Information Models and Information Exchange in

Plant-wide Monitoring and Control of Industrial Processes

David H

astbacka

, Petri Kannisto

and Matti Vilkko

Laboratory of Pervasive Computing, Tampere University of Technology,

Korkeakoulunkatu 1, FI-33720 Tampere, Finland

Laboratory of Automation and Hydraulic Engineering, Tampere University of Technology,

Korkeakoulunkatu 3, FI-33720 Tampere, Finland

Keywords:

Information Models, Data Exchange, Interoperability, Industrial Processes.

Abstract:

The efﬁciency of industrial processes depends on how well the processes can be controlled and this affects

the quality, use of resources as well as the environmental impact. Advanced monitoring and control solutions

for large-scale industrial processes require information from different systems. The challenge in integration

is diverse messaging structures and lack of common semantics in exchange of information between related

information systems as well as their human operators. This paper provides a comparison of some of the

existing standards of the domain deﬁning suitable structures. Based on these, a model for data and event

message structures is developed. The approach builds on a separation of concerns keeping the messaging

semantics independent of the transport layer. The requirement is to enable also asynchronous communication

as adapters are often needed in distributed environments with heterogeneous systems and communication

protocols. The developed structures have been found suitable for communicating measurements and events in

industrial process settings as shown by case examples.

1 INTRODUCTION

The efﬁciency of industrial processes, including redu-

cing environmental impact and the use of resources,

is heavily dependent on optimal control (Lamnabhi-

Lagarrigue et al., 2017; Lima et al., 2016). The ad-

vanced monitoring and control solutions, however, re-

quire integration of data and knowledge from diffe-

rent systems involving also humans as operators.

Development of efﬁcient knowledge and informa-

tion sharing practices involves humans and proces-

ses but requires technology to implement. Intero-

perability is required for efﬁcient collaboration (Pa-

netto et al., 2016). Information sharing is based on a

common understanding of semantics, and thus stan-

dards and agreed meanings should be favoured when

developing means to facilitate information exchange

between people as well as information systems con-

veying the information.

In the setting of industrial processes the pro-

duction is often distributed and it may span beyond

the premises of one single plant. As a result, control

and monitoring of the systems are also distributed and

represent a multitude of different systems with hetero-

geneous interfaces and message structures over which

information needs to be exchanged.

This paper deals with information models and de-

velopment of practices for sharing data and know-

ledge related to industrial processes in such environ-

ments. The focus is also on data exchange, communi-

cation and alleviating infrastructure developed to im-

prove system integration. The work is based on re-

search being conducted as part of the H2020 funded

COCOP project (Coordinating Optimisation of Com-

plex Industrial Processes). The aim of the project is to

increase the competitiveness of the European process

and automation industry.

The main contributions of the paper are as follows:

1) Comparison of messaging standards suitable for

communicating data and events in distributed indus-

trial processes. 2) Design of a messaging API focu-

sing on message structures that facilitates integration

of involved information systems. 3) Practical demon-

strations that indicate the beneﬁts and relevance of the

messaging API and the developed message structures.

The paper is organised as follows. Section 2 pre-

sents related work on information models research in

industrial production and industrial process environ-

216

Hästbacka, D., Kannisto, P. and Vilkko, M.

Information Models and Information Exchange in Plant-wide Monitoring and Control of Industrial Processes.

DOI: 10.5220/0006960602160222

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS, pages 216-222

ISBN: 978-989-758-330-8

ments. Requirements for interoperability are introdu-

ced in section 3 with a comparison of some of the

existing standards deﬁning message and data structu-

res potentially suitable for industrial processes. The

designed message structures and their implementa-

tion is presented in section 4, and case examples utili-

sing them are demonstrated in section 5. A discussion

is provided in section 6 before concluding the paper

with future work in section 7.

2 RELATED WORK

Previously the ANSI/ISA-95 standard has been ap-

plied to production control with agents and a ge-

neric manufacturing ontology was developed that

used some concepts from the standard (Georgoudakis

et al., 2006). An ontological framework for decision-

making on the enterprise level has been developed

for plant database information using ANSI/ISA-88

(Mu

noz et al., 2012).

A model-driven ontology approach for manufac-

turing systems has been proposed to improve infor-

mation sharing (Chungoora et al., 2013). Similarly,

LinkedData has been proposed as a solution capable

to connect industrial data by using globally shared

concepts (Graube et al., 2011). Using ontology se-

mantics to analyse industrial systems in engineering

has been studied for example by (Dai et al., 2013) and

astbacka and Kuikka, 2013).

OPC UA (OPC UA Part 1, 2008) is used as an

integration standard with information modelling ca-

pabilities for communication with industrial devices

and systems. The utilisation of OPC UA for a con-

solidated information model in service architectures

for industrial devices has been studied by (H

astbacka

et al., 2014).

