Industry Data for the Energy Transition

Commodity prices: metals, materials and chemicals?

Annual commodity prices are tabulated in this database for 70 material commodities, as a useful reference file; covering steel prices, other metal prices, chemicals prices, polymer prices, with data going back to 2012, all compared in $/ton. 2022 was a record year for commodities. We have updated the data-file for 2023 data in March-2024.

Material commodity prices flow into the costs of producing substantively everything consumed by human civilization, and increasingly consumed as part of the energy transition. Hence this database of annual commodity prices is intended as a useful reference file. Note it only covers metals, materials and chemicals. Energy commodities and agricultural commodities are covered in other TSE data-files.

Source and methodology. The underlying source for this commodity price database is the UN’s Comtrade. This useful resource covers trade between all UN member countries, across thousands of categories, in both value terms ($) and mass terms (kg). Dividing values (in $) by masses (in kg) yields an effective price (in $/kg or $/ton). We have then aggregated, cleaned and averaged the data for 70 materials commodities.

The median commodity in the data-file costs $2,500/ton on an unweighted basis. Although this ranges from $20/ton for aggregates to $75M per ton for palladium metal.

2022 was a record year for material commodity prices. The average material commodity priced 25% above its 10-year average and 40 of the 70 commodities in the database made 10-year highs.

Steel prices reached ten-year highs in 2022, averaging $2,000/ton across the different steel grades that are assessed in the data-file. This matters as 2GTpa of steel form one of the most important underpinnings in all global construction. Our steel research is aggregated here.

Base metal prices averaged 40% above their ten-year averages in 2022, as internationally traded prices rose sharply for nickel, rose modestly for aluminium and zinc, and remained high for copper (chart below).

Battery metals and materials prices rose most explosively in 2022, due to bottlenecks in lithium, cobalt, nickel and graphite. This is motivating a shift in battery chemistries, both for vehicles and for energy storage. It also means that the average battery material in our data-file was higher priced than the average Rare Earth metal in the data-file (which is unusual, but not the first time).

Commodity chemicals all rose in 2022 across every category tracked in our chart below. These chemicals matter as intermediates. On average, sodium hydroxide prices reached $665/ton in 2022, sulphuric acid prices reached $140/ton and nitric acid prices reached $440/ton.

500MTpa of global plastics and polymers demand is covered in our plastics demand database. Both finished polymer prices (first chart) and underlying olefins and aromatics (as produced by naphtha crackers, second chart) prices rose sharply in 2022. Our recent research has wondered whether terms of trade are likely to become particularly constructive for polyurethanes.

Silicon prices matter as they feed in to the costs of solar, and traded silicon prices also reached ten year highs in 2022, before correcting sharply in 2023. Silica prices surpassed $70/ton, silicon metal prices reached $4,000/ton and polysilicon prices surpassed $30/kg (charts below).

The full database captures 70 globally traded materials commodities and their annual prices over time in $/ton, year by year, from 2012-2022. These are: Acrylonitrile prices, Adhesives prices, Aggregates prices, Aluminium prices, Ammonia prices, Battery Graphite prices, Benzene prices, Butadiene prices, Carbon Fiber prices, Cement prices, Cobalt prices, Cobalt Oxide prices, Cold Rolled Steel prices, Concrete prices, Copper prices, Copper Wire prices, Cumene prices, Electric Motor and Generator prices, Electrical Transformer prices, Epoxide prices, Ethanol prices, Ethylene prices, Ethylene Oxide prices, EVA prices, Formaldehyde prices, Glass Fiber prices, Gold prices, Graphite Anode prices, Graphite paste prices, HCl prices, HDPE prices, HF prices, Hot Rolled Steel prices, Hydrogen Peroxide prices, Integrated Circuit prices, LDPE prices, LiPF6 prices, Lithium Carbonate prices, Lithium Metal prices, Manganese prices, Manganese Oxide prices, Methanol prices, NaCN prices, Nickel prices, Nitric Acid prices, Paint prices, Palladium prices, PET prices, Phosphoric Acid prices, Platinum prices, Polyethylene prices, Polysilicon prices, Polyurethane prices, Propylene prices, Propylene Oxide prices, PTFE prices, Rare Earth Magnet prices, Scandium & Yttrium prices, Silica prices, Silicon Metal prices, Silver prices, Sodium Hydroxide prices, Stainless Steel prices, Steel Alloy prices, Sulfuric Acid prices, Toluene prices, Tubular Steel prices, Urea prices, Vehicle prices, Xylene prices, Zinc prices.

