A new open-access publication in Scientific Reports demonstrates a structured way to categorize agricultural residues for biochar. The researchers combine measurements of the material with a decision model to allow for a fair comparison of apples and oranges. This is also relevant in the Netherlands, as the choice of the residue stream determines the yield and quality of biochar and affects applications in soil, industry, and carbon credits.
The authors evaluate three groups of residual streams: wood residues, food waste, and crop residues such as straw. They look at properties that reveal important information about biochar, such as lignin and carbon content, moisture content, and volatile matter content. Based on this data, the model weighs the options and ranks them. This creates a transparent picture of which residual stream is most promising for biochar.
How did they handle that?
First, the researchers measure the composition of each material. This includes elemental analysis, the distribution of cellulose, hemicellulose, and lignin, and a measurement of the calorific value. They also use infrared spectra to confirm which chemical groups are present. Then, they enter the data into a combination of fuzzy AHP and TOPSIS. These are proven methods for weighing multiple criteria simultaneously and arriving at a single final score for each raw material. The premise is simple: what is important is given more weight than what is less important. The samples in the study were collected in the Białystok region of Poland.
What came out
Among the wood residues, ash wood performed remarkably well. It is rich in lignin and carbon and low in moisture, properties that together often lead to a higher biochar yield and a more stable product. Among the food residues, potato peels and wheat bran performed best. Among crop residues such as straw, triticale straw performed particularly well. In a heating test, a combination of ash wood and triticale straw yielded the most solid material, indicating that the mixture can yield significant biochar through pyrolysis.
In practice, the step-by-step plan provides guidance in selecting input materials in the Netherlands. Designers and purchasers of pyrolysis installations want predictable quality and a good yield. The same set of criteria applies to Dutch waste streams, from prunings to grain straw. The model also helps in tenders or collaborations between farmers and processors to make prior agreements about raw materials, minimum quality, and price.
Side notes
The research remains lab-based, using a limited set of samples from the Białystok region in Poland. The results provide guidance, but don't yet fully address supply chain costs, logistics, or contaminants in practice. Furthermore, the best choice can vary by application. Those primarily looking for soil improvement may consider different properties than those seeking biochar for water purification or materials. This is precisely why a transparent weighing model is useful, as it allows you to adjust priorities.
Biochar is increasingly on the agenda in agriculture and industry. Yet, the question of which residual stream is best used remains unclear. This study offers a practical way to substantiate choices with measurement data. This accelerates discussions between growers, processors, and buyers and reduces the risk of disappointment in practice.
Link to research: Optimizing agricultural biomass selection for biochar production using multi-criteria decision-making
