performing processes – a well-designed plant can be verybadly operated!!

• Process engineers tell automation engineers what thecontrol system needs to do


8

Timeline for the course

Lectures Intro * * Excu * * * * * * * *Exercise DLs HW 1 HW 2 HW 3 HW 4

Exams SUMOquiz

Exam 1 Exam 2 Extra 1&2

Modellingproject work

Selecttopics

Questionsto Porvoo

Modellingtask

Controltask

Presentation Clientpresentation

Presentations

Written report 1st draft+feedback

Finalreport


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Week 16 17 18 19 20 21 22

HW 1 SUMO quiz HW 2&3 HW 4

Project Topics C&C1 Excu C&C2 OutcomesPres&Rep

Course execution • Two mid-term exams• 60 min at the beginning of two

sessions (8.5. & 22.5.)• Simulation project work

• Individual work with pairdiscussions

• Create your own virtualtreatment plant, HermanninsaariWWTP

• Demonstrate a specific operationand process control study withyour process

• Presentation of the work andresults (Final session on Wed27.5.)

• Meeting with Hermannansaaripeople Friday 24.4.!!!


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• Lectures and exercises• Lecture sessions: 3.5 hours

Wednesday afternoon and Fridaymorning

• Each session will be divided intoseveral interactive lectures, andhands-on exercises with SUMO

• Four homework exercises• Additional optional sessions for

homework assignmentsMondays between 10 and 12in Teams!

• Get to konw SUMO quiz – tobe finished in MyCourses,grading pass/failed

Communication§ MyCourses -page§ Lecture material available mostly before the lecture§ Instructions for homework assignments§ Submission of home assignments & grades§ Information and submissions for the modelling project

• Communicating– Whole course: MyCourses & email– [email protected], [email protected],

[email protected]

mailto:[email protected]

mailto:[email protected]

Course grading• 30 % mid-term exams - 2 exams 20 points each• 40 % individual project (presentation and written report)

- 1/3 from the presentation, 2/3 from the written report- Grading scale 1 – 5- Based on assessment of Michela and Anna

• 30 % homework exercises + activity during the course- 4 exercises, 80 points total- Bonus possibility up to 0.5 grade when attending the lectures- NOTE!! Late submission – 1 weekà 50% off, moreà 100%

off


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Grading thresholds:1-40% of total points 2-52% 3-64% 4-76% 5-88%

Workload

Changes from last year to reduce the workload:- Lectures 11à 10- Changes in the exercises, HWs published earlier- Improved assignment documents

Learning activity Workload calculation(hours)

Remarks

Lectures 24Excercises 20Home assignments x4 20 5 hours per homework

assignmentReading materials 30 5-10 pages for most of the

sessions Project work 16 8 hour for computer simulations,

8 h for reporting and preparingthe presentation

Midterm exam (2x) 10 5h preparation for each mid-term exam

Independent reflection 15In total 135

OUR CASE STUDY PLANT –HERMANNINSAARI WWTP IN PORVOO

Project work tasks prepared onthe basis of this plant

Friday 24.4. during the courseteaching session meeting withElina Antola from Porvoo

Special focus on instrumentsat the plant and processcontrol


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PEDAGOLOGICAL TRIAL FOR THEPROJECT WORKJIDEA: Include professionalcontext and role games intoteaching

IN PRACTICE:- Playing the role of client and

consultant in pairs- Two C&C meetings during

the project:- 1) Introduction of the topicà

questions for Porvoo- 2) Testing/training for the

final presentation- From C&C meeting 1 prepare

a Teams or Zoom recording(about 10 min per topic)

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Course introduction / Lectures and labwork


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Course booksRieger L., Gillot S., Langergraber G., Ohtsuki T.,Shaw A.,Takacs I., Winkler S. (2012) Guidelines for UsingActivated Sludge Models

Biological wastewater treatmentAuthor(s) / Editor(s) Henze, Mogens;Loosdrecht, Mark C. M. van; Ekama, George A.;Brdjanovic, Damir Publisher IWA PublishingCopyright Date 2008ISBN 978-1-84339-188-3Electronic ISBN 978-1-68015-582-2

Olsson, G., Nielsen, M., Yuan, Z., Lynggaard-Jensen, A., Steyer, J.P.(2005). Instrumentation, Control andAutomation in Wastewater Systems,IWA Publishing, London.


