HCl-mediated hydrothermal carbonization of sewage sludge to produce high-quality fuels: machine learning predictions, mechanisms and combustion characteristics

This study employed acid-catalyzed hydrothermal carbonization (HTC) to optimize the fuel properties of sewage sludge (SS) and applied machine learning methods to predict its fuel performance. An acid-mediated HTC mechanism was proposed based on model compounds, and the combustion performance of hydrochar derived from SS was systematically evaluated. The results indicate that the hydrochar prepared at 220 °C in 0.5 mol/L HCl exhibits optimal fuel properties, with H/C ratio, O/C ratio, and fuel ratio of 1.23, 0.15, and 0.55, respectively. The Random Forest regression model demonstrated high effectiveness in predicting carbon content, achieving a correlation coefficient of 0.96. Mechanistic analysis with model compounds showed that during HTC, proteins in SS were hydrolyzed into amino acids, then deaminated, decarboxylated, and polymerized into hydrochar. Proteins are broken down by HCl into amino acids and then further into organic acids and amines. The acid inhibited the Maillard reaction, accelerating carbon framework formation via organic acid decarboxylation and condensation, and significantly enhanced aromatic structures. The introduction of acid increased the intensity of the weight loss peaks of volatile matter and fixed carbon, raised the ignition temperature, and reduced the combustion temperature. This study provides a theoretical foundation for optimizing acid-mediated HTC technology.