A Perspective on Future Automation Systems in Context of Process Systems Engineering

Research Group of Process Control and Automation

Prof. Sirkka-Liisa Jämsä-Jounela, Vice Dean of Research

PhD Alexander Smirnov

Aalto University; School of Chemical Engineering, Espoo, FINLAND

Research Group of Process Control and Automation

Prof. Sirkka-Liisa Jämsä-Jounela

Vice Dean (Research)

Dr. Jukka Kortela Rinat LandmanJose Valentin

Gomez FuentesDr. Alexander Smirnov Dr. Bei Sun

Antton Lahnalammi Nelli Pauliina Jämsä

Dr. Yancho Todorov

Iiro Harjunkoski, Adjunct Professor

(ABB Ladenbourg)

Babak Nasiri

Dr. Maryam Mohammadi,

Sugam ShuklaKabugo J. Clovis

Maarit Tihinen, Guest Lecturer

(Digitalization in Process Industry)

General TrendsGlobal Competition &

Sustainable Development

Research & Development• Process Design• Process Control• Process Operations

Business Competences• Specialized Products• Competitive Edge• Profitability• Flexibility

Digitalization

Cyber-physical Systems

Cloud Computing

Big Data

Industrial Internet

Networked/Shared economy

Blue economy

Green economy

Bio-economy

Circular economy

Technological Trends PSE Driving Forces

Aim is to embed the relevant Chemical Engineering knowledge via mathematics

and programing to process equipment, Unit Processes, Plants etc in order to increase profitability, safety and to decrease environmental effects

Global trend –4th industrial revolution

We Face a Tremendous Transition in the structures and operation of Automation Networks and Hierarchy

The traditional automation pyramid and control hierarchy with structurally separated control,

scheduling and planning to their own hierarchical levels have come to their end

Process (Level 0)

Regulatory control (Level 1)

Supervisory control (Level 2)

Operations mgt (Level 3)

Business Planning and Supply Chain (Level 4)

Sensors, Actuators and Hardware

Control Systems, PLC, DCS and

iPC

SCADA Systems

MESManufacturing

Execution

Systems

ERP

CPSIoT

Industry 4.0

5G

IIoT

New research directions in process system engineering

motivated by system architectural innovations

Networked Control (Control of network and control over network; systems of

systems)

End of Isolated Solutions: all solutions can directly be connected to theinternet/intranet and communicate and exchange data with each other

BIG Data & Process Control & Production Control & Scheduling

From process monitoring towards deep learning and AI3

Three waves of AI

Wave 1: 1980s- Wave 2: 2000s- Wave 3: 2010s-

Hand Crafted SW

Uselful for small problems

• Reasoning

Deep Learning

Classification and Prediction

Lacks object representation

• Perception

• learning

Advanced AIAutonomous learningand symbolic reasonong• Perception

• Learning• Autonomy• Reasoning

It learns while working with you and then works for you

New research directions in process system

engineering motivated by system architectural

innovations

Predictive maintenance, Fault Diagnosis– key themes for Industry 4.0/IIoT

Maintenance offers great potential value in the Industrial Internet of Things

(IIoT)/Industry 4.0

the overall situational awareness of plant operations and equipment condition

can be greatly improved

new FDD algorithms for predictive CPS and/or for cloud manufacturing

environment with big data analytics are needed

9/13/2017Elojuhlat

Case Studies Towards More Flexible and Interoperable Control Systems

Case 1: EU FP6 NECSTNetworked control systems tolerant to faults

First industrial implementation of

fault tolerant model predictive control

Novel mathematical methods for control

of networks and control over

networks

New research area: networked

control

Fault tolerant control for a dearomatisation process

By: Sourander, M.; Vermasvuori, M.; Sauter, D.; et al.

