Modeling for proximate analysis and heating value of torrefied biomass with vibration spectroscopy

Understanding the compositional changes of organic matter in torrefied olive mill pomace compost using infrared spectroscopy and chemometrics

Composting olive mill pomace (OMP), the major by-product of the olive oil industry, is an attractive waste management practice in the context of sustainable food production. Thermal treatment of compost at mild temperatures (torrefaction) can aid to improve its characteristics as a soil amendment. This study aims to understand the chemical changes occurring during torrefaction of olive mill pomace-based (OMP) compost, as well as to evaluate the treatment effects on compost at different stages of maturation. Here, treatments at different temperatures (175, 225, and 275 °C) and duration (from 1 to 5 h) have been employed to obtain a sort of torrefied samples. In general, the H/C and O/C atomic ratios of compost samples decreased with torrefaction temperatures, which suggests an incipient coalification of the organic matter. Furthermore, the results showed that a combination of FT-NIR and FT-MIR spectroscopy using a low-level data fusion strategy is very sensitive to the molecular changes occurring both in the composting process and during heating. Principal Component Analysis (PCA) of the merged spectra revealed that the changes at 175 °C are mainly the loss of water (O-H contributions at 3300 and 5169 cm^-1) together with the degradation of proteins (observed in the decrease of amide I and II characteristic bands). Furthermore, the samples heated at this temperature can still be differentiated by their initial maturation stage. On the other hand, thermochemical changes occurring at higher temperatures are more intense and make the samples more alike, independently of the composting time. When heating above 225 °C, the loss of O-H happens together with the decrease of aliphatic moieties, reflected in the bands 2920 and 2850 cm^-1 (FT-MIR) and 4258, 4323, 5665, and 5781 cm^-1 (FT-MIR). This can be attributed to the thermal degradation of cellulosic materials and, additionally, to the degradation of the residual oil in the case of poorly composted samples. Heated samples are characterized by the presence of carbonyl groups (1709 cm^-1) and humic-like complex and polymerized aromatic structures (1579 cm^-1). Since the characteristics of the torrefied compost at 275 °C are very similar regardless of the initial maturation stage, torrefaction may be a very interesting way to reduce the composting time of olive mill pomace to obtain a high-quality organic amendment for soil application.

Kinetic Parameters of Nut Shells Pyrolysis

The European Union created a European Green Deal Program (EGDP). This program aims at a sustainable economy through the transformation of the challenges related to climate and the environment. The main goal of EGDP is climate neutrality by 2050. The increase of alternative biomass residues utilization from various food processing industries and cooperation in the energy and waste management sector is required to meet these expectations. Nut shells are one of the lesser-known, yet promising, materials that can be used as an alternative fuel or a pre-treated product to further applications. However, from a thermal conversion point of view, it is important to know the energy properties and kinetic parameters of the considered biowaste. In this study, the energy and kinetic parameters of walnut, hazelnut, peanut, and pistachio shells were investigated. The results showed that raw nut shells are characterized by useful properties such as higher heating value (HHV) at 17.8–19.7 MJ∙kg−1 and moisture content of 4.32–9.56%. After the thermal treatment of nut shells (torrefaction, pyrolysis), the HHV significantly increased up to ca. 30 MJ∙kg−1. The thermogravimetric analysis (TGA) applying three different heating rates (β; 5, 10, and 20 °C∙min−1) was performed. The kinetic parameters were determined using the isothermal model-fitting method developed by Coats–Redfern. The activation energy (Ea) estimated for β = 5 °C∙min−1, was, e.g., 60.3 kJ∙mol−1∙K−1 for walnut, 59.3 kJ∙mol−1∙K−1 for hazelnut, 53.4 kJ∙mol−1∙K−1 for peanut, and 103.8 kJ∙mol−1∙K−1 for pistachio, respectively. Moreover, the increase in the Ea of nut shells was observed with increasing the β. In addition, significant differences in the kinetic parameters of the biomass residues from the same waste group were observed. Thus, characterization of specific nut shell residues is recommended for improved modeling of thermal processes and designing of bioreactors for thermal waste treatment.

Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density

Visible and near infrared (Vis-NIR) spectroscopy is a mature analytical tool for qualitative and quantitative analysis in various sectors. However, in the face of "curse of dimensionality" due to thousands of wavelengths for a Vis-NIR spectrum of a sample, the complexity of computation and memory will be increased. Additionally, variable optimization technique can be used to improve prediction accuracy through removing some irrelevant information or noise. Wood density is a critical parameter of wood quality because it determines other important traits. Accurate estimation of wood density is becoming increasingly important for forest management and end uses of wood. In this study, the performance of two-dimensional (2D) correlation spectroscopy between wavelengths of various spectral transformations, i.e., reflectance spectra (R), reciprocal (1/R), and logarithm spectra (log (1/R)), were analyzed before optimizing spectral variable. The spectra of optimal transformation were decomposed using biorthogonal wavelet family from 3rd to 8th decomposition level based on lifting wavelet transform (LWT). The optimal wavelet coefficients of LWT were selected based on the performance of calibration set using partial least squares (PLS). Two frequent variable selection methods including uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were also compared. The results showed that the dimensionality of spectral matrix was reduced from 2048 to 16 and the best density prediction results of Siberian elm (Ulmus pumila L.) were obtained (R_p²R = 0.899, RMSEP = 0.016) based on LWT.

Alternative Fuels from Forestry Biomass Residue: Torrefaction Process of Horse Chestnuts, Oak Acorns, and Spruce Cones

The global energy system needs new, environmentally friendly, alternative fuels. Biomass is a good source of energy with global potential. Forestry biomass (especially wood, bark, or trees fruit) can be used in the energy process. However, the direct use of raw biomass in the combustion process (heating or electricity generation) is not recommended due to its unstable and low energetic properties. Raw biomass is characterized by high moisture content, low heating value, and hydrophilic propensities. The initial thermal processing and valorization of biomass improves its properties. One of these processes is torrefaction. In this study, forestry biomass residues such as horse chestnuts, oak acorns, and spruce cones were investigated. The torrefaction process was carried out in temperatures ranging from 200 °C to 320 °C in a non-oxidative atmosphere. The raw and torrefied materials were subjected to a wide range of tests including proximate analysis, fixed carbon content, hydrophobicity, density, and energy yield. The analyses indicated that the torrefaction process improves the fuel properties of horse chestnuts, oak acorns, and spruce cones. The properties of torrefied biomass at 320 °C were very similar to hard coal. In the case of horse chestnuts, an increase in fixed carbon content from 18.1% to 44.7%, and a decrease in volatiles from 82.9% to 59.8% were determined. Additionally, torrefied materials were characterized by their hydrophobic properties. In terms of energy yield, the highest value was achieved for oak acorns torrefied at 280 °C and amounted to 1.25. Moreover, higher heating value for the investigated forestry fruit residues ranged from 24.5 MJ·kg−1 to almost 27.0 MJ·kg−1 (at a torrefaction temperature of 320 °C).

Elemental Composition and Flue Gas Emissions of Different Components from Five Semi-Arid Woody Species in Pyrolysed and Non-Pyrolysed Material

