Biomass Samples Research Articles

Comparative Carbon Stock Potential of Indigenous Agroforestry Systems in Silte Wereda, Southern Ethiopia

Agroforestry system (AFS) is described as one of the promising mitigation options for climate change through its high carbon sequestration capacity. This study was conducted in Silite District; Southern Ethiopia to assess the unaccounted carbon stock potential of selected traditional agroforestry systems. The study assessed the carbon stock potential of AFS biomass and soil carbon pools. Biomass and soil samples were taken from temporary plots laid for this study, 20 × 20 m for home garden, 50 × 100 m for parkland, and 10 × 10 m for woodlot AFS. Height (H) and diameter at breast height (DBH) were taken from the sample plots to estimate biomass carbon. Litter, herb, and grass samples were collected from 1 m2 quadrant within the main plot. The findings show that the total biomass carbon ranged from (1.28-7 Mg ha -1) though there was no significant difference among the systems and higher biomass carbon was attributed by parkland AFS while the lowest was woodlot. A significantly higher amount of SOC was recorded in home garden AFS along the two depths (82.5 Mg ha -1) than the other two systems and the lowest was attributed to parkland (41.7 Mg ha -1). Therefore, this traditional AFS should be supported for their contributions in climate change mitigation schemes as they can sequester a reasonable amount of carbon

Comparative analysis of nutrient absorption in rice cultivation: Aerobic versus anaerobic conditions in furrow‐irrigated rice

AbstractRice (Oryza sativa L.) production in the Mid‐Southern United States has traditionally been under conventional flood (CF) production, namely, direct‐seeded and delayed‐flood production. However, furrow‐irrigated rice (FIR) has grown to comprise over 15% of Arkansas’ and 30% of Missouri's rice hectarage. The uptake of several nutrients, including phosphorus (P), potassium (K), and zinc (Zn), has been shown to differ between aerobic and flooded rice production. Hence, a nutrient uptake survey was conducted from 2018 to 2020 in FIR fields to determine the difference in nutrient uptake (macro‐ and micronutrients) between the upper generally aerobic environment at the top of the field and the bottom of the field, where a generally anaerobic or flooded environment existed from R1 to maturity. Aboveground biomass samples were taken at R3 from four nitrogen (N) treatments at the top and bottom of five sites on a clayey soil texture and four sites on a loamy soil texture. Results suggest that there is significantly lower P, K, sodium (Na), and manganese (Mn) uptake at the top of the field compared to the bottom of the field on both soil textures. Additionally, the N treatments that yielded the highest biomass generally led to the greatest uptake of all nutrients examined. The decrease in P and K uptake in the aerobic portion of an FIR field suggests that they may require altered fertilizer recommendations compared to the traditional CF rice system.

Comparative analysis of kinetic model-fitting methods and selection priority for horse manure pyrolysis

The diverse origins of biomass wastes provide a constraint on the practical application of pyrolysis, as it leads to uncertain chemical reactions, feedstock conversion, and energy consumption. Various model-fit methods are available to identify the decomposition kinetics, however, limited studies have compared the reliability and accuracy of those kinetic models. This work compared five different kinetic models, two based on model-fit methods (Coats–Redfern (CR), Kennedy-Clark (KC) and three based on the application forms of Criado Master Plots (CMP I, CMP II and CMP III). The mass loss data from the pyrolysis of horse manure (HM) through a thermogravimetric analyser was used as a biomass sample for evaluation. Results demonstrated that all five model-fit methods report different kinetics data. Although the model-fit methods (CR and KC) allow a direct determination of kinetic triplets, the accuracy of data is lower. The CMP-II and CMP-III methods (incorporating model-free data in the model-fit methods) showcase multi-step chemical kinetics and better describe the decomposition behavior of HM due to its multi-compositional properties. To obtain kinetic triplets with enhanced accuracy, it is recommended to incorporate model-free data into the model-fit method. Improved precision of the kinetics data is essential for developing experimental parameters and optimizing the process. Future research should include the selection criteria of model-fit methods shown in this study to improve the accuracy of kinetic data.

