Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

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)

Development of ZnFeCe Layered Double Hydroxide Incorporated Thin Film Nanocomposite Membrane with Enhanced Separation Performance and Antibacterial Properties

Developing thin-film nanocomposite (TFN) membranes by incorporating nanomaterials into the selective polyamide (PA) layer is an effective strategy to improve separation and antibacterial properties. In this study, TFN nanofiltration (NF) membranes were fabricated by interfacial polymerization of piperazine (PIP) and trimesoyl chloride (TMC) with the addition of Zinc-Iron-Cerium (ZnFeCe) layered double hydroxide (LDH). The improved surface hydrophilicity of TFN membranes was investigated by water contact angle analyses and pure water flux measurements. Successful production of the PA layer on the membrane surface was determined by Fourier-transform infrared (FTIR) analysis. Atomic Force Microscope (AFM) images showed that the addition of LDH into the membrane resulted in a smoother surface. The scanning electron microscope and energy-dispersive X-ray spectroscopy (SEM/EDS) mapping of TFN membrane proved the presence of Ce, Fe, and Zn elements, indicating the successful addition of LDH nanoparticles on the membrane surface. TFN 3 membrane was characterized with the highest flux resulting in 161% flux enhancement compared to the pristine thin film composite (TFC) membrane. All membranes showed great rejection performances (with a rejection higher than 95% and 88% for Na2SO4 and MgSO4, respectively) for divalent ions. Additionally, TFN membranes exhibited excellent antibacterial and self-cleaning properties compared to the pristine TFC membrane

Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

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)

Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge

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