Oscar Wilde noted that the cynic is the man who knows the price of everything, but the value of nothing. To avoid falling into this trap, we also have economic models for most of the commodities in this commodity price database.

We will continue adding to this commodity price database amidst our ongoing research. You may find our template useful for running Comtrade queries of your own. Or alternatively, if you are a TSE subscription client and we can help you to use this useful resource, then please do email us any time.

Bill of materials: electronic devices and data-centers?

Electronic devices are changing the world, from portable electronics to AI data centers. Hence what materials are used in electronic devices, as percentage of mass, and in kg/kW terms? This data-file tabulates the bill of materials, for different devices, across different studies.

This data-file captures the bill of materials for electronic devices, such as cell phones, tablets, laptops, hard discs, solid state-drives, printed circuit boards, servers in data-centers, power supply units, adapters, copper cables and fiber optic cables.

Five materials make up c85% of the mass of typical electronic devices: advanced polymers (c20%), steel (c20%), glass (18%), aluminium (12%) and copper (12%). However, the exact numbers vary by product, as shown in the chart above.

Steel is the joint largest material exposure for electronic devices, although this is unsurprising, as steel is the most-used structural material on the planet, and in digital devices as well, it is used for the chassis/enclosure of data-center racks and other components, in switchgears, fans, heat sinks, etc.

Advanced polymers are the single most important material, both by mass and by specialization. HDPE and PVC are often used for electrical insulation in wires, cables and power supply units. PCBs are c35% epoxy resin. Polycarbonates are used in hard drives and optical disc drives. Solid state drives use specialty polymers, such as liquid crystal polymers.

Copper use from the rise of AI is more debatable. For example, several older studies suggest copper use in AI data-centers can range from 30-60 tons/MW. But on the other hand, these older studies may not fully reflect the scale-up of computing density per rack, which could reduce copper use to 10 tons/MW, albeit this would still tighten global copper balances by around 1% per year through 2030.

The ability to thrift out bulk material intensity factors by raising computing performance density, using advanced materials and manufacturing techniques is highly reminiscent of the same trend in new energies (raising solar efficiency, raising battery voltages). This creates opportunities in vapor deposition equipment, advanced polymers, and ultra-high purity materials including tantalum, silver, gold, tin, et al.

New energies: the age of materials?

Energy and AI: the power and the glory?

Finally, the vast range of advanced materials used in electronic devices and data-center components is shown by the vast number of materials in the data-file: ABS, Al2O3, Aluminium, Barium, Barium Titanate, Benzoic acid polymer, Brass, Calcium Oxide, Carbon, Cardboard, Chromium, Copper, Cromium, Dioxygen, Epoxy Resin, Ethylene Vinyl Acetate, Fan, Ferrous, Fibrous Glass Wool, Glass, Glass Fiber, Gold, HDPE, HVA-2, Iron, Iron Oxide, LCP Polymer, Lead, Li-ion batteries, Magnesium silicate, Magnesium, Magnets, Manganese, Neodymium, Nickel, Palladium, Paper, PCB, Pegoterate, Phenol polymer, Pigment Black 28, Polybutyl Terephthalate, Polycarbonate, Polycarbonate Acrylonitrile, Polycarbonates, Polyimides, Polymers, Polyurethanes, Proprietary, PVC, Silica, Silicon, Silver, Sodium Oxide, Solder, Steel, Styrofoam, Synthetic Rubber, Tantalum, Tin, Titanium, Vinyl Silicone Oil, Zinc.

Electromagnetic energy: Planck, Shockley-Queisser, power beaming?