Once in operation, industrial processes are depen-

dent on maintenance, and maintenance strategies are

typically proactive rather that reactive (Sharma et al.,

2014) (H

astbacka et al., 2016). For integration and

asset management in maintenance OPC UA has been

studied by (Seilonen et al., 2011) and (H

astbacka

et al., 2014) among others.

3 INTEROPERABILITY WITH

COMMON CONCEPTS

In order to efﬁciently integrate data from different sy-

stems the data structures need consolidation. There

are some standards available for data semantics but

no one solution that covers the needs. In practice this

Table 1: Comparison of data structure speciﬁcations.

means that systems implemented by different vendors

have slightly varying practices.

The implementation of advanced monitoring and

control applications for industrial processes requires

communication from different unit processes as well

as communication to the coordination level. For infor-

mation exchange it means that information also needs

to be shared to the lower level units both from the

coordination layer and other unit processes. In order

to make use of the data in control algorithms, com-

putational models etc. it needs to be unambiguously

understood by all parties.

However, as the information systems vary both in

implementation as well in their purpose they are the-

refore heterogeneous exposing and consuming infor-

mation in different manners. As a result of differing

protocols all necessary information should be convey-

able in the message structure not relying on system

speciﬁc communication protocol features. This in-

cludes, for example, timestamps, quality and reliabi-

lity metadata as communication might need to be me-

diated using asynchronous communication channels

such as message buses.

Table 1 provides a comparison of the standards

that specify potential message structure. Each stan-

dard is discussed in more detail in the following para-

graphs.

OPC UA (OPC UA Part 1, 2008) is a de facto in-

tegration technology for industrial processes. It is be-

sides a protocol also an information modelling envi-

ronment that allows for dynamic discovery of data in

a secure fashion in networked environments. As such,

however, it only provides the basic concepts and data

types, and relies on information models such as com-

panion speciﬁcations for interoperability (OPC UA

Part 5, 2009). OPC UA provides a client-server com-

munication model but its PubSub speciﬁcation ena-

bles the use of a message bus as well (OPC UA Part

14, 2018). With this the scalability and distribution

advantages of a message bus apply even when OPC

UA is utilised. However, compared to a generic mes-

sage bus as the platform, the requirements of OPC UA

Information Models and Information Exchange in Plant-wide Monitoring and Control of Industrial Processes

217

communication reduce the freedom of design related

to messaging and message contents.

The Observations and Measurements (O&M)

standard (Observations and Measurements, 2011)

does not currently have any wide acceptance among

industrial production. Therefore, there are no legacy

systems that would readily use it in their interfaces.

Still, O&M provides an excellent foundation for pre-

senting measurement values. Although the origin of

O&M is in the geospatial domain, the structures and

metadata are highly similar to the industrial domain.

Some of the concepts have different names, though.

For instance, what is called ”feature of interest” in

O&M maps to ”position ID” or similar in an indus-

trial plant.

ANSI/ISA-88 (ANSI/ISA-88.00.01, 2010) and es-

pecially ANSI/ISA-95 (ANSI/ISA-95.00.01, 2010)

deﬁne information structures for manufacturing ope-

rations management (MOM). While ANSI/ISA-88

focus on individual processes and their equipment

structures ANSI/ISA-95 focuses on integration of

manufacturing information systems related to manu-

facturing operations. ANSI/ISA-95 covers schedu-

ling, resourcing, production capabilities and person-

nel, and there is a B2MML (Business to Manufactu-

ring Markup Language) speciﬁcation to serialise the

data structures into XML for that purpose. The data

structure speciﬁcations are loose allowing for exten-

sion, which requires additional speciﬁcation about the

actual data structures utilised.

Not only message structures are important, but the

encoding of measurement is another remarkable to-

pic. For measurement units, there should be a contract

about encoding, because an ad hoc approach would

inevitably lead to multiple encoding methods (such as

the temperature ”Celsius” being either ”C” or ”Cel”).

Conﬂicts are also possible. For instance, consider ”a”

that may mean either a year or an are; also, is ”C” Cel-

sius or Coulomb? Although the context may reveal

the semantics of the unit, ambiguity is rarely positive.

Table 2 presents some speciﬁcations for encoding me-

asurement units.

UCUM (The Uniﬁed Code for Units of Measure)

(Schadow and McDonald, 014s) focuses on unam-

biguous representation of measurement units. The

goal is an extensive coverage of all measurement

units currently relevant in various ﬁelds. The mo-

tivation for the development is the limited coverage

and ambiguity of existing standards and speciﬁcati-

ons. UCUM does not aim to deﬁne an explicit spe-

ciﬁcation of all units possibly. Thus, to enable the

encoding of any kind of unit, UCUM deﬁnes rules.

