There’s nothing more exciting than taking your laboratory-validated microalgae process to industrial-scale production.
But this step is also a crucial one, because there’s a real risk of inaccurate production forecasts undermining your potential business case. Unless you take steps to avoid this ‘scale-up performance gap’,, it can lead to costly iterative redesigns to your process and uncertainty for investors.
This blog outlines the best way to ensure you can rapidly scale-up production process for microalgae, with clear and reliable expectations of quality and quantity.
The reasons for scale-up performance gaps can be quite complex, but by understanding the causes you can avoid unexpected surprises when scaling your production volumes.
What is the scale-up performance gap in microalgae production?
‘Scale-up performance gap’ refers to the discrepancies between your projected yield and quality of microalgae, versus the actual measured results. This is most often seen when you move from a lab scale R&D production process to a pilot or commercial-scale process, but it can also occur whenever there are unexpected changes to growing conditions.
Quite simply, techniques and growth parameters – which worked perfectly for 5 liters – could easily fail to deliver expected results when this is scaled up to 500, 5000 or 50.000 liters. So, you need reliable confirmation that the process parameters generate an identical performance when scaling up your microalgae process.
As you can imagine, this scale-up effect can be very negative; it can undermine your business case, spook investors, and interrupt your production schedules. When it happens, the only response is to redesign your system repeatedly, with multiple cycles of validation for each change you make. This can severely impact your timelines for a commercial launch if multiple iterations are needed.
Thankfully, decades of experience in this sector have shown that the scale-up performance gap – or “data shock” – can be effectively avoided with the right strategy and process design.
6 ways to avoid data shock when scaling production
These are the main reasons why lab results can create false expectations and disappointment when they are replicated at a larger scale, and how to avoid them.
#1 Culture shock
One major driver of a scale-up performance gap is the gap between academic R&D and commercial operations. In the lab, success is measured in publications and proof-of-concept results; in a factory, it’s throughput, uptime and unit cost.
If the R&D phase is run without those commercial realities in mind, the process is often optimised for “nice data” rather than scalable performance. High light levels, complex control strategies or fragile operating windows may look great in a paper but are unstable, unsafe or uneconomic at scale. In other words, the scale-up risk is designed into the process from day one.
In an R&D setting, it is natural to optimise for the best possible values and to exclude outliers, so that the dataset is clean and publishable. However, those “outliers” can sometimes reveal important vulnerabilities in the process when conditions drift away from the ideal. The result is a strong focus on how to achieve peak performance, rather than on how to consistently reproduce what is economically feasible at industrial scale.
This is why close collaboration between R&D and production is so valuable. When the production team is actively involved in guiding experimental design and setup choices, they can highlight operational priorities and constraints from the outset, ensuring that the process is both high-performing and scalable.
#2 Control over process parameters
With a near-ideal setup in the lab, it’s much easier to obtain impressive results; you’re working in a well-defined environment that is easier to control. The stability of processes in a laboratory setting is also naturally higher than what is achievable – or economically realistic – at commercial scale.
However, this ‘ideal scenario’ becomes misleading and disappointing, because it can rarely be replicated when you move towards industrial production.
A concrete example would be assuming that your culture receives the maximal measured sunlight intensity in summer, 24/7, throughout the entire production process. These growing conditions result in a high forecast for productivity, when, in reality, these numbers cannot be obtained at a large scale. Light intensity, distribution, and the level of control achievable with a flat panel technology is difficult to scale. Despite the advantages for small scale R&D, the amount of light required is not economically feasible for industrial production.
Ultimately you may need to make a compromise, so you can generate a profitable output without being overbalanced by expensive ‘lab oriented’ processes.
For example, if you intend to use natural lighting at production scale, then your pilot should also use the same setup. This allows you to collect data on a range of conditions that represent the real-world variability you can expect.
#3 Media formulation
Researchers will generally use analytical grade chemicals in the lab, but the use of these is not cost effective at larger scales.
