Munoz, Aspen Technology, Bedford, Massachusetts
Energy and chemical companies around the
globe obtain value from digitalization across all stages of the asset
lifecycle, from design to operations. Today, with the aggressive timeline to
meet net-zero targets, digital technologies are playing an even more crucial
role in helping companies get closer to achieving their sustainability goals
much faster and in the most economical way.
Carbon capture is one of the key
strategies being adopted as one aspect of the myriad solutions to address sustainability
and net-zero targets. However, technical and economic challenges are still limiting
adoption and commercialization of carbon capture at a wider scale. With the
combination of digitalization and sustainability, digital technologies are
crucial enablers across the complete carbon capture value chain, driving
continuous innovation, supporting economic scale of technologies, accelerating
implementation and providing confidence in long-term geological carbon dioxide
(CO2) storage (FIG.
Digitalization addresses the key
challenges to the successful commercialization and wide-scale deployment of carbon
capture by helping to reduce costs and minimize risks, prioritize investments,
accelerate project execution and improve efficiency in operations across the
complete value chain.
Innovation is necessary to drive down costs. During the past decade, rigorous process
simulation has been used to successfully develop carbon capture technologies
using amine-based solvents, and more recently, to drive commercialization of new
technologies for direct air capture. By leveraging physical property libraries
and experimental data, carbon capture technology developers have been able to accurately
represent the complex interactions of carbon capture processes and identify the
most optimal operating conditions to maximize capture efficiency and reduce energy
However, while carbon capture is a proven
technology, its present scale is insufficient to achieve the necessary targets.
With the end goal of reducing the total cost of CO2 capture and
accelerating the adoption curve, ongoing research is exploring new solvents and
solvent mixtures, innovative methods to enhance solvent performance and even novel
Digitalization in early research
stages enables reduced time to large-scale deployment, especially when
considering the aggressive timelines to achieve carbon capture targets. Rigorous
process simulation provides an accurate representation of the complex chemistry
and thermodynamics, making it possible to quickly assess new technologies, support
research decisions to improve efficiency and performance, and drive down costs.
With accurate predictions of the
physical and chemical interactions and a rigorous representation of the
process, it is possible to evaluate the performance of the carbon capture
methods under different flue gas compositions and operating conditions to
determine the most optimal scenario. Experimental and pilot plant data can
easily be incorporated into a model of the carbon capture process through
property regression and estimation methods to improve the accuracy of the
predictions. Achieving visibility into the behavior of the technology under
different scenarios helps to plan a more targeted design of experiments,
reducing costs and time during the development of these new technologies, while
providing the foundation necessary to enable carbon capture at scale.
Optimal design of carbon capture processes. Technical
and economic feasibility is necessary for successful commercialization of
carbon capture at scale. Rigorous flowsheet modeling of carbon capture can help
to evaluate, design and optimize large-scale processes to drive confidence and
move quickly from lab to pilot to commercial scale.
Having a model of the carbon capture
facility provides a better understanding of the process and equipment
performance. Precise mass predictions enable the calculation of accurate
capital costs associated with carbon capture, while energy and water
consumption insights from the models are crucial to support better decisions to
minimize operational costs.
State-of-the-art, rate-based modeling uses
heat and mass-transfer correlations based on transport properties and column
internals geometry to obtain a precise representation of the CO2
separation process. A deep understanding of solvent chemistry, thermodynamic
limits and mass-transfer helps companies develop and deploy better systems to
reduce carbon capture energy consumption and capital costs.
Understanding column hydraulics is an
important aspect of column design. Evaluation of hydraulic constraints and
column performance can help to determine equipment operational limits to avoid low
carbon capture efficiencies. Column hydraulic analysis can be used to improve
column performance, while evaluating the reuse of existing equipment to
minimize capital expenses (FIG. 2).
Heat exchanger performance and
potential problems can be identified upfront using detailed thermal models. During
design phases, engineers can leverage process simulation to analyze alternatives
and evaluate equipment costs—based on the provided constraints—to select the
most viable alternative. This detailed analysis enables process engineers to
understand heat exchanger performance within the context of the system,
empowering them to make design decisions and hand off the designs to equipment
specialists for further analysis and detailed design, ultimately allowing for
early decisions with longer lead items.
