Microwaves help turn sugar industry waste into high-performance biochar
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Optimization of microwave-assisted pyrolysis parameters for sugarcane bagasse biochar using response surface methodology
view moreCredit: Weitao Cao, Haoyang Jing, Demoz Teklil Araya & Wenke Zhao
Agricultural waste from the global sugar industry could become a powerful tool for clean energy, pollution control, and sustainable materials, thanks to new research showing how microwave technology can dramatically improve biochar production.
In a study published in Sustainable Materials and Chemicals, researchers report that microwave-assisted pyrolysis can be precisely optimized to convert sugarcane bagasse, the fibrous residue left after sugar extraction, into highly porous biochar with exceptional surface properties. By fine-tuning key processing conditions, the team achieved biochar with a surface area exceeding 1,150 square meters per gram, making it well suited for applications such as pollutant adsorption, wastewater treatment, and energy storage.
Sugarcane is one of the world’s most widely grown crops, with more than two billion tons produced each year. Processing this crop generates hundreds of millions of tons of bagasse annually, much of which is burned or discarded, creating environmental burdens and wasting valuable resources.
“Sugarcane bagasse is often treated as a low-value by-product, but it actually has enormous potential as a sustainable carbon material,” said corresponding author Wenke Zhao. “Our work shows that with the right microwave-assisted process, this waste can be transformed into high-performance biochar with carefully controlled pore structure.”
Unlike conventional pyrolysis, which heats biomass from the outside inward, microwave-assisted pyrolysis delivers energy directly into the material. This results in faster, more uniform heating and greater control over the chemical reactions that shape the final product.
To identify the best conditions for producing high-quality biochar, the researchers systematically studied the effects of three key variables: pyrolysis temperature, the amount of potassium hydroxide used as an activating agent, and the flow rate of carbon dioxide gas during processing. They then applied response surface methodology, a statistical optimization approach, to model how these factors interact and to predict optimal operating conditions.
The analysis revealed that potassium hydroxide addition had the strongest influence on biochar properties, followed by carbon dioxide flow rate, while temperature played a smaller but still important role. Under optimized conditions, the team produced biochar with an exceptionally high specific surface area and a finely tuned balance of micro- and mesopores.
“These pores are critical,” Zhao explained. “They determine how well biochar can trap pollutants, store charge in energy devices, or interact with chemicals in environmental applications.”
The researchers also showed that their predictive models closely matched experimental results, confirming that the optimization strategy can reliably guide biochar production without extensive trial-and-error experimentation.
Beyond sugarcane bagasse, the findings offer broader insights for converting many types of agricultural and biomass waste into valuable carbon materials. Microwave-assisted pyrolysis, combined with advanced statistical modeling, could help scale up sustainable biochar production while reducing energy use and processing costs.
“This study provides a practical roadmap for designing efficient, high-value biochar systems,” Zhao said. “By turning agricultural waste into functional materials, we can reduce environmental pressure while creating new opportunities in clean energy and environmental protection.”
The research highlights how innovative processing technologies can support circular economy goals by transforming waste streams into advanced materials with real-world impact.
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Journal reference: Cao W, Jing H, Araya DT, Zhao WK. 2026. Optimization of microwave-assisted pyrolysis parameters for sugarcane bagasse biochar using response surface methodology. Sustainable Carbon Materials 2: e003 doi: 10.48130/scm-0025-0014
https://www.maxapress.com/article/doi/10.48130/scm-0025-0014
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About Sustainable Carbon Materials:
Sustainable Carbon Materials (e-ISSN 3070-3557) is a multidisciplinary platform for communicating advances in fundamental and applied research on carbon-based materials. It is dedicated to serving as an innovative, efficient and professional platform for researchers in the field of carbon materials around the world to deliver findings from this rapidly expanding field of science. It is a peer-reviewed, open-access journal that publishes review, original research, invited review, rapid report, perspective, commentary and correspondence papers.
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Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Optimization of microwave-assisted pyrolysis parameters for sugarcane bagasse biochar using response surface methodology
Article Publication Date
30-Jan-2026
Machine learning reveals how to maximize biochar yield from algae
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Machine learning optimization for algal biochar yield: integrating experimental validation and sensitivity analysis
view moreCredit: Jawad Gul, Muhammad Nouman Aslam Khan, Umair Sikander, Asif Hussain Khoja, Melanie Kah & Salman Raza Naqvi
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.
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Journal
Biochar
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Machine learning optimization for algal biochar yield: integrating experimental validation and sensitivity analysis
Article Publication Date
27-Jan-2026
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