In the petrochemical industry, business
leaders are continuing to push for new approaches to optimize technology
applications without disrupting their operational targets and key performance
indicators (KPIs).
The massive volume of data generated and
shared can be critical in plant operations. The introduction of the Industrial
Internet of Things (IIoT) has presented a beneficial solution for process
plants, especially petrochemicals, which are always focused on maintaining and
increasing safety, reliability and efficiency, with maximum asset utilization.
The IIoT is considered a tool for partially
responding to work disruptions and optimizing operational parameters. The
application of modern intelligent data integration and digital twin models help
operators to monitor profitability as they perform functions by balancing
unit-level costs with unit-level KPIs. This has resulted in a positive approach
to business sustainability—i.e., data processing can lead to risk mitigation,
enhanced plant safety, increased reliability and operational efficiencies.
Moreover, to protect asset health, plant predictive
analytics can perform the necessary calculations to assist in early predictions
of equipment problems.
Refining-petrochemical
integration optimization. With continued market demands (e.g.,
crude oil pricing, environmental regulations), the refining and petrochemical
industries are seeking optimized solutions to ensure their survival and
sustainability. Integrating the refinery and petrochemical industries enhances
the synergies of existing opportunities between both industries to generate
value for the entire crude oil production chain, applying economic and
environmental aspects.
FIG.
1
depicts
an example of an integration scheme that clarifies the relationship between
refinery streams and their related petrochemical products. From data analysis, it
can be concluded that due to the increased volume of data processing and the increased
complexity of integrated complexes, the processing industry is facing the
following challenges in plant operations and value chain optimization:
Therefore, due to the massive influx of complex data
that is difficult to optimize for production control, artificial intelligence
(AI) has been introduced to partially solve these challenges, enabling better process
control. In addition, an optimized value chain process has led to the enhancement
of the entire supply chain. This is established by leveraging historical
and real-time data to generate accurate forecasts and efficient network-wide
plans to attain maximized profitability.
Supply
chain application. An enhanced planning approach on the value
chain process has been introduced that can attain maximum profitability and
KPIs. With improved visualization of historical and real-time market data, this
approach can build an optimized supply chain network that can generate accurate
forecasts.
This optimized
supply chain networks should provide better future forecasting and planning,
covering working capital reductions, production throughput and on-time orders. The expected
outcomes can include:
FIG.
2
shows asset optimization solutions in a polymerization process, where target polymer production/scheduling
is based on the design production slate and calculated downtimes between grade
changes. This is performed by reducing transitions and addressing the real needs
of polymer producers with decreased costs and new polymer market requirements. The
following parameters should be considered:
IIoT solutions. With challenges
in the petrochemical industry and related high data dimensions and time lags in ML
applications, the introduction
of the IIoT has presented a useful solution for petrochemical complexes. This solution
is focused on the maintenance, safety, reliability and efficiency of processes,
leading to an acceptable sustainability level. However, challenges remain,
especially regarding system cybersecurity and the poor management of collected
data. Therefore, to achieve a high level of business sustainability, there
should be an advanced approach to utilize collected and generated plant data that
can be easily translated into action. Such data should be processed and generated
across cybersecurity layers. AI applications have been introduced as an optimum
solution. The use of AI in the petrochemical industry has resulted in improved
operational efficiency, reliability and safety. It has also provided tools for
asset and value chain optimization, performance and production.
The introduction of digital twin
models in the petrochemical industry has been considered, which can integrate
ML tools and real-time big data. Deciding to use digital twin process applications
can be divided into three primary sections: economic evaluation, investment
costs and internal rate of return (IRR).
The first step for applying a digital twin
system is to collect operating plant data. To ensure
outcome accuracy, data validation should follow to detect any reconciliation
issues or gross errors.
The second step is to perform dynamic target
calculations considering feedrate, quality, composition and independent
parameters, while also meeting selected product rates, specifications and
environmental regulations.
The
third step is to embed the optimized system into the entire supply chain of the
production complex. The application of the digital twin system should solve the
following:
For example,
inputting petrochemical data (such as a polymerization reactor’s control, along
with feedstocks, catalyst used, cooling medium and reaction time) into a
digital twin system can help operators understand how these factors are working
in relation to impacts on the process’ profitability and catalyst performance
in a safe and optimized manner (FIG. 3). These advanced digital application solutions
integrate real-time data and advanced analytics for better decision-making,
underpinning applications that can dramatically improve process control
behavior, efficiency and sustainability.
With
digital twin systems, plants can now achieve a new level of business
sustainability—one that improves not just environmental impacts, but also asset
performance, regulatory compliance and financial performance. These
systems will also
help organizations with business transactions, allowing operators to test
economical and efficient processing outcomes in their operations.
From the above material, it can be visualized that digital
twin system applications in the petrochemical industry are considered a system
integration approach through engineering, procurement, construction management
and other key services related to supply chain and marketing activities. These applications
are based on a developed operating control system, driving plant
sustainability operations to attain the necessary visibility analysis and
insights to address the challenges inherent to meeting process goals.
Takeaway.
Traditionally, industrial
information technology (IT) and operational technology (OT) have been
considered separate entities with little crossover. However, hardware and
software systems that monitor plant operations and optimization studies provide
operational control that has been engineered and supported by data (IT).
In
today’s digital world, the interconnection between IT and OT is providing the
processing industry with valuable data to attain high KPIs. This is based on the IIoT, ML and
loop design for information exchange between the factory and a virtual digital
model.
End users can now leverage existing assets
and investments to drive toward a safer, more reliable and efficient
petrochemical enterprise. Therefore, the application of a digital twin model can
result in a more secure, safer and more efficient petrochemical complex. HP
ABBAS EZZAT works as a Petrochemical Professor at Pharos University and a Distinguished Scientist at the Materials Science Department within the Institute of Graduate Studies and Research at Alexandria University. He is also a local consultant for the Egyptian petroleum and petrochemical sectors and a Senior Associate Consultant for Channoil Consulting Ltd. in London. Prior to joining academia, he occupied several top management positions in the Egyptian petroleum and petrochemical industries. Dr. Ezzat holds an MSc degree in chemical engineering from Washington University and a PhD in petrochemical applications from Alexandria University. He completed his postgraduate studies in petroleum processing technologies from the School of Chemical Engineering at Oklahoma State University.