Benefit of the nutritional and mineral composition of sea lettuce from a traditional salina: Implications for human consumption

The proximal composition and its seasonal variation of the green seaweed Ulva sp. harvested in a traditional saline (earthen ponds used for marine salt extraction) from Cadiz Bay (Southern Spain) was evaluated. Ulva sp. was also collected in a reference location within the Bay in order to compare and evaluate the effects of the particular characteristics of the saline in the composition of the macroalgae. Moisture, protein, lipid, ash, carbohydrate, fiber and macro- (Na, K, Ca, Mg), micro-mineral contents (Fe, Zn, Cu) and heavy metals (As, Cd, Co, Cr, Hg, Ni, Pb, Sn) of harvested biomass samples as well as environmental parameters of seawater (temperature, salinity, pH, DO, NH4+, NO3−, NO2− and PO43−) were measured. The results showed that Ulva sp. from the earthen ponds in the traditional salina was a better source of proteins, lipids, K and Mg, highlighting in summer with values of 27.54 % versus 6.11 %; 6.71 % versus 3.26 %; 26.60 mg g−1 versus 14.21 mg g−1 and 23.13 mg g−1 versus 17.79 mg g−1, respectively. It also had Na/K and Ca/Mg ratios of less than one, suggesting a healthy food source. Considering the Commission Recommendation (EU) 2018/464 as a working reference, Ulva sp. did not exceed the limit of toxic metals for human consumption.A season and site-season significant interaction on the composition of the seaweeds was observed. The proximal and mineral composition of Ulva sp. was influenced by the special features and environmental conditions of the earthen ponds. Hence, significant differences were observed in the macroalgae collected in the earthen ponds in summer and autumn, in contrast to the winter and spring samples, whose characteristics were similar to those from the inner bay. The closure of the lock-gates in summer to favor the production of salt significantly modified the environmental characteristics of the saline, affecting the physiological capacity of Ulva sp. to assimilate and storage nutrients, and therefore its tissue composition. As a consequence, the highest contents of lipid, ash, Ca, K, Mg and Fe were estimated in the macroalgae.

Impact of industrial effluent on selected grass species

Industrial wastewater contains a variety of substances such as organic carbon, salts and heavy metals, which along with arsenic (As), cadmium (Cd), lead (Pb) and nickel (Ni) are known to be highly toxic even in very low concentrations. This study was carried out to test the impact of industrial effluent from the Industrial Estate Islamabad, the capital city of Pakistan, on four local grass species of the Pothohar region, i.e., Panicum maximum (Mombasa grass), Cenchrus ciliaris (Dhaman grass), Cynodon dactylon (Khabbal grass) and Chloris gayana (Rhodes grass). These grasses were grown in tap water (T0), industrial effluent (T1), and in industrial effluent-tap water ratio (50:50; T2). The concentrations of As, Cd, Pb, and Ni accumulated by these grasses were appraised with inductively coupled plasma optical emission spectrometry (ICP-OES). All four grasses differed significantly in terms of their accumulation of different types of metal elements in the shoot and root organs. The analysis of industrial effluent, and root and shoot biomass samples of all four grasses did not have high amounts of heavy metals with reference to the levels of the metals documented by WHO. It is generally recommended that the effluents from the Industrial Estate Islamabad can be used with some caution for growing forage grasses including the four species tested in this study.

Impact of cultivation conditions on physicochemical characteristics of Miscanthus × giganteus biomass

AbstractMiscanthus × giganteus has emerged as a successful energy crop for remediating marginal land, offering potential as an alternative energy source for climate change mitigation. This study aimed to assess the physicochemical properties for gasification of Miscanthus × giganteus biomass from plantations with diverse soil origins, characteristics, contamination levels, age, and climate conditions, aiming to determine their influence on the energy properties. Fuel parameters, i.e.: ash content, volatile matter, calorific value, ultimate analysis, and ash melting behavior were measured. Analysis of biomass samples revealed significant variations in the ash content and volatile matter with the average values of 4.5 and 78.8%, respectively, depending of the produced biomass. The calorific value remained consistently high, ranging from 18 to 19 MJ kg−1, even when biomass was derived from the contaminated soil. Additionally, biomass ash exhibited complete melting at temperatures exceeding 1300 °C, signifying its suitability for gasification without encountering technical obstacles. Employing PCA analysis, the significance of plantation age, location, and soil characteristics in shaping the physicochemical properties of Miscanthus × giganteus biomass for gasification was highlighted. The results illustrated that soil contamination by trace elements had a minimal impact on the physicochemical properties, whereas plantation age and climate conditions significantly influenced to the fuel properties of biomass.

