Researchers have developed a powerful machine learning framework that can accurately predict and optimize biochar production from algae, offering a faster and more sustainable path toward carbon rich materials for climate mitigation, soil improvement, and environmental applications.
Biochar is a solid, carbon rich product created when biomass is heated in low oxygen conditions. It has attracted global attention for its ability to store carbon long term, improve soil health, and support renewable energy systems. While most biochar is made from wood or agricultural residues, algae are emerging as a promising alternative because they grow rapidly, require little land, and can be harvested from freshwater or marine environments. However, producing high yields of biochar from algae has been challenging due to their complex chemistry and sensitivity to processing conditions.
In a new study published in Biochar, researchers combined experimental data with advanced machine learning models to identify the conditions that maximize algal biochar yield while reducing the need for costly trial and error experiments.
"Traditional biochar optimization relies on extensive laboratory testing, which is time consuming and expensive," said corresponding author Muhammad Nouman Aslam Khan. "Our approach uses artificial intelligence to learn from hundreds of previous experiments and guide future production in a much more efficient way."
The research team assembled a large dataset from 48 peer reviewed studies published over the past decade, representing 373 experimental data points for algal biochar production. These data included information on algae composition, such as carbon, nitrogen, volatile matter, and ash content, as well as key processing variables like temperature, heating rate, residence time, particle size, and nitrogen flow rate.
Several machine learning models were tested, including decision trees, support vector machines, Gaussian process regression, and ensemble tree methods. The researchers further enhanced these models using optimization algorithms inspired by natural systems, including genetic algorithms and particle swarm optimization.
Among all approaches, an optimized ensemble tree model performed best, accurately predicting biochar yield across a wide range of algal feedstocks and processing conditions. The model achieved strong agreement with experimental results and was able to pinpoint which factors matter most.
"Temperature turned out to be the dominant control on biochar yield, followed by volatile matter and heating rate," Khan explained. "This confirms what experimentalists have observed, but now we can quantify these effects and understand how they interact."
Using inverse optimization, the model identified an optimal set of conditions that could produce a biochar yield of more than 76 percent. These predictions were then validated experimentally using algae samples collected from freshwater reservoirs, with measured yields closely matching model estimates.
Beyond prediction accuracy, the study also assessed uncertainty and sensitivity using Monte Carlo simulations and Sobol analysis. These tools revealed that many production parameters influence biochar yield through complex interactions rather than acting alone, highlighting the value of machine learning for capturing nonlinear behavior.
"This framework is not just about prediction," said Khan. "It helps researchers and industry partners design smarter experiments, reduce waste, and scale up algal biochar production more sustainably."
The authors note that algae based biochar could play an important role in carbon sequestration, wastewater treatment, soil amendment, and renewable energy systems, particularly in regions where algal biomass is abundant.
By integrating machine learning with experimental validation, the study demonstrates a practical pathway for accelerating biochar innovation while lowering costs and environmental impacts.
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Journal Reference: Gul, J., Khan, M.N.A., Sikander, U. et al. Machine learning optimization for algal biochar yield: integrating experimental validation and sensitivity analysis. Biochar 8, 8 (2026).
https://doi.org/10.1007/s42773-025-00511-w
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About Biochar
Biochar (e-ISSN: 2524-7867) is the first journal dedicated exclusively to biochar research, spanning agronomy, environmental science, and materials science. It publishes original studies on biochar production, processing, and applications—such as bioenergy, environmental remediation, soil enhancement, climate mitigation, water treatment, and sustainability analysis. The journal serves as an innovative and professional platform for global researchers to share advances in this rapidly expanding field.
