W. DANIELS, TrendMiner,
As companies in the manufacturing process industry explore
sustainable ways to manage their energy use, engineers are finding that the deadline
for drastically reducing carbon emissions is approaching rapidly.
Sustainability has reached the top of every
organization’s agenda. Process manufacturers find that social pressure—even from their
investors and employees—can
be as strong as regulatory measures to reduce carbon dioxide (CO2) emissions.
There is an increased awareness among all generations regarding the negative
effects of carbon emissions on climate change. Governments have put teeth to
regulations by setting deadlines for CO2 elimination. While the
European Union strives to be emissions-neutral by 2060, China has set 2030 as
its target. At the time of this publication, more than 40 countries had a
carbon emissions tax.
In addition to the environmental initiatives, and with
fewer than 30 yr until the deadline, companies must become sustainable to stay
ahead of the curve. According to Deloitte, 56% of oil and gas companies tie their executive compensation to
decarbonization efforts. Investments in solar, wind and other energy
alternatives now top $350 B/yr. Additionally, during times of crisis (e.g., the
COVID-19 pandemic), companies have learned that a diversified package of energy
assets helps achieve energy needs.
About 20% of emissions savings come from improving energy
efficiency. Process experts must clearly visualize emissions and their
associated processes to make these improvements. They also must be able to flag
excess emissions and investigate the root causes of such anomalies. Engineers
must be capable of monitoring emissions, flaring and energy consumption as they
identify areas for operational improvement. Organizations can go the extra mile
by making step changes, such as electrification, photovoltaics, carbon capture
and using green energy.
The low-hanging fruit of emissions reduction hides inside
operational improvements. Energy efficiency goals are linked directly with
reduced operational expenses, and improving processes lowers energy consumption.
Tackling operational improvements also enables organizations to take an
incremental approach. Companies do not face large investment costs, and
improvements typically rely on process knowledge already in place within the
Achieving operational improvements becomes easier with
advanced self-service analytics.a An advanced analytics platform
empowers process experts to make data-driven decisions that improve energy
efficiency. As a result, engineers make substantial contributions to an
organization’s sustainability efforts and bottom line.
The following examples include two situations where
self-service analytics improved operations, increased energy efficiency and
helped engineers meet corporate sustainability goals.
Eliminating a source of flaring. A
dashboard following the overall emissions status of an asset showed a sudden
increase in CO2 emissions. Although it is not a critical event on
its own, it could lead to more severe incidents if the root cause of the
flaring is not determined. A more frequent occurrence of similar events could result
in a significant loss.
Engineers used their advanced analytics platform to
review 1 hr of time-series flow data going to a flare. While the trendlines for
such a feed should be constant, the data selected shows a sudden spike. Process
experts need to determine why the spike is occurring and how frequently it previously
occurred. Determining how often they occur is the best first step, as this
often can help determine the root cause.
Process experts can use a pattern recognition search to find
periods when spikes occur. After selecting the period where the analysis is
stable period followed by a sudden spike—the advanced analytics solution determined a
similar period had occurred 38 times before, in this case. Engineers can
overlay the flaring incidents and compare the layers to determine if the
behavior follows the same pattern.
To learn more about the spike, engineers can calculate
the maximum value of the flares to determine which flare was the most severe. They
can determine this by adding the calculation on the same tag within the advanced
analytics platform. The calculations can then be run for all 38 incidents to rank
the flaring events in severity. With the most severe events as a starting
point, process experts can move on to determine the root cause of the flare.
In the advanced analytics platform, engineers can use the
high-throughput recommendation engine to search for correlations in process
behavior in all other tags. In this case, it found one tag with a 94%
correlation with the spikes in flaring. It was an early indicator, making it
more likely to be the root cause of the CO2 spike. More
specifically, the identified correlation showed that a valve closed quickly right
before each flare spike, as shown in FIG. 1. Process experts confirmed this
correlation equated to causation and made changes to the control system to
prevent the valve from closing quickly under normal operation.
The company reduced overall emissions and resolved a
safety concern by correcting the root cause of the flare spikes. This also
helped prolong the valve’s life and resolved problem that could interfere with
an engineer’s other work.
This case nicely illustrates the power of self-service
analytics. Through a simple sequence of steps, process experts can leverage
their knowledge to identify root causes and make data-driven decisions quicker
Operational improvement of an integrated solar
combined cycle. An advanced self-service analytics platform can
also assist in retrofitting and step changes. Frequently, process experts have
limited in-house experience with new technology. Companies must quantify how
new technology will affect existing processes and must be able to demonstrate the
With advanced self-service analytics, engineers can build
knowledge faster, more easily quantify process influences and expedite the
rollout of process monitors to more users.