To avoid a performance gap, validation in the lab should be based on the actual, industrial grade nutrients and other chemicals you will use for production.
#4 Volumetric light supply
There are some things that you simply cannot replicate at the lab scale, because of fundamental differences in the equipment used.
For example, a lab-scale compact photobioreactor will typically use a smaller pipe diameter of 32mm, while Lgem’s pilot and industrial-scale pipe diameter is 65mm.
This seemingly small difference in infrastructure has a big effect on the growing conditions for your culture because not least because the light must pass through a greater volume of fluid, changing the light distribution and other key parameters. This must be compensated for by increasing the light intensity accordingly.
To ensure that each cell receives the same photosynthetically active radiation per day, you will need to increase the exposure level and/or time. Or, looking at it another way, you may need to consider what volumetric light supply rate you can profitably sustain at large scale, and use this to determine the conditions used for R&D, so your data reflects what is realistically achievable.
#5 Light regime and source
Your production setup needs to be evaluated and validated based on the light quality it can actually produce, instead of ideal conditions in the lab.
If you plan on relying on 100% natural light at production scale, then this must also be the basis for your validation. For example, the Helios system uses natural sunlight, and this will vary depending on the weather and season.
However, there’s also no guarantee that natural sunlight in a laboratory setting will match what is achieved at a different time or season in a massive greenhouse. Temperature is also a factor, as this may fluctuate considerably in a greenhouse setting. Such fluctuations will naturally have a greater effect on smaller culture volumes, and this alters the photosynthesis rates you can obtain.
To ensure predictable results, it may be worth considering the use of artificial LED lighting. This can either reduce the variability of natural lighting, or replace it altogether with a 100% predictable output. It also allows you to tightly control the spectra used to optimize production of specific metabolites.
Artificial lighting also reduces the variables involved, and this helps you identify the ideal growth parameters for your strain and microalgae product in the R&D phase.
#6 Data model consistency
The most important growth parameters are dilution rates, biomass concentrations, biomass productivity, and target metabolites such as pigments, fatty acids, or other molecules. When these are validated with conditions that match your large scale production process, these can be reliably extrapolated to a commercial scale.
Continuous datalogging from integrated sensors helps ensure that you have a true picture of growing conditions. In theory, this enables you to reproduce the successes in a lab at a far greater scale.
However, data needs to be collected in a comparable way and using the same kind of sensors. This ensures you can compare ‘like for like’ in all setups, for instance by using the AlgaeOps product from Polariks.
Implementing chemostats and other operational strategies can give you better insights and control over the outputs in a commercial scale facility.
Bringing your microalgae product to market
The microalgae market has a bright future. Innovative companies and researchers are continuing to identify new potential products and applications for microalgae.
We can expect considerable growth in the algae industry in the next five years and beyond as these are given regulatory approval and introduced to the market.
However, to ensure profitable production of new microalgae products, it’s essential that businesses and investors can lean on reliable data collection methodologies. These enable data to be extrapolated successfully at all scales of production. The foundation for this is to determine the ideal operational window for producing target compounds at lab scale using commercial-scale conditions. This allows data to be extrapolated to achieve reproducible results at industrial scale.
Proven technology is a significant advantage as it builds on many years of successful research and optimization. In combination with a solid data collection methodology, you gain a stable basis for translating data into predictable changes in the setup.
Drawing on the experience and expertise of the AlgaeHUB® helps de-risk your business case. By pairing your product with proven process know-how, you can avoid the most common pitfalls. This collaboration enables validation at multiple scales with far lower capital expenditure, while generating data robust enough to support investment decisions. It also allows you to produce cost-effective product batches for testing, customer trials, and early market entry, further strengthening your business case.
Want to discuss your project or leverage the expertise of seasoned microalgae experts?
Check out the AlgaeHUB® to learn more about how you can take your project to the next level.
