In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN₃ (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R² 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN₁, R² 0.897, root mean square error 1.289).

Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters (higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN₁ (based on C, H, O content) exhibited HHV predicting performance with R² = 0.974, another model, NN₂, was also able to predict HHV with R² = 0.936 using only C and H as input. Moreover, the inverse model of NN₃ (based on H, O content, and HHV) could predict C content with an R² of 0.939.

This study investigates the utilization of Japanese cedar bark (JCB) waste for the preparation of chemically-activated porous biochar materials using K₂CO₃ (xKC, where x represents the K₂CO₃ to JCB weight ratio). The research explores the versatile applications of these biochar materials, specifically focusing on CO₂ adsorption and as supercapacitors. A thorough analysis employing chemical composition, microstructure, gas adsorption isotherms is conducted to gain insights into the surface and structural properties of the materials. The CO₂ adsorption and electrochemical performance are assessed through isotherms at 298K and various electrochemical techniques. The study identifies functional groups and pore structures as critical factors influencing the adsorption capacity of xKC. Among the samples, 2KC demonstrated the optimal CO₂ adsorption (approximately 180 mg g^− 1), while 6KC exhibited superior electrochemical stability, with a maximum capacitance of around 210 F g^− 1 at 10 mA g^− 1. This comprehensive exploration provides valuable insights into the impact of material properties on both CO₂ adsorption and electrochemical behavior in K₂CO₃-activated biochar from JCB, suggesting promising interdisciplinary applications.