Dr. Rick Vandoorne, Railway Engineering Computational Designer at Zutari, and Dr. Andrè Broekman, Civil Engineering Computational Designer at Zutari and Research Associate at the University of Pretoria, recently delivered a presentation examining the future of railway decarbonisation and the transition away from carbon-intensive fuels. Hosted by the University of Pretoria, their talk formed part of its annual Railway Research and Training Review, bringing together industry experts to explore advancements in rail transport.
Andrè and Rick work in Zutari’s Global Design Centre (GDC), an offshoring unit that provides exposure to both international and local projects. Their presentation, titled “Decarbonisation 2050,” envisions the future of railway transport, examining what that future may look like and how it can be realised.
In the context of their work, they are exploring how to replace carbon-intensive sources of tractive power such as diesel-electric locomotives with battery-electric locomotives or, as Andrè humorously describes it, “the world’s most expensive Scalextric playset.”
The framework they use to approach many of these projects integrates automation, machine learning, and big data analysis into their daily workflows. This approach merges traditional engineering analysis and design—as taught at institutions like the University of Pretoria—with computational design theory and thinking, leveraging the processing power of modern computers.
However, as the presenters emphasised, technology is not a substitute for engineering expertise. A firm grasp of fundamental principles and specialisation remains essential. At its core, computational tools act as catalysts, amplifying an engineer’s abilities and capabilities when applied effectively.
Their fundamental data framework consists of four key stages:
- Craft – The everyday tasks engineers perform, such as creating Excel spreadsheets or writing scripts.
- Standardisation – Structuring data inputs and outputs to ensure consistency and efficiency in information processing.
- Systemisation – Developing continuous, reliable workflows that optimise processes and reduce errors. While some systems may fail, they are stress-tested to ensure data pipelines function effectively.
- Externalisation – Expanding and refining these systems to enhance broader industry applications and ensure long-term sustainability.
By following this structured methodology, Zutari is enhancing efficiency in railway engineering workflows and supporting the industry’s shift towards data-driven decision-making and decarbonisation strategies.
The tools developed within this framework must be accessible and adaptable for different users within the company or across various units. A key consideration is how these tools are deployed—are they web-based applications, spreadsheets, online platforms, or scripts? The ultimate goal is to ensure they seamlessly integrate into engineering workflows and support efficient project delivery.
As the presenters highlighted, a guiding principle in their digital approach comes from their digital practice leader, who states:
“Code is not about replicating existing processes or how things have been done before—it is fundamentally a data-led method of problem-solving.“
With data playing a critical role in modern railway engineering, these tools are designed to enhance data processing, analysis, and decision-making. Following this, the presentation transitioned to Rick, who introduced insights from railway operations in Australia, providing a broader global perspective on decarbonisation efforts.
To illustrate the potential impact of decarbonisation, Rick used a practical example from the Pilbara region in Western Australia. As part of the offshoring unit, he is exposed to numerous global markets. In his view, few places demonstrate the scale and necessity of decarbonisation as effectively as the operations in the Pilbara.
Three major companies dominate the region’s railway networks. While they are not the only operators in the area, they are the largest and, apart from Vale, the biggest iron ore exporters in the world. Vale remains the largest global exporter, but these Pilbara-based companies are key players in the industry.
To provide some context on the scale of these operations—in 2022 alone, the Pilbara region exported approximately 900 million tonnes of iron ore, accounting for 40% of the global iron ore supply. This highlights the immense scale of the mining and railway operations in this region.
One of the most critical aspects of these operations is their locomotive fleet size, which is a key factor when considering decarbonisation efforts.
According to 2022 data, Rio Tinto operates approximately 225 locomotives, while BHP has 182 and, Fortescue Metals Group (FMG) runs a fleet of approximately 45 locomotives. While these numbers represent a substantial investment in railway infrastructure, Rick noted that they are relatively small compared to other major railway operators, such as Transnet. However, the operational status of Transnet’s locomotives remains unclear.
