How digital is essential for carbon capture at scale

G. 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. 1).

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 consumption.

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 capture technologies.

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 process designs.

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 lifecycles.

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. 4).

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.

The 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).

Across 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.

Takeaway. 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.