JOURNAL OF PROCESS CONTROL Volume: 19 Issue: 7 Pages: 1091-1102 Published: JUL 2009

Status: 2010

New technologies integrated across diciplines

MPC

Process Data analytics services via cloud computing

Research on standards for information modelling and mgt of process data in theIoT infrastructure

OPC

UA

ISA-

95

ISO

15926 CAEX AutomationML PandIX

IEC

61970

(CIM)

IEC

61850

Hierarchy x x x x x x x x

Aggregatio

n x x x x x x x x

Variables x x x x x x x x

Functions x x - - - - - -

References x x x x x x x -

Classes x x x x x x x Partly

Methods x - - - - - x -

Inheritance x x x x x x x -

Data Types x - x x x x x x

Table 1. Comparison of the information modelling standards

(Mahnke, Gössling et al. 2011, Schleipen 2010, AutomationML consortium 2015, Schuller, Epple 2012)

Status: 2017

Case 2: EU FP7 PAPYRUSPlug and Play monitoring and control architecture for optimization of large scale production processes

Production planning and control

Real-time optimization

Advanced process control

Basic control

Process Control and Optimization

Plant

Unit Processes

Field components

Plant Asset Management

Global development and economical trends

International competition and global business environment

Th

era

py

Ad

apte

d/d

istr

ibute

d

corr

ective

actio

ns

KPIs computation Prognosis

Diagnosis

Diagnosis-FDD

Root cause

analysis

-Data-based methods

-Model-based methods

Fault Tolerant Control

Production planning and control

Real-time optimization

Advanced process control

Basic control

Process Control and Optimization

Plant

Unit Processes

Field components

Plant Asset Management

Global development and economical trends

International competition and global business environment

Th

era

py

Ad

apte

d/d

istr

ibute

d

corr

ective

actio

ns

KPIs computation Prognosis

Diagnosis

Diagnosis-FDD

Root cause

analysis

-Data-based methods

-Model-based methods

Fault Tolerant Control

Algorithms to detect thickness sensor fouling, leakages,

blockages & valve stiction in the drying section

Novel mathematical theory for distributed fault tolerant MPC

for large scale systems driven by KPIs

Status: 2014

Fault analysisMost common fault types and fault locations

Fault type

BOARD MACHINE 4 BreakageClogging,

jammingFouling Leakage

Loosening,

disengagementMalfunction Noise

Other

damageOverheating

Unlisted

Other

damages

Unspecified Vibration Others Total

Stock preparation - 2.5 % - 2.1 % 0.4 % 1.7 % - 1.9 % - - - 0.2 % - 8.7 %

Short circulation 0.2 % 0.2 % - 0.6 % 0.2 % 2.3 % - 0.2 % 0.2 % - - 0.8 % - 4.8 %

Broke processing 0.2 % 1.0 % - 0.8 % 1.2 % 3.3 % - 1.2 % - - - 0.2 % - 8.1 %

Wire section - 1.7 % - 2.5 % 1.2 % 2.7 % 0.8 % 2.5 % - - - 1.9 % - 13.2 %

Press section - 0.4 % - 1.7 % 0.6 % 2.1 % - 2.1 % - 0.2 % - 0.6 % - 7.6 %

Drying section - - - 3.5 % 0.8 % 2.5 % 0.2 % 1.7 % 0.4 % - - - - 9.1 %

Calender section 0.2 % 1.4 % - 1.9 % 1.0 % 4.5 % 0.2 % 2.3 % 0.2 % - - 0.4 % - 12.2 %

Reeling - 2.5 % - 2.3 % 1.7 % 4.1 % 0.2 % 1.7 % - 0.2 % 0.2 % - - 12.8 %

QCS - - 2.3 % 0.2 % 0.2 % 16.1 % - 0.8 % 0.8 % - - - 3.1 % 23.6 %

Total 0.6 % 9.7 % 2.3 % 15.5 % 7.4 % 39.3 % 1.4 % 14.3 % 1.7 % 0.4 % 0.2 % 4.1 % 3.1 % 100.0 %

Distributed FDD system for the board machineOverview

Distributed FDD system for the board machine

Process monitoring level

SOM for thickness sensor fouling

Detection of periodic disturbances in quality variables caused by abnormal behavior of control loops

Process unit level

FDD for the drying section (clogging, jamming, and leakages of valves; condensate problems)