A hybrid approach of anaerobic digestion model no. 1 and machine learning to model and optimize continuous anaerobic digestion processes

Anaerobic digestion is a promising approach to dispose of biodegradable waste and wastewater, generating biogas as an alternative energy resource. This work proposed a so-called M-CADM1 for continuous anaerobic digestion simulation, which combined the machine learning and anaerobic digestion model No.1 (ADM1). The detailed reaction path and intermediate products in different stages of anaerobic digestion are specified in ADM1. The kinetic parameters were modified by machine learning. The characteristics (elemental composition) of feedstocks were used to predict kinetic parameters. A total of 75 biomass samples were used to establish for machine learning models. Five element contents (C, H, O, N, S), feedstock feed rate, and anaerobic digestion temperature were used as the input. The kinetic parameters were set as output. The sensitivities of 17 kinetic parameters were evaluated. 7 kinetic parameters with the highest sensitivities were selected as ADM1 model inputs by sensitivity analysis. The R2 and RMSE were used as the index to evaluated the accuracy of machine learning model. The best R2 and RMSE reached 0.84 and 0.196. The TIC was used as the index to evaluated the accuracy of M-CADM1. By comparing the simulated value with the experimental value, the accuracy of the overall M-CADM1 expressed by TIC of kitchen waste was 0.036. The organic acid content and pH in the reactor were considered as indicators to study the accuracy and stability of the M-CADM1. Trends in organic acids, free ammonia or hydrogen inhibition, and pH were consistent with experimental continuous anaerobic digestion results.

Phytoremediation capacity of hybrid aspen at sites affected by industry and agriculture.

Fast-growing Populus spp. are well-acknowledged to restore contaminated soils from heavy metals in industrial areas. Thus far, there is no knowledge about the phytoremediation capacity of Populus spp. plantations in hemiboreal Estonia conditions to restore industrially polluted areas. The objective of this study was to assess the soil contamination rate of heavy metals (As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb and Zn) and their uptake by mature hybrid aspen (Populus tremula × Populus tremuloides Michx.) in plantations in different industrial pollution areas (e.g. cement factory, oil shale mining). For the reference, industrially polluted plantations were compared with the low pollution area hybrid aspen plantation on former agricultural soil, which was influenced by fertilization and liming before afforestation. Twenty-one years after afforestation, soil samples were collected from the 0-10cm topsoil layer. Aboveground biomass sampling was performed for bark and stem wood by ingrowth cores to separate wood formed during early (1-10years) and late (11-21years) stand development. Two decades after the afforestation of industrially polluted areas, the heavy metal concentrations in the soil were higher than the reference plantation and the standard reference for unpolluted soils in most cases. The highest concentrations of heavy metals in woody biomass were in the oil shale quarry spoil; because of poor growth, the accumulated pools in aboveground biomass were low. Cd differed from other metals and accumulated less in wood and more in bark. The concentration of heavy metals (Cd, Cr, Cu, Fe, Mn, Ni and Zn) was higher in the first decade of stand formation (1-10years) than in the last 10years (11-21years). High pools of heavy metals were accumulated in aboveground biomass in the reference plantation, indicating the considerable removal of heavy metal residues from the previous fertilization and liming source with harvest. Two decades of afforestation with hybrid aspen is too short for complete ecosystem restoration from heavy metals in industrially polluted areas.

