A remarkable number of scientific papers are available in the literature about the process of hydrothermal carbonization (HTC), also called wet or hydrous pyrolysis, applied to biomass substrates. ...However, the biggest share of it focuses on the characterization of HTC products obtained from different feedstocks. Only a few works are available on the process thermodynamics, particularly about the determination of the enthalpy change and the related heat release or absorption during HTC, which is a key parameter to understand the nature of the process and to evaluate the heat requirements for the design of industrial scale HTC plants. The present review summarizes the research carried out with the aim of assessing the process enthalpy for HTC. Two main approaches have been identified and described, and the research works are sorted into two categories: enthalpy calculation based on the direct application of the Hess’s law and enthalpy evaluation based on differential heat measurement. The hypotheses and results obtained by the different authors are critically analysed and discussed, and directions for further research are proposed.
This paper presents the system analysis and the techno-economic assessment of selected solar hydrogen production paths based on thermochemical cycles. The analyzed solar technology is Concentrated ...Solar Power (CSP). Solar energy is used in order to run a two-step thermochemical cycle based on two different red-ox materials, namely nickel-ferrite and cerium dioxide (ceria). Firstly, a flexible mathematical model has been implemented to design and to operate the system. The tool is able to perform annual yield calculations based on hourly meteorological data. Secondly, a sensitivity analysis over key-design and operational techno-economic parameters has been carried out. The main outcomes are presented and critically discussed. The technical comparison of nickel-ferrite and ceria cycles showed that the integration of a large number of reactors can be optimized by considering a suitable time displacement among the activation of the single reactors working in parallel. In addition the comparison demonstrated that ceria achieves higher efficiency than nickel-ferrite (13.4% instead 6.4%), mainly because of the different kinetics. This difference leads to a lower LCOH for ceria (13.06 €/kg and 6.68 €/kg in the base case and in the best case scenario, respectively).
•The study aims at constructing wall-to-wall estimates of forest growing stock (GSV).•We combine NFI plot data, remotely sensed and auxiliary variables.•We applied the methodology in Mediterranean ...Forest.•We create a wall-to-wall GSV forest map in a large test area.•The GSV map was used to produce model-based estimates of GSV at small scale.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.
For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.
Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.
This review aims at summarizing the literature for the coupling of the biological process of anaerobic digestion (AD) with one of three thermal processes: gasification (Gs), pyrolysis (Py), and ...hydrothermal carbonization (HTC). These thermal processes are investigated as pre and/or post treatments for AD, aiming at reducing its main drawbacks. The different cases are considered separately, based on the thermal treatment that is coupled with the AD process: AD-Gs, AD-Py, and AD-HTC. For each group, up to three sub-cases are discussed: the thermal treatment of the digestate, the use of char from the thermal treatment as stabilizer and enhancer in the AD reactor, and the AD of the aqueous products of the thermal treatment (this last case is not available with Gs). When possible, the result of the different researches for each configurations are grouped for comparison. Finally, an overview of the most promising future research investigations is given.
•Biological and thermo-chemical processes towards the bio-refinery concept.•Anaerobic Digestion coupled with Gasification.•Anaerobic Digestion coupled with Pyrolysis.•Anaerobic Digestion coupled with Hydro-Thermal Carbonization.•Main challenges, points of interest and suggested future research focus.
Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm ...hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019–October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE < 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, gmean > 0.83).
Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ...ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes.
Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting ...both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.
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•Hydrothermal carbonization of cattle manure-based digestate to produce hydrochar.•Effect of process conditions on hydrochar combustion properties are investigated.•Thermogravimetry ...is used to study hydrochar combustion kinetics.•Peak deconvolution describes contributions of various hydrochar components.•Higher hydrothermal carbonization temperatures produce better hydrochar fuel.
Hydrothermal carbonization (HTC) can convert wet biomass into hydrochar (HC), a solid carbonaceous material exploitable as fuel. In this study, HTC was applied to anaerobic digestate from cow manure. HCs obtained at three HTC temperatures (180, 220, 250 °C) were characterized in detail and their combustion behavior was investigated by thermogravimetric analysis (TGA) coupled with peak deconvolution. Increasing HTC temperatures increased the fixed carbon content (17.9–20.7%), the ash content (27.2–32.5%) and the calorific value (14.3–18.2 MJ/kg), while decreased the hydrogen (5.01–4.54%) and oxygen content (24.09–12.35%) of HCs. DTG profiles peak deconvolution unveils the presence of five major components in the HCs. HCs combustion kinetics were studied applying the KAS method. Average apparent activation energy values of 100, 88, 67 kJ mol−1 were obtained for HC180, HC220, HC250, respectively. HTC at 250 °C produced the HC with the best fuel characteristics.
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•Species distribution modeling is a powerful tool to support forest management.•SDM can be used to forecast climate change impact on forests.•A wide range of algorithms are used and ...many modeling tools were developed.•Uncertainties connected to climate change and future projections must be included.
Species Distribution Modelling (SDM) techniques were originally developed in the mid-1980s. In this century they are gaining increasing attention in the literature and in practical use as a powerful tool to support forest management strategies especially under climate change. In this review paper we consider species occurrence datasets, climatic and soil predictor variables, modelling algorithms, evaluation methods and widely used software for SDM studies. We describe several important and freely available sources for species occurrence and interpolated climatic data. We outline the use of both presence-only and presence/absence modelling algorithms including distance-based algorithms, machine learning algorithms and regression-based models. We conclude that SDM techniques provide a valuable asset for forest managers. However, it is essential to consider uncertainties behind the use of future climate change scenarios.
•HTC fundamentals can be studied by high pressure differential scanning calorimetry.•The best results are obtained performing two runs on the same sample.•The validity of the assumption of ...equilibrium in the blank is assessed.•Thermal contribution of reactions in blank is negligible compared to HTC effect.•Secondary reactions change the system composition with negligible exothermicity.
High pressure differential scanning calorimetry (DSC) is suitable to study the thermodynamics and kinetics of hydrothermal carbonization (HTC). To isolate the thermal effect of the process from the heat required to increase the DSC system temperature, a recently proposed method uses an additional run on the already reacted sample as the blank. The method assumes that the blank system is in equilibrium during the whole run; it neglects the potential for secondary reactions in the blank run, which may affect the results. This work assesses the effect of such an assumption on the final enthalpy results by performing additional re-runs on the same specimen and adopting longer isothermal segments to push the system composition closer to equilibrium. The study of HTC of cellulose at 250°C demonstrates that the thermal contribution of reactions in the second run is negligible compared to the primary HTC effects in the first run and within the experimental error. A deeper investigation of the thermal effect during re-runs shows that the biggest effect of reactions in such runs is to change the system composition rather than a direct exothermicity. Further, the exothermic activity disappears after 12 hours, suggesting that the sample has reached thermal equilibrium.
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