The Bioforcetech automated and sustainable systems transforms biosolids into valuable products while reducing volume by 90%. We promotes the circular economy.

With the growing global water crisis, wastewater reuse is increasingly essential, particularly in applications where treated water is safe for reuse. This study presents a sustainable hydrogel adsorbe...

Carbon-based materials like biochar, charcoal, activated carbon, hydrochar, and carbon nanodots are essential in various applications, from soil health to pollution control. Understanding their dis…

Explore how biochar and hydrochar enhance plant growth in microgravity, crucial for future space exploration and sustainable food production.

Hydrochar amendments reduced cumulative N2​O emissions in boreal grassland, proving superior to biochar, which increased emissions 1.56-fold over control when fertilized. Furthermore, hydrochar-onl…

A recent study in Scientific Reports by Gayathri Rangasamy and colleagues demonstrates that hydrochar derived from industrial sludge can remove 99.63 percent of Red F2B dye from water. This sustain…

Applications of artificial intelligence (AI)- and machine learning (ML)-based methodologies for predicting optimal conditions in sustainable and effective management of biowastes and natural resources are of great concern. However, the AI-applications for optimizing the hydrothermal treatment (HT) of organic solid biowastes and prediction of nutrients fate during the HT process have not yet been investigated. Therefore, this study explores the application of different ML models (e.g., XGBoost, Decision Tree, and Random Forest) for optimizing HT of swine manure, focusing on the role of calcium (Ca) and iron (Fe) ions in phosphorus (P) distribution in the produced liquid and solid phases (hydrochar). Specifically, we investigated the fate of total P (TPS) in the hydrochar and inorganic P (IPL) in the liquid phase during HT. Experimental validation was conducted alongside the ML predictions, with XGBoost outperforming the other models, showing strong predictive accuracy for TPS (R2 = 0.77) and IPL (R2 = 1.0). Key factors influencing model accuracy included feedstock composition, reaction temperature, duration, solid–liquid ratio, and Ca and Fe concentrations. We found that the impact of time on TPS and IPL was minimal when the reaction time was less than 200 min, while pH showed a positive correlation with TPS and IPL. NMR and XRD analyses indicated that as the reaction severity increased, the organic P content in the hydrochar became more uniform. These findings highlight the potential of AI-based methodologies for optimizing HT processes, contributing to more sustainable and effective solutions for safe recycling, management, and development of bioresources. Graphical Abstract

The copper complexing of dissolved organic matter released from hydrochar (HDOM) affects the former's environmental behavior. In this study, how hydro…

Discover the future of biochar and hydrochar in agriculture, water treatment, and environmental solutions with Dr. Toufiq Reza on The Biochar Show.