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

Abstract

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).

Knowledge Map of Spatial Planning and Sustainable Development: A Visual Analysis Using CiteSpace

“…Knowledge maps help us clearly review previous studies and forecast future development directions. The tool which helps make knowledge maps, such as CiteSpace, is used by many researchers around the world at present in various fields such as agriculture [148], food science [149,150], technology [151][152][153][154][155], planning [156][157][158][159], tourism [160], energies [161,162], medicine [163,164], health [165,166], ecology [167][168][169][170][171], and so on [172][173][174]. As an important tool for sustainable development, spatial planning has undoubtedly attracted the attention of many countries.…”

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

“…A total of 100-120 full sets of the required data were collected for the ANN analysis, while 70-110 of them were used for the ANN modeling. All simulations were performed in the MATLAB environment, using the deep learning toolbox [4,[16][17][18][19]23,[29][30][31].…”

“…Table 2 outlines the results from the statistical measures calculated herein examining the generalization and prediction capability of the proposed ANNs as well as the evaluation of the models' performance. For the sake of completeness, we use and evaluate the most common estimation errors, usually found in the literature [4,[17][18][19]29,30,34], which are the root mean squared error (RMSE), the mean absolute error (MAE), the relative error (RE), and the regression coefficient (R 2 ), defined by the following equations:…”

“…However, the high ash content and potential NO x emissions during combustion have been identified as the main issues to be dealt with. The applicability of sewage sludge hydrochar as biofuel and associated drawbacks were reviewed in our earlier work [4]. Recent research focuses on the parallel exploitation of sewage sludge, to obtain the solid hydrochar and at the same time recover P as a bio-fertilizer component.…”

A primer on predictive techniques for food and bioresources transformation processes

“…Likewise, the prediction of organic solid waste treatment outcomes has been tackled using artificial neural networks, support vector machines, and ensembles of decision trees (Guo et al, 2021). Artificial neural networks have also been applied to predict higher heating values in fuels obtained from renewable biomasses (Vardiambasis et al, 2020). In other cases, machine learning models can replace costly experiments by predicting features of hypothetical products: Szczypiński et al (2021), for example, reviews machine learning approaches to screening synthesizable organic materials.…”