DETERMINATION OF ENERGY CONTENT OF PLANT BIOMASS FOR DOMESTIC AND SMALL-SCALE INDUSTRIAL HEATING APPLICATIONS

Determination of the calorific values and elemental contents of plant biomass are important in considering their heat energy potential and environmental friendliness. It is also important in performance modelling calculations on thermal systems. This study measures the calorific values of twenty (20) biomass comprising herbaceous plants and agricultural waste with the aim of understanding their energy potential to be used as alternative fuels for small-scale industrial and domestic heating activities. The direct measurements of the calorific values were made using Bomb calorimeter (model 6100 series) and estimated from the ultimate analysis data of the samples. The relationships between the calorific value and the total carbon and hydrogen contents of samples were also investigated. The analysis results indicate that palm kernel shell and locust bean pod have the highest energy values of 41.1165 MJ/kg and 36.2230 MJ/kg respectively. Camel foot and soybean stalks give the lowest energy values of 6.0484 MJ/kg and 5.3353 MJ/kg respectively. The energy values of about 60% of biomass samples are in the range of 15-21 MJ/kg in agreement with the widely reported values in the literature. Further analysis indicates that the experimental measurements do not excellently agree with the values estimated using correlation equations and, in most cases, the experimental data is higher than that estimated using correlation equations. However, about 60% of the data points computed using the two equations agree closely. The study shows that the calorific values of all samples are strong function of their total carbon contents and have no...

Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue

This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (Di) and burnout index (Df), the comprehensive combustion performance (Si) and flammability index (Ci) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2P), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for Df, obtained through GA-PLSR using the first derivative (D1), and Si, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2P, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.

Green synthesized carbon quantum dots as chemiluminescence sensor for sulfanilamide detection

N–S co-doped quantum carbon dots (N–S-CQD) were synthesized using a hydrothermal method with Artemisia annua L. as a precursor and dl-cysteine as the nitrogen-sulfur source. N–S-CQD can enhance the chemiluminescence (CL) intensity of KMnO4 system, while sulfanilamide (SA) is capable of significantly inhibit the CL intensity of KMnO4-(N–S-CQD) system. Based on this finding, we developed a novel CL method for the determination of SA. Under the optimal conditions, SA could be accurately determined in the concentration range of 50–1600 μM with a limit of detection of 0.22 μM. The investigation of potential interferences and recovery rates confirmed the method's suitability for assessing biomass samples.

A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass

Hydrothermal carbonization (HTC) is a thermochemical conversion technology to produce hydrochar from wet biomass without drying, but it is time-consuming and expensive to experimentally determine the optimal HTC operational conditions of specific biomass to produce desired hydrochar. Therefore, a machine learning (ML) approach was used to predict and optimize hydrochar properties. Specifically, biochemical components (proteins, lipids, and carbohydrates) of biomass were predicted and analyzed first via elementary composition. Then, accurate single-biomass (no mixture) based ML multi-target models (average R2 = 0.93 and RMSE = 2.36) were built to predict and optimize the hydrochar properties (yield, elemental composition, elemental atomic ratio, and higher heating value). Biomass composition (elemental and biochemical), proximate analyses, and HTC conditions were inputs herein. Interpretation of the model results showed that ash, temperature, and the N and C content of biomass were the most critical factors affecting the hydrochar properties, and that the relative importance of biochemical composition (25%) for the hydrochar was higher than that of operating conditions (19%). Finally, an intelligent system was constructed based on a multi-target model, verified by applying it to predict the atomic ratios (N/C, O/C, and H/C). It could also be extended to optimize hydrochar production from the HTC of single-biomass samples with experimental validation and to predict hydrochar from the co-HTC of mixed biomass samples reported in the literature. This study advances the field by integrating predictive modeling, intelligent systems, and mechanistic insights, offering a holistic approach to the precise control and optimization of hydrochar production through HTC.Graphical

Microbial characterization of raw biomasses of Alaria esculenta, Chlorella vulgaris, Lemna minor

