Information Models and Information Exchange in
Plant-wide Monitoring and Control of Industrial Processes
David H
¨
astbacka
1
, Petri Kannisto
2
and Matti Vilkko
2
1
Laboratory of Pervasive Computing, Tampere University of Technology,
Korkeakoulunkatu 1, FI-33720 Tampere, Finland
2
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
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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
(H
¨
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.
KMIS 2018 - 10th International Conference on Knowledge Management and Information Sharing
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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