Beyond fleet size, the cost of transitioning away from diesel-powered locomotives is a significant factor. A new diesel locomotive may cost in the order of 2 to 4 million USD but shifting to battery-electric or alternative energy locomotives is expected to require an even greater investment. The sheer capital expenditure involved in replacing locomotives across the three largest iron ore exporters in the Pilbara alone could amount to billions of dollars, making careful evaluation of alternative energy solutions essential. The decision to decarbonise is not just a technological challenge but also a financial one, requiring companies to ensure their investment in new locomotive technology is both sustainable and economically viable.
The railway networks in the Pilbara serve as vital links between the mining sites and export hubs. Rick presented a map showing the key railway operators in the region, highlighting Rio Tinto’s extensive network, BHP’s heavy-haul infrastructure, and FMG’s and Roy Hill’s transport routes. While BHP, FMG, and Roy Hill primarily export through Port Hedland, Rio Tinto operates through two additional ports, reinforcing the scale and complexity of their logistics operations.
As the world’s largest iron ore exporters consider decarbonisation strategies, the scale, cost, and logistical challenges of transitioning their locomotive fleets must be carefully weighed.
Understanding which alternative energy solutions—whether battery-electric, hydrogen, or hybrid systems—are most feasible for heavy-haul rail operations will be central to the industry’s long-term sustainability efforts.
Out of interest, Port Hedland, exported 48 million tons of iron ore in January 2024 and 565 million tons for the whole of 2024. This immensely large-scale operation that underscores the significance of efficient and sustainable railway logistics.
So, how is decarbonisation going to be achieved?
There are a few possible options, all of which involve some form of electrification.
One approach is to replace diesel locomotives with conventional electric locomotives, installing overhead traction equipment (OHTE) across the entire railway network and powering operations with renewable energy. This would be a direct method of reducing carbon emissions but would require significant infrastructure investment.
Another alternative is battery-electric locomotives, which would still require charging infrastructure but offer a distinct advantage: the ability to store dynamic braking energy directly on board. In heavy haul operations, where large amounts of energy are dissipated during braking, this regenerative energy storage can improve efficiency and reduce overall power consumption.
Hydrogen-powered locomotives, using hydrogen fuel cells, present another option. While they do not require direct electrification along the entire route, they come with their challenges, including hydrogen production, storage, and distribution at the scale required for heavy freight operations.
Energy waterfall charts illustrating the energy requirements for both loaded and empty train trips are a useful tool to provide a quantitative perspective. Energy waterfall models serves as a data-driven tool for assessing the feasibility of different low-carbon propulsion systems in railway operations.
Heavy haul rail operations are uniquely different from commuter rail. Typically, a heavy train is transported from a higher elevation above sea level down to the port at sea level or as close to sea level as possible. The empty train is then hauled back up to the mine. This process results in a large amount of gravitational potential energy that must be removed from the system during the loaded trip.
For the most part, this energy dissipation is topology-dependent, but in general, the train is braking continuously as it descends. In current diesel-electric locomotives, dynamic braking converts this excess energy into electrical energy, which is then directed into the resistor grids and dissipated as heat.
Battery-electric locomotives, however, store that same braking energy in onboard batteries, making them a more energy-efficient solution. This technology is continuously maturing, and the major Pilbara miners have already ordered trial battery-electric locomotives to test their feasibility in heavy-haul operations.
The amount of dynamic braking energy generated is dependent on factors such as rolling resistance, pneumatic braking, and gravitational potential energy. In general, first principles modelling will not account for environmental conditions such as ambient wind and temperature fluctuations that may affect battery cooling requirements. However, in reality, conditions may vary significantly.
An extreme example would be a tropical cyclone – which demonstrated just how much wind conditions can impact rolling resistance. This resistance—referred to as Davis resistance—can fluctuate dramatically depending on wind speed and direction.
This is where computational design becomes crucial. By analysing historical wind data, engineers can characterise wind patterns in a given area and model how these conditions influence resistance and the resulting energy balance. This kind of data-driven approach allows for more accurate predictions and optimised operational strategies for an energy-efficient rail transport system.
