An automated future?
The race is on for process industries to achieve fully autonomous operations
within 10 years Contributor Bert Konings, executive director of new business
Automation is certainly not a new concept
for the process industry, but the ongoing
pandemic has placed a new impetus on
the sector to scale up and become
increasingly automated. Due to social
distancing guidelines and nervousness
among workforces to return to their places of
work, a higher priority is being placed on the ability
to continue running operations without workers
needing to be on site.
Yes, coronavirus has somewhat put the brakes on
economic growth this year, but ironically, it is proving
one of the biggest stimulations for future innovation.
In fact, Yokogawa’s research shows that two thirds
of process industry companies across the globe now
expect to have fully autonomous operations by the
year 2030. But the first question is; to what extent
are companies automating, and how?
“A well-designed autonomous system
will bring the benefits of remote
operations and safer working.”
Reaching ‘level five’ automation
As process industries embrace automation, both
operational technology (OT) and IT professionals
are looking at ways to increase productivity through
autonomous operations. We’ve recently seen the
renewables sector putting a greater emphasis on
automation investment as a direct result of COVID-19,
for example. With process industries aiming to
increase their investment in industrial automation over
the next three years – a definite trend is emerging.
And that trend is leading to a race within the
industry – a race to what we at Yokogawa refer
to as level five automation. Level five is defined as
operations being completely autonomous, within
the integration of systems for supply chains and
process operations, among others. Simply put, they
require no human intervention to be operational.
As companies count the costs of an absent
workforce, a well-designed autonomous system will
bring the benefits of remote operations and safer
Automated technologies to aid decision making
There are four autonomous technologies that have
become a top priority for companies in order to
achieve level five automation.
AI is the driving force behind autonomous
operations, and process industry companies will
be investing hugely in AI systems that will allow for
decision-making technologies to be implemented.
● Cloud analytics and big data
The sheer volume of data required for processes to
run autonomously and be instantly shared between
many systems and devices, will be tremendous.
The distributed edge networks in which this data
is created -and the continual cycles of DevOps
style software upgrades required to ensure the
data integrity and safety of the network - means
that without an effective cloud approach, and
analytics system, automation systems won’t operate
effectively. Cloud analytics, along with big data that
provides ways to analyse, systematically extract
information from, or otherwise deal with data sets
that are too large or complex to be dealt with by
traditional data-processing application software.
● Intelligent sensors and devices
If systems are to run autonomously, they must
be able to diagnose and calibrate independently
too. Hence, smart sensors are vital to enable
concepts like self-diagnostics, self-calibration, and
self-configuration/parameterisation. There is an
increasing need to measure quality attributes and
raw material attributes, which is enabled by smart
sensors such as in digital twin deployment.
● Cybersecurity measures
Of course, as the above technologies become
connected and autonomous, the wider the cyber
threat surface grows. Cybercriminals and hackers
are extremely adept at noticing industry trends,
and so investment in cutting edge cybersecurity
solutions is naturally a top priority.
Addressing the skills gap
As these technologies become ever more complex,
they require constant training and development of
more staff – leading to the potential for an evergrowing
window of human error. Automating, and
programming, existing processes makes keeping up
with the curve far more time consuming.
One issue that concerns the manufacturing