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)

Long-term TNT and DNT contamination: 1-D modeling of natural attenuation in the vadose zone: case study, Portugal

The vadose zone of a trinitrotoluene (TNT) and dinitrotoluene (DNT) contaminated site was investigated to assess the mobility of those explosives under natural conditions. Located in the left margin of the River Tejo Basin, Portugal, the site is located on unconsolidated sediments. Wastewaters associated with the 50-year explosives production were disposed in excavated ponds, from where water would infiltrate and pollute the unsaturated and saturated parts of the local aquifers. Two boreholes were drilled to 9 m depth in such a former waste pond to investigate the contaminant's fate in the vadose zone. Sediment samples were taken every 1-2 m for analysis of the polynitroaromatics (p-NACs) and organic volatile compounds, pH, organic carbon content, cation exchange capacity and grain size analysis. The main contaminant was TNT representing >70 % of the total p-NACs concentration that peaked approximately 7 mg/kg in one borehole, even if the median in both boreholes was of similar to 1 mg/kg. DNT was 4-30 % of the total p-NACs and nitrotoluene (NT), up to 5 %. No other (volatile) organic compound was detected. The predominance of TNT as the main contaminant implies that any natural mass reduction has been inefficient to clean the site. Several 1-D model simulations of p-NACs cleaning of the vadose zone under natural conditions indicated that the most probable scenario of combined advection and partitioning will only remove TNT after 10's of years, whereas DNT and NT will hardly be removed. Such low concentrations and long times for the p-NACs removal, suggest that by now those compounds have been washed-out to a level below standard limits

Characterization of hydrochars produced by hydrothermal carbonization of rice husk

Biochar is the carbon-rich product obtained when biomass, such as wood, manure or leaves, is heated in a closed container with little or no available air. In more technical terms, biochar is produced by so-called thermal decomposition of organic material under limited supply of oxygen (O2), and at relatively low temperatures (< 700 °C). Hydrochar differentiates from biochar because it is produced in an aqueous environment, at lower temperatures and longer retention times. This work describes the production of hydrochar from rice husks using a simple, safe and environmentally friendly experimental set-up, previously used for degradation of various wastewaters. Hydrochars were obtained at 200 °C and 300 °C and at residence times ranging from 2 to 16 h. All samples were then characterized in terms of yield, surface area, pH, conductivity and elemental analysis, and two of them were selected for further testing with respect to heating values and heavy metal content. The surface area was low for all hydrochars, indicating that porous structure was not developed during treatment. The hydrochar obtained at 300 °C and 6 h residence times showed a predicted higher heating value of 17.8 MJ kg−1, a fixed carbon content of 46.5% and a fixed carbon recovery of 113%, indicating a promising behaviour as a fuel

Subcritical water treatment of landfill leachate: Application of response surface methodology

WOS: 000343614400002PubMed: 25151110Context: Leachate is the liquid formed when waste breaks down in the landfill and water filters through that waste. This liquid is highly toxic and can pollute the land, ground water and water ways. It is mandatory for landfills to protect against leachate in most countries worldwide. Controlling the pollutant loading, means reducing its quantity by containing or treating the waste to comply with certain discharge characteristics which are compatible with the receptor medium. Objective: This paper describes the reduction of the organic load of a mature landfill leachate using a novel experimental set-up that employs hydrogen peroxide under subcritical conditions and aims to establish this method as an effective alternative to currently used options. Response surface methodology was applied to optimize the treatment process and determine which of the following there parameters - temperature, residence time and hydrogen peroxide concentration - played the most important role. Method: The method employed is based on the use of laboratory-scale, stainless steel reactors, filled with the leachate and appropriate quantities of hydrogen peroxide. Under subcritical conditions (temperature in the range of 100-374 degrees C and enough pressure to maintain the liquid state of water), hydrogen peroxide produces hydroxyl radicals which are highly reactive and oxidize the organic molecules of the leachate. Results: The highest COD decrease of 85% was experimentally observed at 300 degrees C, 500 mM H2O2 and 180 min residence time. It was determined that the combination of oxidant concentration and temperature is the rate-determining factor, whereas residence time has a lesser effect on the process. Conclusions: A simple, quick, effective and environmentally-friendly method for the treatment of the organic load of landfill leachate was developed and optimized at laboratory scale. (C) 2014 Elsevier Ltd. All rights reserved

Synthesis of copper (I, II) oxides/hydrochar nanocomposites for the efficient sonocatalytic degradation of organic contaminants

Herein, novel Cu2O–CuO/HTC composites were prepared by hydrothermal precipitation employing as carrier sawdust hydrochar carbonized at 200 °C for 2, 6, and 12 h. The composites were used for the effective sonocatalytic degradation of three dyes (Acid Blue 92 (AB 92), Acid Red 14 (AR 14) and Acid Orange 7 (AO 7)) with different molecular structure. To gain insight into the functional groups, crystalline structure, elemental composition and optical characteristics of the Cu2O–CuO/HTC composites, FT-IR, XRD, EDX and UV–vis analyses were carried out. Also, the surface morphology and area of the Cu2O–CuO/HTC composites were investigated by SEM and BET analysis. The effect of different parameters, such as dye concentration, solution pH, and catalyst dosage on the sonodegradation process was examined. Among the as-prepared composites, the Cu2O–CuO/HTC-2 h sample exhibited the best performance, offering a degradation efficiency of 85.43% after 90 min. GC-MS analysis was in addition employed to determine potential intermediates. To assess the mineralization of dye solution under optimum conditions, COD analysis was performed implying 77.77% removal efficiency. Additionally, the reusability and stability of the as-prepared composites were verified. The leaching copper concentration in the aqueous phase was measured within four consecutive runs. © 2020 The Korean Society of Industrial and Engineering Chemistr

Recent advances in the application of nanomaterials for the remediation of arsenic-contaminated water and soil

This review specifically deals with the latest advances in the application of nanotechnologies and nanocomposites for remediation of arsenic (As)-contaminated water and soil. Remediation mechanisms generally include physicochemical adsorption and (photo)chemical redox reactions and filtration. Recently, various types of engineered organic/inorganic nanocomposites have been designed in membrane forms, embedded structures, or composites with extraordinary physical-chemical properties, and outstanding capacity for removal or immobilization of As in contaminated sites. In the present article, we give an overview of engineered nanomaterials developed recently (2017-2021) and their interaction mechanisms with As in contaminated water and soil. Emerging approaches include the development of bio-nanocomposites and nanomaterials that show both oxidative and adsorptive capacities. For the first time, we set out to perform a comprehensive assessment of the advantages of nanomaterials in As-contaminated soils with the focus on the mechanisms of decreasing bioavailability and leaching of As. Although great researches have been developed, serious study gaps and a new direction to future researches have been identified. © 2021 Elsevier Ltd

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&ndash;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&rsquo; 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