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SUMO introduction


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SUMO history at the lab


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Important to do after the introductionlecture

Install SUMO to your computer!If you have problems contact Sara or Anna!

Start to think about your project topic!!

You can read chapters 14.1. and 14.2. from the coursebook (Biological wastewater treatment)


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Introduction tomodelling


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What is a model?


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Key definitions

A model is a systematicrepresentation of a realsystem

SystemInputs Outputs

Identification

Model

Disturbances

Simulation

What is a model?

The system is a defined partof the world. The interactionswith the environment (the restof the world) are described bymeasurable inputs and outputsignals and the disturbances

Key definitionsModels in general give a formal description of the system and provide arepresentation of what we consider the essential aspects of that systemin a usable form. They can be very different.

Mental models

Give an explanation of someone’s thoughtsabout how something works in the real worldwithout involving any mathematicalrepresentation

Graphical modelsProvide a convenient way to describe theproperties of a process by means of tablesor plots

Mathematicalmodels

Describe the relationships among the systemvariables in terms of difference ordifferential equations

Key definitions

Key definitions

A full understanding of the nature of thesystem can identifying and describing thephysical, chemical and biological laws thatgovern the system.

High interdependence of the state variablesmakes the calibration a difficult task

Structured models

Key definitions

The model development is mainly driven bymeasured data from the actual system. Itsmain advantage is that highly accuratemathematical models without detailedknowledge of the system

The accuracy of the model relies on thequality of the data

Unstructured models

Key definitions

Physical interpretable parameters that arepossible to be estimated by means ofstatistical methods

The potential advances include a reduceddemand of experimental data and morereliable extrapolation

Hybrid models

Uses of wastewater modelsWhat is the “perfect” model?

What are the characteristicsof a perfect models?

The simplest model which does the job

Science-based to the extent possible,with judicious use of empiricalknowledgeInclude only what is essential giventheir intended use

Consistent with existing and evolvingpractice

Adaptable as both knowledge andpractice requirementsevolve

G. T. DaiggerWater Science and Technology 63 (3) 516-526, 2011

Are there too many parameters in ourmodels?

• 1 parameter model to get to themoon

k

k

k1,k2

• 3 parameter model to get to themoon

Are there too many parameters in ourmodels?

Uses of wastewater models

Why models? Mathematical models are useful andnecessary for:

q System understanding: to obtain and enlarge insight in differentphenomena (ranging from physical laws to economic relationships)

q System design: simulation and operator trainingq Process control: to quantify the effect of the manipulated variables

and disturbances on the controlled variablesq Process monitoring: to obtain a template of the process under

normal conditions or to perform root-cause analysisq Soft-sensing: to estimate state variables that cannot be easily

measured in real time on the basis of the available measurementsq Optimization: to identify constraints among decision variables and

make optimal decisions

Model building exerciseSelect the model purpose: required modelaccuracy, model boundaries, …

Determine the system parameters: equipmentsizes, volumes, process topology, …

Develop a set of axioms or descriptionof the process

Evaluate the a priori knowledge of theprocess and postulate themechanisms within the process

Test the constructed model by simulations toanalyze/verify its behavior (at steady-state anddynamically)

Modelling platforms

Programming languages

Commercial software

q Matlab/Simulinkq C++q Fortranq Pythonq ...

q Sumo – Dynamitaq GPS-X – Hydromantisq Simba – Ifak systems GmbHq West – Mike powerd by DHIq BioWin – Envirosimq ...

Modelling platforms

q Interfaces with models of entire wastewater treatment systemsq Able to model entire wastewater treatment plantq Incorporates wide range of unit processesq Flexible and adaptable

§ Accommodates range of unit process models§ Easily modified units to evaluate potential innovations§ New unit processes easily formulated and incorporated

q Does not require extended learning to at least perform basiccalculations

q Interfaces easily with other engineering automation toolsq Accommodates unit processes with diverse characteristics

Characteristics

G. T. DaiggerWater Science and Technology 63 (3) 516-526, 2011

SUMO – basic principles


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1. Build yourprocessCONFIGURE

2. Choosethe modelàSUMOprepares thespecificmodel

3. Add systemspecific datae.g. volumes,flows…

4. Select the outputforms

5. Select theduration andfrequency -SIMULATE

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