Biofuels are sustainable alternatives to fossil fuels. However, they must comply with energy efficiency requirements and contribute to environmental protection. This study was focused on elemental composition (carbon, hydrogen, nitrogen, sulphur and chlorine) of different plants’ components (stems, branches, twigs and leaves) from pyrolysed (charcoal) and non-pyrolysed samples of five semi-arid trees: Acacia berlandieri, A. wrightii, Ebenopsis ebano, Havardia pallens and Helietta parvifolia. Carbon fluctuated from 80.77% to 89.30% in charcoal and 44.99% to 49.70% in non-pyrolysed biomass, and hydrogen ranged from 2.38% to 2.69% in charcoal and 5.89% to 6.62% in non-pyrolysed biomass. Nitrogen accounted for 0.39%–0.65% (branches) and 0.32%–0.64% (stems) in charcoal, and the ranges for non-pyrolyzed material were 2.33–4.00% (leaves), 1.06%–1.76% (twigs), 0.21%–0.52% (branches) and 0.15%–0.28% (stems). Considerably higher concentrations of sulphur compared to chlorine were found, with increasing values for both elements from the base of trees to the leaves. Non-pyrolysed samples were characterized by 68.05 mg/kg–769.16 mg/kg (stems), 118.02 mg/kg–791.68 mg/kg (branches), 225.11 mg/kg–1742.25 mg/kg (twigs) and 374.73 mg/kg–6811.52 mg/kg (leaves) for sulphur, and 117.86 mg/kg–528.08 mg/kg (stems), 109.18 mg/kg–464.15 mg/kg (branches), 905.47 mg/kg–4205.19 mg/kg (twigs) and 2799.68 mg/kg–5072.76 mg/kg (leaves) for chlorine. In charcoal, the concentration ranges for sulphur were 47.54 mg/kg–376.95 mg/kg (branches) and 42.73–292.20 mg/kg (stems) and 139.34 mg/kg–419.68 mg/kg (branches) and 177.39 mg/kg–479.16 (stems) for chlorine. The study has shown that pyrolysis increased the amount of carbon and decreased the amount of hydrogen. Coincidentally, the amount of nitrogen, chlorine and sulphur could be decreased significantly by pyrolysis which means an improvement of the fuel considering the flue gas emissions. Besides the influence of the type of combustion plant and the influence of the source of biofuel, the treatment has a significant influence on the amount and composition of flue gases emitted in the combustion.

Fructose induced neurogenic hypertension mediated by overactivation of p38 MAPK to impair insulin signaling transduction caused central insulin resistance

Type 2 diabetes are at a high risk of complications related to hypertension, and reports have indicated that insulin levels may be associated with blood pressure (BP). Fructose intake has recently been reported to promote insulin resistance and superoxide formation. The aim of this study is to investigate whether fructose intake can enhance superoxide generation and impair insulin signaling in the NTS and subsequently elevate BP in rats with fructose-induced hypertension. Treatment with fructose for 4 weeks increased the BP, serum fasting insulin, glucose, homeostatic model assessment-insulin resistance, and triglyceride levels and reduced the serum direct high-density lipoprotein level in the fructose group. The Tempol treatment recovered the fructose-induced decrease in nitric oxide production in the NTS. Immunoblotting and immunofluorescence analyses further showed that fructose increased the p38- and fructose-induced phosphorylation of insulin receptor substrate 1 (IRS1^S307) and suppressed Akt^S473 and neuronal nitric oxide synthase phosphorylation. Similarly, fructose was able to impair insulin sensitivity and increase insulin levels in the NTS. Fructose intake also increased the production of superoxide in the NTS. The results of this study suggest that fructose might induce central insulin resistance and elevate BP by enhancing superoxide production and activating p38 phosphorylation in the NTS.

Preparation and Characterization of Epoxy Resin Cross-Linked with High Wood Pyrolysis Bio-Oil Substitution by Acetone Pretreatment

The use of cost effective solvents may be necessary to store wood pyrolysis bio-oil in order to stabilize and control its viscosity, but this part of the production system has not been explored. Conversely, any rise in viscosity during storage, that would occur without a solvent, will add variance to the production system and render it cost ineffective. The purpose of this study was to modify bio-oil with a common solvent and then react the bio-oil with an epoxy for bonding of wood without any loss in properties. The acetone pretreatment of the bio-oil/epoxy mixture was found to improve the cross-linking potential and substitution rate based on its mechanical, chemical, and thermal properties. Specifically, the bio-oil was blended with epoxy resin at weight ratios ranging from 2:1 to 1:5 and were then cured. A higher bio-oil substitution rate was found to lower the shear bond strength of the bio-oil/epoxy resins. However, when an acetone pretreatment was used, it was possible to replace the bio-oil by as much as 50% while satisfying usage requirements. Extraction of the bio-oil/epoxy mixture with four different solvents demonstrated an improvement in cross-linking after acetone pretreatment. ATR-FTIR analysis confirmed that the polymer achieved a higher cross-linked structure. DSC and TGA curves showed improved thermal stability with the addition of the acetone pretreatment. UV-Vis characterization showed that some functional groups of the bio-oil to epoxy system were unreacted. Finally, when the resin mixture was utilized to bond wood, the acetone pretreatment coupled with precise tuning of the bio-oil:epoxy ratio was an effective method to control cross-linking while ensuring acceptable bond strength.

Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e., R² > 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality.

The effect of ethanol on hydroxyl and carbonyl groups in biopolyol produced by hydrothermal liquefaction of loblolly pine: (31)P-NMR and (19)F-NMR analysis

The goal of this study was to investigate the role of ethanol and temperature on the hydroxyl and carbonyl groups in biopolyol produced from hydrothermal liquefaction of loblolly pine (Pinus spp.) carried out at 250, 300, 350 and 390°C for 30min. Water and water/ethanol mixture (1/1, wt/wt) were used as liquefying solvent in the HTL experiments. HTL in water and water/ethanol is donated as W-HTL and W/E-HTL, respectively. It was found that 300°C and water/ethanol solvent was the optimum liquefaction temperature and solvent, yielding up to 68.1wt.% bio-oil and 2.4wt.% solid residue. (31)P-NMR analysis showed that biopolyol produced by W-HTL was rich in phenolic OH while W/E-HTL produced more aliphatic OH rich biopolyols. Moreover, biopolyols with higher hydroxyl concentration were produced by W/E-HTL. Carbonyl groups were analyzed by (19)F-NMR, which showed that ethanol reduced the concentration of carbonyl groups.

Integration of decentralized torrefaction with centralized catalytic pyrolysis to produce green aromatics from coffee grounds

The aim of this work was to integrate decentralized torrefaction with centralized catalytic pyrolysis to convert coffee grounds into the green aromatic precursors of terephthalic acid, namely benzene, toluene, ethylbenzene, and xylenes (BTEX). An economic analysis of this bioproduct system was conducted to examine BTEX yields, biomass costs and their sensitivities. Model predictions were verified experimentally using pyrolysis GC/MS to quantify BTEX yields for raw and torrefied biomass. The production cost was minimized when the torrefier temperature and residence time were 239°C and 34min, respectively. This optimization study found conditions that justify torrefaction as a pretreatment for making BTEX, provided that starting feedstock costs are below $58 per tonne.

Effect of liquefaction temperature on hydroxyl groups of bio-oil from loblolly pine (Pinus taeda)

Loblolly pine was liquefied with ethylene glycol at 100, 150, 200 and 250 °C in order to analyze the effect of liquefaction temperature on hydroxyl groups of bio-oil, and to determine the source and variation of hydroxyl groups. The optimum temperature was found to be 150-200 °C. Hydroxyl number (OHN) of the bio-oil was ranged from 632 to 1430 mg KOH/g. GC-MS analysis showed that 70-90% of OHN was generated from unreacted EG. (31)P NMR analysis showed that the majority of hydroxyl groups were aliphatic, and none of the bio-oil exhibited any detectable hydroxyl groups from phenolic sources. Finally, it was found that all bio-oils were stable in terms of OHN for 2 months when stored at -10 °C.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass.

Near infrared spectroscopy calibration for wood chemistry: which chemometric technique is best for prediction and interpretation?

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

Rapid determination of sugar content in corn stover hydrolysates using near infrared spectroscopy

Near infrared (NIR) spectroscopy provides rapid determination method for biomass characterization. The objective of this study is to use NIR to model and predict contents of monosaccharides in biomass hydrolysates. A uniform distribution of pretreatment conditions was used to generate representative samples that cover wide ranges of sugar concentration for model development. The formation of glucose, xylose, and arabinose was studied via response surface methodology and 3D models were provided to show the effects of pretreatment conditions. The NIR models developed with partial least squares are able to provide excellent and good prediction for glucose and xylose concentration in biomass hydrolysates, respectively. Data transformation did not increase model performance, but the reduced wavelength range improved model prediction for all the sugar contents. The NIR method significantly reduced the time and cost of sugar determination.