Electromagnetic radiation is a form of energy, exemplified by light, infrared, ultraviolet, microwaves and radio waves. What is the energy content of light? How much of it can be captured in a solar module? And what implications? We answer these questions by modelling the Planck Equation and Shockley-Queisser limit from first principles.

Electromagnetic radiation is the synchronized, energy-carrying oscillation of electric and magnetic fields, which moves through a vacuum at the speed of light, which is 300,000 km per second.

Most familiar is visible light, with wavelengths of 400 nm (violet) to 700 nm (red), equating to frequencies of 430 (red) to 750 THz (violet).

At the center of the solar system, our sun happens to emit c40% of its energy in the visible spectrum, 50% as infra-red and c10% as ultraviolet, and very little else (e.g., X-rays, gamma rays at high frequency; microwaves and radio waves at high wavelength). But this is not a coincidence…

Planck’s Law: Spectral radiance as a function of temperature?

Planck’s Law quantifies the electromagnetic energy that will be radiated from a body of heat, across different electromagnetic frequencies, according to its temperature, the speed of light, Boltzmann’s constant (in J/ºK) and Planck’s constant (in J/Hz).

In the chart below, we have run Planck’s equation for radiating bodies at different temperatures from 3,000-8,000ºK, including the sun, whose surface is 5,772ºK. Then we have translated the units into kW per m2 of surface area and per nm of wavelength.

Hence by integration, the ‘area under the curve’ shows the total quantity of electromagnetic radiation per m2. If the surface of the sun were just 10% hotter, then it would emit c50% more electromagnetic radiation and 55% more visible light!

Charts like this also explain why the filament of an incandescent light bulb, super-heated to 2000-3000ºC is only going to release 2-10% of its energy as light. Most of the electromagnetic radiation is in the infra-red range here. And this is the reason for preferring LED lighting as a more efficient alternative. LEDs can reach 60-90% efficiency.

Planck’s Law and Solar Efficiency?

Planck’s Law also matters for the maximum efficiency of a solar module, and can be used to derive the famous Shockley-Queisser limit from first principles, which says that a single-junction solar cell can never be more than c30-33% efficient at harnessing the energy in sunlight.

Semiconductor material has a bandgap, which is the amount of energy needed to promote a single electron from its valence band into its conduction band: a higher energy state, from which electricity can be drawn out of a solar cell. For silicon, the bandgap is 1.1 eV.

Semiconductor physics: pièce de resistance?

What provides the energy is photons in light. The energy per photon can be calculated according to its wavelength. This involves multiplying Planck’s constant by the Speed of Light, dividing by the wavelength, and then converting from Joules to electronVolts. For a radiating body at 5,772ºK, the statistical distribution of photons and their energies is below.

So what bandgap semiconductor is best? If the bandgap is too high (e.g., 4eV), then most of the photons in light will not contain sufficient energy to promote valence band electrons into the conduction band, so they cannot be harnessed. Conversely, if the bandgap is too low (e.g., 0.5eV), then most of the energy in photons will be absorbed as heat not electricity (e.g., a photon with 2.0 eV would transfer 0.5eV into electron promotion, but the remaining 1.5 eV simply heats up the cell).

The mathematical answer is that a bandgap just above 1.3 eV maximizes the percent of incoming sunlight energy that can be transferred into promoting electrons within a solar cell from their valence bands to their conduction bands, at 43-44% (chart below).

If we run a sensitivity analysis on the bandgap, the next chart below shows that our 43-44% conversion limit holds for any semiconductors with a bandgap of 1.1-1.35eV, more of a plateau than a sharp peak.

The Shockley-Queisser limit is usually quoted at 30-34%, which is lower than the number above. In addition to the losses due to incomplete capture of photon energy, the maximum fill factor of a solar cell (balancing load, voltage and current) is around 77%, so only 77% x 44% = 34% of the incoming light energy could actually be harnessed as electrical energy. Moreover, in their original 1961 paper, Shockley and Queisser assumed an 87% efficiency limit for impedance matching relative to the 77%, which is why the number they originally quoted was around 30%.