CML (Chemical Markup Language) (Chemical

Markup Language, 2018) is focused on chemicals,

Table 2: Speciﬁcations to encode measurement units.

Figure 1: Message formats and communication protocols

positioned in the levels of the automation pyramid (the le-

vels are deﬁned in ANSI/ISA-95 (2010)).

and the coverage of the speciﬁcation is low compa-

red to that of UCUM.

UNECE Codes for Units of Measure Used in In-

ternational Trade (Recommendation No. 20, 2010)

provides good coverage. However, some codes are

numeric making some of them difﬁcult to manually

interpret.

UnitsML (UnitsML, 2011) is a speciﬁcation to re-

present units. The main focus is on the schema rat-

her than an actual speciﬁcation of units. Related to

UnitsML, NIST (National Institute of Standards and

Technology) have developed UnitsDB that speciﬁes

units, but unfortunately it is not publicly available.

Figure 1 positions the previously mentioned infor-

mation models and communication channels on the

commonly known levels of the ANSI/ISA-95 hierar-

chy. None of the presented message structures are

sufﬁcient as such to cover all the needs of commu-

nicating industrial process data measurements and

events. They do, however, provide parts that are usa-

ble and they can also be used in combination, which

will be continued in the next section.

KMIS 2018 - 10th International Conference on Knowledge Management and Information Sharing

218

4 IMPLEMENTING MESSAGE

STRUCTURES FOR PROCESS

MONITORING AND CONTROL

To facilitate messaging, a messaging API has been

designed. The current implementation is in C#, but

a subset of it exists in Java. Still, considering that

standardised, platform-independent message structu-

res are utilised, there are no limitations related to im-

plementation techniques. Instead, the fundamental

requirement is to just follow the standard structures.

Therefore, there is no requirement to use any particu-

lar API, but their utilisation saves effort producing the

same output as agreed message structures. When the

API is utilised, the application developers do not have

to work in the level of the concrete message structu-

res but they can concentrate on the application logic

instead.

To implement communication, two aspects must

be covered: a communication protocol that provides

a delivery medium and the structures of messages.

The communication protocol provides part of the in-

teroperability as well as several other characteristics

related to reliability, throughput capacity etc. The

detailed comparison and selection of communication

protocols is out of the scope of this paper. In this work

AMQP has been chosen as the main communication

protocol. The message formats are based on multiple

standards. In the design of the API, it is an intentional

choice to completely separate message structures and

any bindings to the communication protocol. Then, it

is straightforward to utilise other communication pro-

tocols if needed, such as other message buses (e.g.,

ZeroMQ) or HTTP. The utilisation of separate proto-

col and message libraries slightly adds to complexity

(due to separate interfaces), but the ﬂexibility of the

approach is considered more important. Figure 2 il-

lustrates the approach.

The high-level typing of the API message structu-

res is given in Table 3. Each type is explained in the

following paragraphs. Although the table associates

most message types with only one standard, the stan-

dards actually consist of other general-purpose stan-

dards, such as Geography Markup Language (Geo-

graphy Markup Language, 2007). The Sensor Ob-

servation Service (Sensor Observation Service, 2012)

standard directly refers to Observations and Measure-

ments (Observations and Measurements, 2011) as the

actual payload of response messages. In standards

speciﬁcation, such modularity introduces dependen-

cies as well as complexity between the standards, but

it also enables the reuse of speciﬁcation effort and ad-

ded value on previous work. The classes of the API

being developed are illustrated in Figure 3 but the im-

Figure 2: In the API, the utilisation of message formats has

been separated from the communication protocol.

Table 3: The most important types utilised to build messa-

ges.

plementation details of the API libraries are out of the

scope of this paper.

The types have the following uses. Three basic

types are utilised to deliver the fundamental measu-

rement data. First, the Observation type encloses the

metadata of measurements. It speciﬁes ﬁelds that ena-

ble the identiﬁcation of data sources, measurement

methods, data quality and so forth. Second, for the

actual measurements, there are multiple sub-types of

Item. For instance, a single measurement value is en-

closed in an Item Measurement that holds a measu-

Figure 3: Message type classes provided by the API libra-

ries being developed. API libraries facilitate producing and

consuming compatible message structures but are not requi-

red as long as agreed message structures are used.

Information Models and Information Exchange in Plant-wide Monitoring and Control of Industrial Processes

219

rement unit and the related value. For complex mea-

surements with multiple ﬁelds, there is a type called

Item DataRecord. Third, time series data is delivered

with the TimeSeries type. However, to implement

a request-response scenario, further types are neces-

sary. The GetObservationRequest type speciﬁes mul-

tiple ﬁelds to identify what actual measurement data

is being requested, including temporal ﬁlters, mea-

surement procedures and measurement points. Re-

spectively, GetObservationResponse encloses the re-

turned measurement data.