This study aimed to identify the types of microorganisms present in Alaria esculenta, Chlorella vulgaris, and Lemna minor as well as to characterize the microbial communities in their raw biomasses. The different raw biomass samples were assigned to a total of 35 and 32 classes, respectively, representing different bacterial and fungal phyla. The most common bacterial genera in all raw biomasses were Acinetobacter (11.68%) and Sphingobacterium (5.72%). Cyanobacteria was found to be the most abundant phylum (59.66%) in Chlorella vulgaris, followed by Lemna minor (type 2) (42.08%), Lemna minor (type 1) (35.97%), and Alaria esculenta (type 1) (26.07%). The most prevalent fungal genera in all raw biomasses were Mycosphaerella (31.64%), Cladosporium (5.08%) and Alternaria (3.63%), while Ascomycota was the most abundant phylum (89.04%) in Lemna minor (type 2), followed by Alaria esculenta (type 1) (80.0%), Lemna minor (type 1) (78.84%) and Chlorella vulgaris (67.14%). In addition, a taxonomic comparison was carried out to assess the variations in the microbiota across all samples. Chl. vulgaris and L. minor (type 2) exhibit a high prokaryotic and fungal composition according to the analyses in comparison to the others. Seven and six genera (>1%) dominated the bacterial and fungal communities, respectively, in all the samples according to the next generation sequencing (NGS) analysis. The development of effective measures to ensure the quality and safety of these aquaculture plants for consumption, as well as the improvement of their cultivation, processing, and storage will be greatly aided by these findings.

In vitro protein digestion and carbohydrate colon fermentation of microbial biomass samples from bacterial, filamentous fungus and yeast sources

This study evaluated the nutritional quality of different microbial biomass samples by assessing their protein digestibility and carbohydrate fermentability in the colon using in vitro methods. Four microbial samples were produced: one hydrogen-oxidizing bacterial strain (Nocardioides nitrophenolicus KGS-27), two strains of filamentous fungi (Rhizopus oligosporus and Paecilomyces variotii), and one yeast strain (Rhodotorula babjevae). The microorganisms were grown in bioreactors, harvested and dried before analysis. The commercial fungal product Quorn was used as a reference. The protein digestibility of the microbial samples was analysed using the INFOGEST in vitro model, followed by quantification of N-terminal amine groups. An in vitro faecal fermentation experiment was also performed to evaluate the degradation of carbohydrates in microbial biomass samples and formation of short-chain fatty acids (SCFA). The fungal biomass samples had higher protein hydrolysis (60–75 %) than the bacterial sample (12 %) and Quorn (45 %), while the yeast biomass had the highest protein digestibility (85 %). Heat-treatment of the biomass significantly reduced its protein digestibility. Total dietary fibre (DF) content of fungal biomass was 31 – 43 %(DW), mostly insoluble, whereas the bacterial biomass contained mainly soluble DF (total DF: 25.7 %, of which 23.5 % were soluble and 2.2 % insoluble). After 24 h of colonic in vitro fermentation, SCFA production from the biomass of Paecilomyces, Quorn and Rhodotorula was similar to that of wheat bran, while 17 % and 32 % less SCFA were produced from the biomass of Rhizopus and the bacterial strain, respectively. Further studies are needed to clarify the reasons for the observed differences in protein digestibility and DF fermentability, especially regarding the cell wall structures and role of post-processing.

Improving the Methodology for Determining the Biomass/Coal Co-Combustion Ratio: Predictive Modeling of the 14C Activity of Pure Biomass