Another issue is that the solar energy arriving at a given point on Earth has been depleted in certain wavelengths, as they are absorbed by the atmosphere. 1,362W/m2 of sunlight reaches the top of the Earth’s atmosphere. While on a clear day, only around 1,000W/m2 makes it to sea level at the equator. We know the atmosphere absorbs specific infrared wavelengths as heat, because this is the entire reason for worrying about the radiative forcing of CO2 or radiative forcing of methane.

Hence for an ultra-precise calculation of maximum solar efficiency, we should not take the Planck curve, but read-out the solar spectrum reaching a particular point on Earth, which will itself vary with weather!!

Multi-junction solar is inevitable?

The biggest limitation on the efficiency of single-junction solar cells is that they only contain a single junction. This follows from the discussion above. But what if we combine two semiconductors, with two bandgaps into a ‘tandem cell’. The top layer has a bandgap of 1.9eV (e.g. perovskite) and the second has a bandgap of 1.1eV (e.g., silicon). The same analysis now shows how the maximum efficiency can reach 44%.

Cells with multiple semiconductors are already being commercialized. For example, we wrote last year about heterojunction solar (HJT) and this year about the push towards perovskite tandems in solar patents from LONGi. It feels like the ultimate goal will be multi-junction cells that capture along the entire solar spectrum (chart below). It will simply take improvements in semiconductor manufacturing.

solar efficiency in record-breaking multi-junction cells

HJT solar: Indium summer?

Power Beaming and Other

Elsewhere in the electromagnetic spectrum, this data-file also contains workings into the energy efficiency of microwave energy, transmitting it through space and converting it back to useful electricity via rectennas.

Power beaming: into thin air?

All of the numbers and calculations go back to first principles, in case you are looking to model the Planck Equation, Shockley-Queisser limit, multi-junction solar efficiency, lighting efficiency, or other calculations of electromagnetic radiation energy.

Generac: power generation products?

Cost per kW of Generac product suite as a function of the generators' capacities. Different fuel types are in different colours.

Generac is a US-specialist in residential- and commercial-scale power generation solutions, founded in 1959, headquartered in Wisconsin, with 8,800 employees and $7bn of market cap. What outlook amidst power grid bottlenecks? To answer this question, we have tabulated data on 250 Generac products.

Generac‘s $4bn pa of sales, in 2023, were >50% residential, >35% commercial, 10% other. 80% was domestic within the US, and 20% was international. 12% is attributed to ‘energy technologies’ which includes storage, solar MLPE, EV charging, smart thermostats, electrification, etc. But what about the product mix? How is it exposed to power grid bottlenecks? Or indeed a broader US construction boom?

In this data-file, we have tabulated details on 250 Generac Products. 70% are generators, and another c10% are transfer switches to connect generators to loads. Our data show the breakdown of units by size, fuel, prices and other technical parameters.

Residential solutions comprise 55% of Generac’s revenues, as 6% of US homes now have standby generators, but a smaller share of the SKUs. Our data show the average size (in kW), list prices (in $) and costs (in $/kW), but we also think low efficiency in some of these residential generation units is not entirely helpful for decarbonization aspirations.

Generac’s larger industrial generators range from 100kW to 2MW in size. Generac’s diesel-fired units cost $500/kW and are c30% efficient, while its larger gas-fired units cost $700/kW and are c25% efficient. These units tend to generate 10-40 kW/m3 of space, and discharge exhaust at 700-800ºC, so they could find application amidst grid bottlenecks?

In our base case model for a diesel genset, we assume a 16-20c/kWh LCOE is needed for a $700/kW installation of a 10MW unit with 42% efficiency buying wholesale diesel. Generac units would appear to have higher costs for reasons in the data-file.

In our base case model for a CCGT, we need a 6.5c/kWh LCOE at an $850/kW installation of a 300MW unit with 55% efficiency buying $4/mcf wholesale natural gas. Again, for the Generac units, we get to a higher LCOE, yes this is the area we think could be most economically justified by growing power grid bottlenecks, especially at industrial facilities that can harness the 700-800ºC waste heat.

Outside of the generation business, remaining SKUs comprise transfer switches to connect generators to loads, pressure washers, light towers for construction, pumps, batteries and mobile heaters, hence there may be heavy exposure to construction and infrastructure projects.