5 CASE EXAMPLES

Two practical demonstration examples have been im-

plemented with the messaging API. The ﬁrst demo

implements a scenario where one unit process sub-

mits information to a second unit process so that the

second can use that information to optimise its opera-

tion. The practical context is copper reﬁnement, and

the involved unit processes are a Flash Smelt Furnace

(FSF) and a Peirce-Smith Converter (PSC) (for refe-

rence about copper reﬁnement, see (Schlesinger et al.,

2011)). The FSF provides batches of material to PSC

for further reﬁnement. To operate efﬁciently, the con-

trollers of PSC need an actual estimate of the compo-

sition of the batches that come from FSF.

To actually implement the communication, the

ﬁrst demo uses the publish-subscribe pattern for com-

munication. Whenever a batch leaves FSF, a related

composition estimate is published. This estimate is

then delivered to the PSC, which has a subscription

for it. The required communication is straightforward

as message structures are concerned; the messaging

is one-way, so the composition estimate is only pu-

blished as such with the related metadata. That is, no

request structures or similar are needed but only the

observation that contains composition information.

The second demo presents a request-response sce-

nario, where a client requests for a temperature value

from a server. The scenario demonstrates a typical

need in process plants. Considering messages, the

scenario is more complex than the publish-subscribe,

because the communication pattern is bi-directional.

That is, there must be a structure to enclose the condi-

tions that communicate what is actually being reque-

sted. To request the current value of a sensor, simply

a position ID is sufﬁcient. However, in more complex

scenarios, there could be temporal ﬁlters as well (e.g.,

”provide me the measurement values from last four

hours”).

The message exchange of the demos is illustrated

in Figure 4. The ﬁrst demo (left) only delivers ob-

Figure 4: Message exchange in the two demos.

servation from publisher to subscribers, whereas the

request-response pattern of the second demo requires

bi-directional communication.

In both of the demos, the messaging API has

shown its power. The creation, serialisation, deseria-

lization and utilisation of messages is straightforward

and does not require many lines of code. Without the

API, the manual work on data structures would re-

quire a large amount of careful work but is interope-

rable thanks to the standard structures utilised in the

messaging.

6 DISCUSSION

The use case examples presented were simple but pro-

ved the point of having a common uniﬁed format en-

gineered to improve semantic interoperability on ex-

changed information. They could easily be extended

to real-life production-related information systems.

The API approach is powerful. However, it is also

important that no client is forced to use the API, as

long as they read or generate the message structures as

agreed. However, the XML schemata that are utilised

are complex and large. If the API is not utilised in

application development, the developer has to know

the schemata in detail.

As the serialisation syntax, JSON would generate

less overhead compared to the current XML imple-

mentation. However, there is a limitation that neither

Observations and Measurements nor ANSI/ISA-95

currently offer a standardised JSON schema speciﬁ-

cation. Fortunately, OPC UA speciﬁes JSON seriali-

sation, but OPC UA was not chosen for the primary

implementation technology due to missing data struc-

tures.

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220

7 CONCLUSIONS AND FUTURE

WORK

Interoperability of information is an essential prere-

quisite for efﬁcient integration of data. This also ap-

plies to the complex domain of industrial processes

that typically are distributed and of large scale. This

paper presented message structures developed that are

needed to implement advanced plant-wide monitoring

and control solutions.

First, a comparison of existing standards was pre-

sented with some structures applicable for data ex-

change. Based on this, and utilising constructs from

these standards, message structures were proposed for

communicating data and events in industrial proces-

ses. The developed concepts were demonstrated with

use case examples. Although the examples were li-

mited by scope the concept can be scaled to larger

real-world industry settings.

In the future, the message structures could be ex-

perimented in the integration of actual production sy-

stems. In addition, new message structures will likely

be added. For instance, for schedules, the structures

of ANSI/ISA-95 will likely be utilised. The API li-

braries are work in progress that will facilitate taking

into use the proposed message structures.

ACKNOWLEDGEMENTS

This work has received funding from the European

Union’s Horizon 2020 research and innovation pro-

gramme under grant agreement No 723661. This

study reﬂects only the authors’ views, and the Com-

mission is not responsible for any use that may be

made of the information contained therein. The aut-

hors want to express their sincere gratitude to the

funder and the project partners in the COCOP pro-

ject (Coordinating Optimisation of Complex Indus-

trial Processes, https://www.cocop-spire.eu/ ).

In addition, the authors are grateful to the Aca-

demy of Finland for their funding (grant 310098).

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