In health science, we often use Body Mass Index (BMI). We know it’s an imperfect, simplified metric, but it provides a standardized starting point for assessment. What if the bioeconomy had a similar tool for its feedstocks?
For years, researchers have struggled to numerically compare different types of biomass. Comparing the energy potential of hardwood, herbaceous crops, and agricultural waste is challenging because their chemical compositions are so diverse. As noted in a recent paper by Andrzej Białowiec and Ewa Syguła, the “type” of biomass has largely remained a qualitative descriptor, making it difficult to derive meaningful, universal trends.
Now, that new paper in the European Journal of Wood and Wood Products offers a thought-provoking perspective, introducing a novel parameter they developed called Carbon-Relative Molar Mass (CRMM).
The Proposal: Carbon-Relative Molar Mass (CRMM)
At its core, CRMM is an attempt to distill the complex elemental composition of organic matter into a single, chemically-grounded number. It goes beyond simple metrics like carbon content by integrating the mass and molar proportions of all key elements (C, O, H, S, N). The process involves calculating the ash-free elemental content and then normalizing the number of moles of each element against carbon—the backbone of all organic matter. The result is a single value, expressed in g/mol, that represents the entire elemental profile of the material relative to its carbon structure.
The Initial Test: Predicting Energy Content
To test their concept, the authors explored how well CRMM could predict the Higher Heating Value (HHV) of a material, a key measure of its stored energy. The initial results are intriguing:
- A Strong Correlation: Using a model based on CRMM, the researchers were able to explain nearly 86% of the variance in the analytically measured HHV of their samples. This is notably higher than the roughly 80% accuracy of many typical empirical models that rely on elemental composition alone.
- A Nuanced Comparison: While the correlation was strong, the authors honestly report the model’s average error (RMSE) was 1.78. They note this is slightly higher than some benchmark models, suggesting a trade-off: the CRMM model may better capture the overall trend in the data, but not without some deviation in its specific predictions.
The Limitations
This paper is not presented as a final solution, but as the beginning of an investigation. The authors are transparent about the work that still needs to be done:
- A Controlled Dataset: The impressive 86% correlation was achieved on a very specific dataset, i.e., “model biomass” created from pure chemical components (alkali lignin, cellulose, xylan) and the biochars derived from them. Its applicability to complex, real-world biomass with high ash content or different compositions is not yet proven.
- Breakdown at the Boundaries: The predictive model becomes less reliable at extreme CRMM values (specifically, for real materials with a CRMM above 30 g/mol). This is a significant challenge that must be addressed before it can be applied more broadly.
- A Call for More Research: The primary conclusion of the paper is that this is a “promising discovery” that “warrants further study with a wider range of biomass types and other solid fuels”.
A New Lens, Not a Silver Bullet
The research by Białowiec and Syguła offers a valuable new lens for biomass characterization. The power of the CRMM concept is not that it is a perfect tool today, but that it represents a different way of thinking. It’s an attempt to move from disparate, empirical models to a more unified, chemically-grounded parameter that could, in theory, be applied to any type of organic matter.
The question isn’t whether CRMM is a flawless solution now, but whether this approach—of creating integrated parameters—can lead us to the more universal and reliable models we need for a truly mature and data-driven bioeconomy.