For this particular project, Andrè noted that the environment itself is exceptionally harsh, with daytime temperatures reaching 44 degrees Celsius and a minimum of 28 degrees Celsius at night. Operating in such conditions presents unique challenges for railway technology, particularly in terms of energy storage and locomotive performance.
When it comes to hardware availability, the industry is not quite at the stage where off-the-shelf solutions can fully meet the demands of heavy haul decarbonisation. However, several independent OEMs are actively developing different technologies to cater to various markets. The choice of battery-electric locomotives (BELs)—as they referred to them—depends on several key factors, including battery capacity, chemistry, and cooling systems. These aspects directly impact the lifetime, efficiency, and performance of the batteries, especially in high-temperature environments.
Energy Management and Hybrid Solutions
Just as fuel efficiency is prioritised in internal combustion engines, optimising energy use is essential for battery-electric locomotives. Since a train can only carry a limited amount of stored energy, energy management plays a critical role in ensuring operational efficiency.
Recent developments in hybrid locomotive technology offer an alternative to fully electric systems. A hybrid locomotive combines diesel and battery-electric power, allowing for greater energy efficiency while still benefiting from regenerative braking—a concept similar to that used in electric vehicles like Tesla. The ability to store energy from braking and reuse it is particularly advantageous on downhill sections, where large amounts of energy are typically lost in conventional diesel operations.
One notable example is the Wabtec FLXdrive battery locomotive, developed in partnership with Roy Hill. This locomotive features a seven-megawatt-hour battery capacity, demonstrating the potential of large-scale battery storage in heavy haul operations. Given that multiple locomotives are typically used in a single train, this battery-electric solution has the potential to significantly reduce emissions.
In a real-world scenario evaluated by Wabtec, a 344-kilometre downhill run was found to be viable using regenerative braking – taking into account that the train is nearly three kilometres in length, weighing an incredible 33,000 tons.
Theory vs Practical
Rick explained that if battery-electric locomotives were to be implemented, some form of en-route electrification would still be necessary unless operating on a short railway line. At current battery energy densities, it is not viable for most railway lines to operate without any en-route electrification.
The advantage is that not all tracks need to be electrified, but some sections of the railway network would likely require electrification. One approach would be to build a siding where locomotives could be parked, plugged in, and charged at a dedicated point. However, from an operational standpoint, this is not ideal, as it would result in additional downtime and likely require companies to purchase extra locomotives to compensate for charging delays.
A more elegant solution would be on-the-fly charging, a novel technology that has been tested in the trucking industry but has not yet been trialled in heavy haul rail operations. While no large-scale implementations exist in rail, Rick suggested that if this system can work in other industries, it could potentially be adapted for railway applications.
This presents an optimisation problem—since the battery capacity alone is not enough to get you all the way back.
(As a side note, trams that operate on battery power without being connected to catenaries use a similar fast-charging system, with charging points embedded in the track.)
Andrè explained that one of the biggest challenges in working with big data is the sheer volume of information that needs to be processed. When simulating and modelling the expected performance of a battery-electric locomotive, key factors such as operation, energy consumption, and battery state of charge must be considered.
The scale of data involved is immense. Rather than dealing with thousands or even millions of data points, their models process close to a billion different data points. Each of these needs to be iterated, analysed, and processed, making data management and computational efficiency critical to their workflow.
Due to the parallel nature of specific computational processes, optimising software code to handle parallel data processing, rather than relying on a single CPU core, can significantly accelerate the processing speed. This approach has been particularly useful in their battery-electric locomotive simulations, where large amounts of data must be processed efficiently.
What they ultimately implemented was a solution leveraging NVIDIA’s CUDA parallel computing platform, an application programming interface designed to optimise parallel processing execution on GPUs. Andrè explained that CUDA allows them to take advantage of existing hardware to significantly speed up their simulations.
Rather than relying on a sequential processing pipeline, where each computation is handled one at a time, they distribute tasks across multiple GPU cores where data is processed across more than one thousand threads in parallel.
All the abstracts which were submitted to the Heavy Haul conference have been accepted for presentation and publication.