Grid connection sizes: residential, commercial and industrial?

Typical Sizes of Grid Connections of different residential, commercial and industrial facilities in kW. The largest connections are needed for green hydrogen, aluminium and data-centers.

What are the typical size of grid connections at different residential, commercial and industrial facilities? This data-file derives aggregate estimates, from the 10kW grid connections of smaller homes to the GW-scale grid connections of large data-centers, proposed green hydrogen projects and aluminium plants. Also included are our notes on each category, data into 5GW of US micro-grids and assessments of the possible winners-and-losers from growing power grid bottlenecks.

A typical home in the developed world currently has a 10-15kW maximum power capacity. Exceeding this load may cause its circuit breaker to trip. Hence some homes may need upgraded grid connections to add electric vehicle chargers or heat pumps.

Office buildings that have crossed our screens typically require 50-500kW grid connections, rising to MW-scale connections for larger office buildings with 15,000m2 of space or more.

Other facilities with which we are all familiar include Walmart Super-Centers (1.3MW average grid connection), medium-sized hospitals (5MW), London tube underground stations (6MW) and airports (c10MW). Although again, capacity varies with size.

One excellent source for these numbers is looking at the sizes of around 1,000 US microgrids, with over 5GW of capacity, of which around 50% were constructed in the last decade, powered by CHPs (50%), gas turbines (17%), diesel generators (10%), solar (12%), wind (5%) and hydro (6%), and supported by 5 GWh of battery storage.

Number of microgrids in the US and the total capacity of microgrids constructed per year.

Light manufacturing and food-processing facilities will also tend to have an average grid connection of around 1MW, across c50,000 such facilities around the United States, which are aggregated in our database of electricity consumption by sector.

For larger facilities, we turn to our own economic models, to quantify the typical grid sizes. As usual, facilities with larger capacities will have larger power grid connections.

Size of grid connections will range from 10-30MW for 200kTpa chlor-alkali plants, 1MTpa cement plants, 1MTpa CCS compressors or 100,000 vehicle per year auto plants.

Finally we come to the true monsters, with grid connections above 100MW, such as larger data-centers, aluminium plants and proposed green hydrogen facilities, some reaching 2-3GW in scale.

Size of grid connection and growth trajectory determine whether industrial facilities will realistically need to generate their own power in increasingly bottlenecked power grids.

Renewables plus batteries: co-deployments over time?

More and more renewables plus batteries projects are being developed as grids face bottlenecks? On average, projects in 2022-24 supplemented each MW of renewables capacity with 0.5MW of battery capacity, which in turn offered 3.5 hours of energy storage per MW of battery capacity, for 1.7 MWH of energy storage per MW of renewables.

Co-deployments of renewables and batteries are tracked in this data-file, tabulating the details of over 100 projects that combined a grid-scale battery with their construction of wind and/or solar assets. The average of these projects in 2022-24 added 0.5MW of battery capacity per MW of renewables, with 3.5 hours of energy storage, for 1.7 MWH of energy storage per MW of renewables.

These numbers have all approximately doubled versus a decade ago, when the co-development of renewables plus batteries was a rarity, and tended to occur at smaller scale. This suggests that rising interconnection costs and risks of curtailment are motivating greater deployment of batteries.

A dozen recent renewables plus battery projects are very large in size, ranging from 100-1,000MW of battery storage capacity, almost all being developed in 2020 or thereafter (chart below). For example, the 875MW Edwards & Sanborn solar project in Kern County, California is co-located with 971MW of BESS units from LGChem, Samsung and BYD.

Conversely, the largest batteries from pre-2017 are c30-50MW in size, and many of the technical papers over this timeframe are consciously considering different battery chemistries — lead-acid, sodium-sulphide — rather than today’s projects that are predominantly LFP lithium ion.

The duration of these grid-scale batteries has also increased from 2.6 hours prior to 2020 to 3.5 hours after 2020, with the upper decile projects hacing 5-6 hours of storage (chart below).