An example of this can be found in retrofitting a combined-cycle
and steam turbines—to
an integrated solar combined cycle (ISCC). After converting to an ISCC,
engineers must determine the effectiveness of the step-change investment. Additionally,
they must monitor the performance and identify any root causes of recent
In this case, engineers wanted to see if the new ISCC was
living up to expectations and determine if any anomalies needed to be corrected
or losses in production that would need to be addressed.
Engineers viewed a dashboard that measured the plant’s key
performance indicators (KPIs) and noticed that the facility’s production had
decreased (FIG. 2).
Process experts wanted to determine if the decrease resulted from retrofitting
the combined cycle to include a solar turbine.
Engineers began by loading data that showed power
production and consumption over time. They then searched for data periods that
showed the process before and after the retrofitting. Because the project had
taken place within a year of the analysis, process experts could use the 1-yr
mark to compare process behavior.
The company’s advanced analytics platform included a
comparison table that showed various production values. Since the addition of
the solar generator, power consumption had decreased, but power production had
increased. This meant its retrofitting was successful, but the information did
not explain why production decreased. Despite the success of the retrofitting,
engineers still had to determine the root cause of this anomaly.
In one of the dashboards, engineers can find other clues
to help them determine production loss. Process experts can see the production
output of each power turbine within the ISCC. From the values on this
dashboard, process experts determined that the steam turbine was not producing
as much power as it should, and the solar field had a performance issue.
Engineers tackled the steam problem first. They used the
advanced self-service analytics platform to view the steam production. The
addition of the solar turbine created a trendline with peaks and valleys. Process
experts can use the daily production average to smooth out the trendline and
make it fit for analysis.
Formulas can then be built to calculate various
measurements. Engineers can look for similar periods to see when the steam turbine
power production fell below normal operation and compare the data. In this
case, engineers determined that the steam turbine started losing power before
adding the solar turbine.
Process experts then began to search for the root cause
of the power loss from the steam turbine. They used the advanced analytics
system’s recommendation engine to suggest root causes. They learned that the steam turbine pressure increased when the
blowdown decreased. Furthermore, they determined that there was a 2-d window
when the blowdown started to decrease before the steam turbine increased. This
also warned that the process would shutdown entirely within 3 wk.
When searching for similar incidents and potential root
causes, it often helps to filter out periods with known but unrelated
deviations from normal operations. This is called contextual data, which
process experts can integrate with time-series data to enhance their analysis.
With a decrease in blowdown determined to be the root
cause of the power loss, engineers used a value-based search to discover when the
blowdown was below a certain threshold for at least 1 d. They discovered this
was unique to this anomaly, so they decided to save the information as
contextual data for future use and to activate a monitor to receive immediate
warnings for future decreases in blowdown.
There was one more peculiarity on the dashboard of the
advanced analytics platform: a cooling problem in the new solar field. Using a
linear temperature graph over time, engineers determined the southwest field of
solar panels reached 675°K.
They used this figure to find similar periods of interest where the temperature
was that high or greater.
Engineers then created a Gantt chart to show relevant
process events by the asset. Similar events are displayed apart from normal
operations, which creates a concise graphical overview of plant performance. Furthermore,
monitors were set up to provide early warnings when solar field process limits
are exceeded. This allows faster resolution of process upsets and higher
process efficiency in general.
Operational efficiency equals energy savings.
energy consumption and improving energy efficiency are project assurances to a
retrofit. However, opportunities for improvement do not lie solely within the
area of reducing carbon dependency.
An advanced self-service analytics platform offers opportunities
for operational improvements at every stage of production. When operational
efficiency is improved, energy efficiency is improved. Companies will find they
use less energy and reduce the discharge of CO2 emissions, and they
can meet their sustainability objectives, but produce the same while using
With advanced self-service analytics, engineers and their
companies can hit upcoming emissions target goals and keep their production schedules
on track. HP
a TrendMiner NextGen
WOUTER DANIELS has
worked for TrendMiner as a data analytics engineer for more than 3 yr. Daniels
helps train and advise companies to get the most out of their stored
time-series process data and uses customer feedback to help improve the
advanced self-service data analytics software. He provides custom solutions for
more complex cases. Daniels earned his PhD in chemical engineering at the
University of Leuven, where he studied data analysis and modeling of
biochemical systems to increase bioproduct yields.