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Research collaborations for low-carbon energy

Read how a rich ecosystem of external research partnerships in academia and industry supports Shell’s efforts to leverage computational science as we develop technologies that help society meet climate goals.

By Suchismita Sanyal, General Manager for Computational Science on Sep 8, 2021

Unprecedented collaboration between businesses, governments and civil society is required to get the world to net-zero emissions. This is also true to develop and scale up the technologies of a low-carbon energy system, such as hydrogen.

As the executive director of the International Energy Agency Fatih Birol underlines, the world needs to develop better and cheaper technologies to meet our climate goals. Society must embrace the energy innovation challenge so that every sector has commercially available low-carbon options.

I take a lot of pride and energy from the contribution of Shell’s computational scientists in Shell’s decarbonisation journey, because their research supports the development and deployment of technologies to avoid, reduce and offset emissions.

I believe a strength - and a beauty - of our daily activities lies in our ways of working. We collaborate across interdisciplinary teams in-house and within a rich ecosystem of external research partners in academia and industry.

In the previous blogs, we have talked about our collaborations with Dalmia Cement, Kreisel Electric and Microsoft. Let me highlight a few more recent examples of how partnerships are enabling better technologies to avoid and reduce emissions.

Electrifying chemicals production to reduce the carbon intensity of our products

Avoiding emissions means we are pulling the most efficient levers to be more energy-efficient in our operations, thus reducing greenhouse gas emissions; it also means decarbonising our operations by design. This is the purpose of our strategic collaboration with Dow and the University of Houston, with whom we design new crackers for chemical processes. The idea is to use electricity coming from renewable sources to heat up the crackers instead of burning hydrocarbons, thereby reducing the carbon footprint of the process significantly.

Electrifying chemical crackers means we could significantly reduce CO₂ emissions from one of the central processes of the chemical industry, provided that the electricity comes from renewable resources. However, the novel technologies must also be technically compatible and commercially competitive with the established processes in existing plants.

The computational modelling of such processes is highly challenging because the questions to answer are by nature multi-scale (from plant to molecules) and multi-physics (they involve numerous complex laws of physics). With recent advancement in electro-thermo coupling models, and in high performance computing (HPC), we have jointly developed advanced modelling frameworks to design the electric heating concepts at plant scale.

In the collaboration with Dow, we used this computational modelling to design, optimize and compare different crackers concepts. Our deep expertise in computational fluid dynamics, systems modelling, and crackers design was instrumental to demonstrate the technical feasibility of electrified crackers.

In 2020-2021, we secured new patents, onboarded additional research institutes in the project and earned the financial support of the Dutch government. The companies are now evaluating construction of a multi-megawatt pilot plant, with potential start-up in 2025, subject to investment support. Once the concept of electrified crackers is developed, it can be applied to electrify multiple chemical processes.

Optimising wind farms by design with computational fluid dynamics

Reducing the emissions associated with the use of our products means that we want to supply more low-carbon products, such as renewable power. Offshore wind power is a key component of the development of our wind power business. And there is a clear business case to build wind farms optimised by design.

Offshore windfarms are built with increasing numbers of turbines by farms to take advantage of economies of scale. Identifying the optimal setting of an offshore wind farm can be the differentiating factor to win tenders and, in daily operations, generate the most power by turbine.

In a large wind farm, the wind speed behind a turbine is reduced due to extraction of energy from the wind, which is called a ‘wake effect’. It can significantly impact the annual energy production of a wind farm. Therefore, predicting the wake effect to optimize the design of a wind farm can play a critical role in the economic success of a project.

Building on our decades of deep expertise in computational fluid dynamics (CFD) and ability to capture complexities of turbulence modelling, we developed an innovative design framework for windfarms together with Shell offshore wind engineers. This framework integrates accurate wind farm flow predictions that capture the wake effects around turbines. This work significantly reduces uncertainties in the yield losses, which helps to remove constraint on project economics. A better optimization of wind farm layout will boost the power production of future wind farms.

Our researchers leverage computational science in close collaboration with the partners of the Crosswind consortium to further demonstrate technologies to integrate the wake effect in real time operations. The better understanding of energy yield of wind farms empowers better techno-economic assessments of projects, which in long run means more renewable wind power will be available when the wind blows.

Learning from parallel industries who have mastered some of our challenges

My last example might have a less direct link to emission reductions, but I believe you will appreciate the important underlying implications this cooperation can have to support the improved energy efficiency of our operations, thus reducing their carbon intensity.

Integrity issues such as the corrosion of pipelines can have high costs and safety implications for the oil and gas industry. Corrosion impacts the availability and safe operation for manufacturing assets. Steam generators are critical elements in many of our manufacturing process and prone to corrosion. This is a challenge shared and well understood by the nuclear energy industry, which has a long track record of technology innovation to address it.

Across businesses, Shell is combining augmented intelligence (which means infusing physics or chemistry rules into artificial intelligence models), computational science and corrosion engineering to develop and deploy highly accurate predictive models for corrosion mechanisms. Our computational scientists joined forces with Framatome, a German-French designer and supplier of systems and equipment for the nuclear sector, to go beyond existing models. Together, they are developing an advanced mechanistic modelling framework to predict corrosion in steam generators using multiphase computational fluid dynamics models.

The model can predict which locations in steam generator tubes are vulnerable to under-deposit corrosion and thus identify early potential risks and enable faster and better remediation. What I like most about this recent work is the diversity of expertise the research team comprises, with interdisciplinary knowledge from the nuclear industry, computational sciences and process and discipline engineering.

Working across disciplines, researchers from Framatome and Shell are not only developing a model that can help improve the asset integrity in our current operations. This corrosion tool can also be used to improve the design and operation of plants producing “blue hydrogen”1, which could play a significant role in the transition to a clean and low-carbon energy system. I feel energised to know that this corrosion prevention model could in future enable the production of more low-carbon energy products.

One company alone cannot solve the climate crisis. The complexity of the challenge behind this simple truth underscores the essential role collaboration plays in developing technologies that make the energy transition a reality. I have spent 20 years in and around the energy sector researching new materials with the support of computational science and want to share one takeaway: the breadth of ground to be covered promises exciting careers paved with enriching collaborations.

1 Blue hydrogen refers to the production of hydrogen from steam methane reforming (SMR) or gasification combined with carbon capture and storage to remove the carbon dioxide emissions from the production process.

Suchismita Sanyal is the General Manager, Computational Science at Shell Technology Centre Bangalore. She leads a group of 60 researchers in the Computational Science group in Shell, delivering digital solutions across multiple Shell businesses and designed to position Shell on a stronger foothold in the energy transition.

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