It is fine to co-develop renewables with batteries, but it is also more costly. A utility-scale solar project might cost $1,000/kW. A grid-scale battery might cost $1,500/kW. Hence combining 0.5MW of batteries per MW of solar might cost $1,750/kW in total, re-inflating levelized costs of solar by around 50-75%, but still possibly less costly than funding network upgrades.

Our long-term forecasts for power grid capex assume that 0.15MW of grid-scale batteries will be deployed per MW of renewables capacity, comprising a mixture of standalone renewables projects and renewables projects that are co-developed with batteries. And there could be upside?

Companies that stood out in deploying and supplying grid-scale batteries are noted in the data-file.

Offshore oilfields: development capex over time in Norway?

Across 130 offshore oil fields in Norway, going back to 1975, real development capex per flowing barrel of production has averaged $33M/kboed. Average costs have been 2x higher when building during a boom, when one-third of projects blew out to around $100M/kboed or higher. The data support countercyclical investment strategies in energy.

This data-file captures the development capex for 130 oilfields offshore Norway, from 1975-2023, in real 2023 USD per flowing barrel. Specifically, data on each field are publicly available from the Norwegian Offshore Directorate, in NOK, while we have cleaned the data, converted it into USD using historical exchange rates from the Bank of England and then translated the numbers in 2023$ real terms.

The average Norwegian offshore field cost $33M/kboed to develop, comprising $3.3bn of development capex, peaking at 100kboed of hydrocarbon production, of which two-thirds was liquids and one-third was gas. There was no material difference in the costs of oilfield or gas field developments (chart below).

Development capex of Norwegian offshore oil and gas fields. No significant difference between oil and gas fields in terms of capex/kboed of peak production.

Development costs can also be indexed over time, running at a relatively constant $30M/kboed in the 1980s, 1990s and early 2000s. Total development capex actually declined from 1970s levels over this time frame, in real terms, due to learning curve effects.

Activity levels have also varied. On average, there have been 12 fields in development across the Norwegian Continental Shelf. However, at peak, there were 20-25 fields under development in 2011-2015.

During this timeframe, the average development cost also doubled to $60-70M/kboed. The distribution of outcomes was also much wider during this timeframe of intense activity levels, with an intensified risk of ‘capex blow-outs’, as one-third of the projects exceeded $100M/kboed.

On the other side of the spectrum, low-cost developments can cost as little as $5-10M/kboed, especially using concepts such as tiebacks and unmanned platforms, and especially when the supply chain had slack capacity. A nice example is Johan Sverdrup. There was also a -16% correlation between field size and offshore development costs.

Our cleaned data-set is available for download below. Across all energy sub-sectors, there are benefits to counter-cyclical investment, whether we are considering oil, gas, LNG, nuclear, wind, solar or power grids.

Global energy demand by end use?

Breakdown of global useful energy demand across 50 categories. The largest are electric motors and residential heat, followed by steel, general manufacturing, and plastics.

This data-file is a breakdown of global energy demand by end use, drawing across our entire research library, to disaggregate the global energy system across almost 50 applications. Such as transportation, heat, electricity, materials and manufacturing. Numbers, calculations, efficiencies and heating temperatures are in the data-file.

Primary global energy demand runs at 160,000 TWH pa. Useful global energy demand (net of efficiency losses) runs at 80,000 TWH pa. Per our global energy supply models, it is relatively easy to disaggregate energy supply, by summing across oil, gas, coal, nuclear, hydro, wind and solar, biomass and other.

A breakdown of global energy demand is more challenging. But we have slowly been building up a library of economic models and supply-demand models, looking theme-by-theme, material-by-material, market by market. Hence we have attempted a full granular breakdown of global energy demand by end use here.

As simple rules of thumb, electricity generation absorbs almost 40% of primary energy, heat is 30%, transportation is almost 25%, and 10% is feedstocks for materials. However, some of the categories overlap. Materials and manufacturing absorb c35% of global primary energy to make 60GTpa of stuff, drawing upon electricity, heat and feedstocks.

A breakdown of primary global energy demand by category is plotted below. The largest categories are electric motors (15%), passenger cars (13%), residential heat (10%), manufacturing processes (6%) and other electrical appliances (5%). Although note that this is also because these are some of the broadest categories in the data-file.