Sampling and 14C detection of biomass are now essential steps to ensure the accuracy of the 14C method, but they require additional time and economic investment. When there are multiple types of biomass fuels, it is not possible to guarantee the uniformity of sampling. The 14C activity of biomass fuels exhibits variability, and this value significantly impacts the precision of the 14C method. Therefore, this study aims to investigate the influencing factors of 14C activity in biomass fuels. It also provides predicted values of 14C activity for different types of biomass fuels for each year from 2020 to 2030. Additionally, this study discusses the potential blending ratio measurement errors that may arise due to the uncertainties of the predicted values. The reduction in the 14C activity of biomass fuels can occur due to the utilization of fossil fuels, human activities, and the photosynthesis mode of C3 plants. This study presents a prediction method for determining the reduction factor. The other component of the prediction methodology involves determining the original 14C activity of biomass fuels. The 14C activity of the annual biomass is equal to the 14CO2 activity (the 14C activity of CO2) of the surrounding environment, and it experiences a decline of 0.355 pMC/year. The 14C activity has ranges of five types of perennial biomass fuels, including wood chips and branches, bark, leaves, wasted furniture, and abandoned building wood, for the time period between 2020 and 2030, are 97.34~102.84, 96.35~106.27, 96.35~102.64, 111.00~118.60, and 111.32~129.47 pMC, respectively. Based on these, this study introduces a new formula for calculating blending ratios, which enhances the current methodology. The calculation errors of blending ratios caused by the uncertainties of the predicted values are generally negligible, with the exception of wasted furniture and construction wood. The annual decrease in the blending ratio calculation error, caused by the uncertainty associated with the predicted value, can be observed. This study aims to reduce the implementation time and economic cost of the 14C method while ensuring the accuracy of biomass blending ratio detection.

Vacuum Pyrolysis of Brazilian coal, peat and biomass – Results on characterization of feedstock, solid residues, pyrolysis liquids and conversion rates

Pyrolysis of solid fuels such as coal, peat and biomass enables conversion into solid and liquid products, with noncondensable gas being a by-product. The present study evaluates conversion characteristics of Brazilian coal, peat and biomass samples using vacuum pyrolysis techniques. Feedstock and their respective solid residues (chars) and pyrolytic liquids obtained under vacuum pyrolysis conditions were characterized by proximate and ultimate analyses, gross calorific value, petrographic analyses, reactivity to CO2, Raman spectroscopy and organic geochemical methods (extraction, liquid chromatography). Chemical and physical properties in the feedstock samples and solid residues are highly variable. In the coals mean vitrinite reflectances ranged from 0.44% to 1.18% Rrandom indicating a rank range from subbituminous to high volatile/medium volatile bituminous coal. Reflectance measurements obtained from vitrinoid particles identified in solid residues from coal, peat and biomass varied from 2.10% to 10.64% Rrandom. Analyses of the liquid products indicate a tendency of the aliphatic fraction to increase in most of the samples during the pyrolysis process, as well as the predominant formation of polar compounds in the condensable liquids. The results of this study suggest that among the coal samples investigated major conversion to liquids and gases (29.7%–33.2%) occurs in the high volatile bituminous coals from Santa Catarina, whereas in the biomass samples Mamona (Castor Beans), wood chips (Eucalyptus), wooden bars (pinus) and signal grass have all conversion rates > 60%. The conversion rates for the peat samples varied between 32.5 and 46.6%. Reflectance values determined on vitrinoid biomass chars indicate a potential use in soil amendment.

Evaluation of Torrefaction on the Quality of Bio-Oil Obtained by Pyrolisis of Green Coconut Wastes

Pyrolysis of green coconut residues is considered a promising source of biomass for transforming energy and generating value-added products from thermochemical processes such as pyrolysis, mainly due to the high volume of this material in the Northeast region. For better pyrolytic results, some pre-treatments of the biomass can be used. In this work, the pre-treatment technique of oxidative and non-oxidative torrefaction was applied, using temperatures of 200 and 250 °C. These torrefied biomasses were subsequently subjected to fast pyrolysis at 700 °C. GC/qMS characterized the bio-oil obtained, and an increase in the content of phenolic compounds was observed in the torrefied biomass samples when compared to the raw biomass, indicating that the process is viable and adds value to the bio-oil obtained by pyrolysis.

NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

This study investigates the non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; ash content) in various chipped and ground biomass using a combination of destructive thermogravimetric analysis (TGA) and non-destructive near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). The thermogravimetric method determines proximate analysis data through TG and DTG curves, tracking biomass mass loss over time or temperature. NIRS scans chipped biomass in diffuse reflectance, and ground biomass in transflectance mode, covering the wavenumber range from 3595 to 12,489 cm−1. PLSR-based models (Full-PLSR, GA-PLSR, SPA-PLSR, MP PLSR 5-range method, and MP PLSR 3-range method) are developed and evaluated based on R2P, RMSEP, and RPD. MC and FC models for chip biomass exhibit satisfactory performance, making them cautiously applicable in various applications, including research. Optimal models for MC and FC in chip biomass, constructed using GA-PLSR with the second derivative and Full-PLSR with a constant offset, yield high R2P values (0.8654 and 0.8773), low RMSEP values (0.85% and 2.12%), and high RPD values (2.9 and 3.0), indicating applicative capabilities. Other parameters such as MC and FC in ground biomass, as well as VM and ash content in both chip and ground biomass, are found suitable for rough screening. Model sensitivity, assessed by calculating LOQ, indicates high sensitivity for VM in both chip and ground biomass and FC in chip biomass, as the calculated LOQ value is lower than the minimum reference values used during model development. However, for the remaining parameters, LOQ values surpass the established minimum reference value, suggesting limitations in predicting samples below the calibration range. Continuous model enhancement incorporating an ample number of representative biomass samples and consistent validation with unknown samples are imperative for ensuring accurate predictions.

Proximate and Physicochemical Analysis of Castor, Neem, and Waste Cooking Oils, and their Waste Seed-Cakes Post Oil Extraction as Potential Raw Materials for Biodiesel Production

In this study, proximate and physicochemical analysis of castor oil (CO), neem oil (NO), and waste cooking oil (WCO) is being investigated. The castor and neem seed oil were extracted using soxhlet extraction method with n-hexane as solvent with yields of 40.43% and 41.10% respectively. The results of the study indicated low moisture contents of 2.95% and 2.58% for castor and neem seed-cake/shell composite biomasses respectively. The castor seed-cake/shell composite biomass contained volatile matter (54.83%), ash content (7.85%), and fixed carbon (34.37%), while the neem seed-cake/shell composite biomass contained volatile matter (52.45%), ash content (12.74%), and fixed carbon (31.25%) respectively. The order of the proximate parameters in percentage for both castor and neem seed cake/shell composite biomasses is: Volatile Matter > Fixed Carbon > Ash Content > Moisture. Acid value is highest in the NO sample (14.95 mg KOH/g) than in the other two samples (CO (3.96 mg KOH/g) and WCO (3.93 mg KOH/g)). The NO sample had highest saponification value (198.17 mg KOH/g) and a low density (0.938 g/cm3) than the CO (181.48 mg KOH/g, 0.984 g/cm3) and WCO (184.29 mg KOH/g, 0.961 g/cm3) samples. The WCO (122.14 gI2/100g) sample had higher amount of unsaturated fatty acid compounds than NO (82.49 gI2/100g) and CO (80.39 gI2/100g) samples based on the iodine values obtained. The low moisture contents and significant amount of carbon contents in the two composite biomasses indicates that activated or carbonized carbon can be prepared using these composite biomass samples.

Characterization of cassia fistula seed and shell co-pyrolysis yield optimization by response surface methodology and compositional analysis

The primary objective of this study was to enhance the bio-oil yield from the seed, shell, and blend of Cassia fistula (CF) through the application of response surface methodology (RSM). The study focused on analysing the product composition, qualities and optimizing the process by altering the heating temperature, the heating rate, and CF seed to CF shell blend ratio. The physicochemical characteristics of biomass samples were evaluated through Fourier Transform Infrared, proximate analysis, elemental analysis, X-ray diffraction, higher heating value, fuel reactivity, thermogravimetric analysis, CHO index, etc. Analysis of variance established that the temperature during co-pyrolysis of the CF seed and shell was the most critical factor in determining the bio-oil yield. The optimal bio-oil yield, gas yield, and char yield were obtained at a heating temperature of 450 °C, a heating rate of 10 °C/min, and a blend ratio of 75%. A statistical model was constructed to examine the parameters identified through analysis of variance as having a substantial impact on the experimental results. This study provides insights into RSM approaches that lead to a more practical approach to process modelling. Gas chromatography-mass spectrometry revealed that the bio-oil was primarily composed of hydrocarbons, followed by ketones and phenolic compounds. The bio-oil analysed exhibited a carbon chain range of C2-C12.