Integrated economics, energy and
emissions analysis enable the quick evaluation of process configurations to
reduce costs and carbon footprint, which in conjunction with the mass and
material balances and equipment evaluation, helps to identify optimal capture
Making informed investment decisions across the carbon
capture value chain. At the earliest stages of a carbon capture and
storage (CCS) initiative and to support investment decisions and drive down
costs, project leaders and key stakeholders typically evaluate long-term
impacts, risks and overall economics over the entire lifecycle. Today’s process
modeling and system-level risk modeling capabilities enable these efforts, examining
the flows, capacities, efficiencies, technology risks and external stochastic
factors that will affect the complete carbon capture system (FIG. 3).
A risk and reliability analysis looks
at different levels, from the equipment to the complete system, to support
better decision-making when dealing with uncertainty, risk and reliability. It
identifies and quantifies the events that lead to performance losses by
evaluating the effect of factors such as equipment redundancy, operations
logic, maintenance practices and logistics alternatives. Considering these
factors, the model predicts the future performance of the design to quantify
the probability of achieving annual CO2 capture targets.
By looking at the entire system, a risk
and reliability assessment helps to identify bottlenecks, understand their
magnitude and use probability analysis to predict future performance. Reliability
data can be used to continuously simulate both operations and maintenance
activities to predict the annual CO2 capture rate during the
lifecycle and quantify the variability in system performance across many
As the model shows the probability of
meeting or exceeding a specific objective, it will also predict the culpability
of different events (e.g., what events are to blame for the lost performance),
guide design changes to remove bottlenecks to optimize the entire system and
minimize potential risks (FIG.
System-wide risk assessment can extend
to evaluate beyond CO2 sources and carbon capture operations to
consider power generation, power storage, transportation, injection and
geological storage, or conversion of CO2 into valuable products.
With a risk and reliability
assessment, it is possible to understand the entire system through a holistic
interdependent model that promotes better decisions. As all uncertainties and
variabilities are considered, the risk is reduced. By understanding the events
causing bottlenecks, insights will guide decision-makers to know how and when
to best expend monetary resources.
Further accelerating time to value. Models used on previous technology development
and research and development stages provide early visibility to help improve capital
expenditure allocation across any future projects and eliminate risks. Detailed
cost estimation software makes it possible to generate estimates from
conceptual data and reduce estimated work hours by up to 80%. Using powerful
model-based methodology, equipment costs are calculated, along with a detailed
breakdown of the associated bulks such as piping, instrumentation, and other
costs such as electrical and civil engineering. This level of detail makes it
easier and faster to identify the factors that drive capital costs. As projects
move forward and more data becomes available, assumptions can be adjusted to
remain true to project requirements, assisting with proper evaluation of the
project scope and execution.
Conceptual 3D layout solutions provide
optioneering for any type of project and increase multidisciplinary
communication and collaboration, resulting in a time savings of up to 40%.
Artificial intelligence (AI) can enhance conceptual layout by optimizing the
pipe routing across the asset, adding additional time savings by avoiding
manual workflows. This helps organizations to improve the design quality, speed
and execution of any project by breaking the silos between teams.
transition from conceptual design to project execution—and ultimately operations—offers
incremental opportunities to further leverage these digital trends (e.g., using
a “born digital” approach, where digital
models from design stages are ready to be used early in operations).
the carbon capture value chain, the conceptual design and feasibility models
can be used to build a strong data management solution that integrates capture,
transportation and sequestration facilities to the cloud and across the
ecosystem of business systems, including enterprise resource planning,
marketing, reliability and maintenance, among others. Furthermore, this
approach enables global scale in a resource-limited environment with remote
operations, expertise consolidation and the flexibility to integrate with an
ecosystem of continuously developing technologies around AI, machine-learning,
carbon accounting and other future needs in the CCS industry.
Technical and economic feasibility using rigorous process modeling and risk
analysis are some areas where digital solutions help to accelerate innovation
and guide investments. Over the project’s lifecycle, collaborative workflows
across disciplines help to improve project economics, decrease risks and
accelerate the commercialization of the solution through end-to-end designs. Leveraging
digital solutions across the carbon capture value chain—from design to
operations—is crucial to reduce costs and risks, accelerate project execution,
and improve efficiency in carbon capture designs and operations. This overcomes
the main challenges limiting the adoption and commercialization of carbon
capture at a wider scale. HP
Gerardo Muñoz is a Solutions
Marketing Manager at AspenTech. He joined the company in 2010 and is now part
of the core team leading go-to-market strategy for sustainability solutions
across the company’s portfolio. He has experience across multiple process
industries and covers key solutions such as hydrogen, CCS and plastics
circularity. He earned a Bch degree in chemical engineering from Tec de
Monterrey in Mexico, and an MS degree in sustainable chemical engineering from
Chalmers University of Technology in Sweden.