SISO level

FDD for valve stiction

FDD for malfunctions of the consistency sensor

Outline of a fault diagnosis system for a large-scale board machine

By: Jamsa-Jounela, Sirkka-Liisa; Tikkala, Vesa-Matti; Zakharov, Alexey; et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY Volume: 65 Issue: 9-12 Pages: 1741-1755 Published: APR 2013

Process

Data driven analysisP&ID

XML schema

Connectivity matrix

Causality matrix

Fault Detection

Propagation path

Hybrid approach to casual analysis on a complex industrial system based on transfer entropy in conjunction with process connectivity information By: Landman, R.; Jamsa-Jounela, S. -L. CONTROL ENGINEERING PRACTICE, Volume: 53 Special Issue: SI Pages: 14-23 Published: AUG 2016

V4

V3

V8

V5

V7

V9

V6

V2

V1

Operators’ dream

Utimate goal: Detect faults, analyze the propagation path using process and plant causality and finally display the propagation path on operator’s screen

Plant monitoring as a service

Diagnostic

service• Tool for fault analysis

• Failure diagnosis

• Failure propagation

through plant

topology & flow sheet

Automation

system

Maintenance &

Production databases

Industrial plants

Status: 2017

CASE 3: EU FP7 STOICISM Sustainable Technologies for Calcined Industrial Minerals in Europe

Technology lift-up of the whole production chain from mine to

the specialized product

MINE

Dynamic model for the CalcinerEnhanced MPC Control

Strategy

Budget 8+2 Milj €

Status: 2014

Distributed eMPC based on big data analysis (ore type) for concentrator control

Optimization and Plant monitoring in Industrial Internet

Big Data analysis

Mining Grinding Flotation Thickening

Integrated databank (local)

Suppliers

Services

Customers and End Users

Mining Data Base

MES

Laboratory Data BaseAPS

DCS

OPC-UA

OPC-UA

OPC-UA

Ore type classification

eMPC

Bank of models for different

ore types

FilteringWaste water

treatment plant

Concentrate

eMPC eMPCeMPC

Fresh water

Industrial Internet

Electricity price Metal price

Patent submitted for the concept together with the enduser

Status: 2017

• Entrepreneurship Training: Business opportunities based on data analytics around Outotec’s waste-to-energy plant

CASE 4: MIDICON: Hackathon for

Process Data Analytics

Data Cloud

Industrial Problems from Outotec GermanyEIT RawMaterials: 20 PhD students

from 13 countries

Global trend –4th industrial revolution

We Face a Tremendous Transition in the structures and operation of Automation Networks and Hierarchy

The traditional automation pyramid and control hierarchy with structurally separated control,

scheduling and planning to their own hierarchical levels have come to their end

Process (Level 0)

Regulatory control (Level 1)

Supervisory control (Level 2)

Operations mgt (Level 3)

Business Planning and Supply Chain (Level 4)

Sensors, Actuators and Hardware

Control Systems, PLC, DCS and

iPC

SCADA Systems

MESManufacturing

Execution

Systems

ERP

CPSIoT

Industry 4.0

5G

IIoT

Digitalization –Aalto Industrial Internet Campus (AIIC)

AIIC aims to create a world-class platform for research, education, and innovation in Industrial Internet based on close encounters of manufacturing and ICT researchers and industries.

Factory of Future Set-up in ABio Center

ACMS on cloud

Cloud computing

Data analytics

Sensors / actuators

Hosts

PLCOPC

Aspect

DNS

Database

OPC UA Wrapper

Duplicated

main server

Computer

Dell 730xd

OPC UA Client

Flexible, reconfigurable, scalable, interoperable network-enabled collaboration

between decentralized and distributed cyber-physical systems

Mobile phone/ laptop

Remote Access

Operation

Stations

5G

Rasberry Pi

I/O

Gateway

Hosts

Virtualization

serversTraining and

learning

1

23

4

5 6 7

ABB 800xA

EDU

PLANT

8

CONCEPT FOR A-BIO PROCESS DATA analytics &CONTROL VALLEY

5G

CPS

IoT

Industry 4.0

IIoT

ABB

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