The database of global energy demand also allows us to quantify the most and least efficient value chains. For example, passenger cars comprise c13% of global primary energy in the chart above but only c5% of global useful energy, due to the relatively low efficiency in internal combustion engines. Conversely, residential heat is highly efficient, comprising c10% of primary energy above but 13% of useful energy.

The breakdown of global energy demand by end use also allows us to disaggregate global heat, which comprises 30% of primary global energy. 50% is used residentially or commercially at temperatures below <100◦C. Conversely, at the other end of the spectrum, 20% is ultra-hot industrial heat >1,000◦C (chart below).

Breakdown of total global heat demand by temperature and category. Residential heat is low temperature but makes up a large fraction of total demand. Materials production requires temperatures up to a few thousand Celsius.

The full data-file includes estimates of global energy demand across almost fifty end uses: agriculture, air conditioning, air separation, aluminium, ammonia, asphalt, aviation, bleach, buses, cement, chlor-alkali, coal production, commercial heat, cooking, copper, desalination, electric motors, electrical appliances, glass, graphite, heavy trucks, hydrogen, industrial acids, internet, lighting, lithium batteries, LNG liquefaction, manufacturing, methanol, oil & gas E&P, oil refining, other metals, paper & pulp, passenger cars, pipelines, plastics, polysilicon, refrigeration, residential heat, shipping, solar modules, steel, trains, transmission, two-wheelers and wind turbines.

CCS: what CO2 purity for transport and disposal?

CO2 purity required for various purposes. The highest purities are required by food, beverage, and medical purposes, as well as shipping and liquefaction. CO2 disposal has the highest variability. For any purpose the purity must still be at least 90%.

The minimum CO2 purity for CCS starts at 90%, while a typical CO2 disposal site requires 95%, CO2-EOR requires 96%, CO2 pipelines require 97% and CO2 liquefaction or shipping requires >99%. This data-file aggregates numbers from technical papers and seeks to explain CO2 purity for transport and disposal.

Our roadmap to net zero includes 7GTpa of CO2 disposal, across various technologies, from straight-run amine CCS, to DAC, CO2-EOR, blue hydrogen SMRs and ATRs, oxy-combustion, potassium carbonate, other sorbents, next-gen membranes. But what CO2 purity levels do these technologies need to meet?

Energy efficiency is the first reason that CO2 purity matters. As a very simple rule of thumb, compressing a gas stream to 80-200 bar requires 90-120 kWh/ton of compression energy. If the gas stream is only 90% CO2, then the energy costs per unit of CO2 are around 10% higher.

Or more. The reason it is necessary to compress CO2 to >80-bar is so that the CO2 will transition into a dense (super-critical) phase. The phase diagram below shows the critical point for pure CO2. But impurities require higher pressures before CO2 reaches supercriticality.

Larger pipelines are also required to move larger quantities of gas at higher pressures. This matters because larger pipelines with thicker walls have higher capex costs, per our data-file into gas pipeline costs.

The other key reason that CO2 purity matters for CCS is that if the gas stream has less than 100% CO2, then by definition, it must contain something else. Clearly, issues will arise is the ‘what else’ is toxic or hazardous (e.g., H2S, amine degradation products such as nitrosamines, NOx, SOx, etc). But even innocuous contaminants can have an impact.

Water is a key impurity that must be managed in a CO2 pipeline. If puddles of water precipitate out, then they will slowly start dissolving CO2, and greatly accelerate pipeline corrosion. CO2 + H2O -> H2CO3 (carbonic acid). H2CO3 -> 2H[+] + CO3[2-]. Fe(s) + 2H[+](aq) -> Fe[2+] (aq) + H2 (g). It is never good to dissolve your pipeline from the inside out. Furthermore, the H2 can cause further stress cracking.

Hence water is usually limited to <500ppm, ideally <50ppm. This is more of a convention than a hard rule (examples are tabulated in the data-file). Usually, as much as 4,450 ppm of water will be soluble in pure CO2 at 40◦C and 100-bar pressures. Even with 10% nitrogen impurities, this only reduces to 3,400 ppm. Some amine breakdown products, or NO2 can have a more “dramatic effect” on the width of the phase envelope.

CCS challenges: by any amines necessary?

But there is also always a margin of safety for cold spots, bends in the pipeline or in the case of depressurization. A pipeline operator has the prerogative to set whatever standards it deems necessary to protect the longevity and efficiency of its investment. Off-spec CO2 may be charged a materially higher transportation tariff if it is accepted at all.

CO2-EOR also requires a higher purity than straight-run CCS, in order to promote miscibility of the CO2 with oil in the subsurface, which will help to swell and mobilize it. This is less important for simple geological disposal.

CO2 transportation by ship or CO2 transportation by truck also requires very high purity, well above 99%, in order to liquefy the CO2, at 20 to -50◦C and 7-15 bar pressures. For example, any residual water vapor in the stream is going to freeze out and plug the system. So this requires the highest purity of CCS value chains and dedicated dehydration.

Carbon capture on ships: raising a sail?

CO2 purity for CCS will generally need to be above 95%, and ideally will be as high as possible. This favors CCS technologies that can create highly concentrated CO2 streams from exhaust gases of differing CO2 concentrations. But the limits are not overly strict, or likely to deter CCS, in our view. For more related research, please see our overview of CCS value chains.

Nafion membranes: costs and hydrogen crossover?

Perfluorinated sulfonate (PFSA) membranes, such as Nafion, are the crucial enabler for PEM electrolysers, fuel cells and other industrial processes (e.g., chlor-alkali plants). The market is worth $750M pa. The key challenges are costs, longevity and hydrogen crossover (in mA/cm2), which are tabulated in this data-file.

Nafion was first synthesised by Walther Grot, of E.I. DuPont de Nemours in the 1960s, as a robust cation exchange membrane for the chlor-alkali process, which had previously used materials such as asbestos to separate the anode and cathode sides of the cell.

Today, Nafion’s original patents have expired, and other producers besides Chemours produce PFSA polymers, under various different brand names. But in this article, we will refer to Nafion as a catch-all for similar PFSA membranes.

Nafion turns out to be a remarkable polymer, the enabling membrane for proton exchange membrane electrolysers and fuel cells. It consists of a fluorinated polymer (PTFE) backbone, off of which branch ether groups, connecting to further fluorinated polymers, ultimately terminating in sulfonate groups (SO3H).

Illustration of the chemical structure of Nafion membranes.

The sulfonate groups are strongly polar, exhibiting surface ultrastructural properties that “appear utterly unlike anything else”. They absorb water and form helical channels of 2-3 nm diameter, through which protons can ‘hop’. So can other small cations. But anions and gases are impeded. This is even the reason that the SpaceX’s Dragon space probe used Nafion membrane to dehumidify air against a vacuum.

Costs of Nafion membranes are estimated at $2,000/m2, based on data-points from online sources and technical papers. Thus the membranes will comprise $100/kW of cost in an electrolyser at 1,000 mA/cm2. This feeds into our electrolyser cost model, and the numbers can be stress-tested in this data-file.

The key challenge with Nafion and other PFSA membranes in a hydrogen electrolyser is hydrogen crossover. For example, this means that H2 forming at the cathode of an electrolyser can diffuse back across the membrane in very small quantities, towards the anode side, and re-oxidize into H2O. This hurts Coulombic efficiency by 0.1-1%.

But the more pressing challenge of hydrogen crossover is that hydrogen oxidation at the electrolyser anode will form not only water, but also peroxide radicals, which then have an annoying habit of degrading catalysts in the anode, the membrane itself, and other cell components.

Hydrogen crossover increases linearly with temperature, with H2 partial pressure (itself a function of current density!), for thinner membranes (which have lower resistance and are aimed at maximizing efficiency), and finally with age. Older or degraded membranes have 2-10x higher hydrogen crossover.

Membrane degradation may thus count against putting electrolysers and fuel cells into mobile applications, such as hydrogen cars, hydrogen trucks and planes. For more details, see our overview of electrochemistry and our overview of electrolyser degradation. Details on hydrogen crossover and possible solutions are in the data-file.

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