Biomass Moisture Content Research Articles

QUALITY OF CHIPS PRODUCED FOR ENERGY PURPOSES IN SOUTHWESTERN PARANÁ

Quality of wood chips produced for energy purposes in the southeast region of Paraná. To characterize and monitor the moisture content of the energy biomass used by a food industry in southwestern Paraná-Brazil, the materials of five different providers were analyzed, four were from Eucalyptus spp. and one from Pinus sp. The chips were characterized according to the moisture content on a wet basis, apparent density, calorific value and immediate chemical analysis was performed. With the collected data, calibration curves were generated to adjust the moisture content measurement equipment, MUG M75, used by the company. The calibration of the M75 was made based on the linear regression model, and all chips presented an adequate calibration curve with a coefficient of determination (R²) above 0.9. The chips had a moisture content of 35 to 45% in the humid base. The highest value of apparent density was 207.67 kg.m³. The superior calorific value was statistically equal for all materials, with an average of 4,384.4 kcal/kg. Regarding the immediate chemical analysis, volatiles were between 80 and 84%, for fixed carbon the highest value obtained was 19.16%. The studied chips had low ash contents, with a general average of 0.59%. All calibration curves showed high accuracy (> 90%), showing that the method used can replace oven drying after the recipe is calibrated for suppliers.

Greenhouse gas emission from prescribed fires is influenced by vegetation types in West African Savannas

Greenhouse gas (GHG) emissions from prescribed fires are poorly investigated, resulting in a high uncertainty in GHG budgets. Using, a carbon mass balance approach and experimental prescribed fires in 80 plots, this study assessed carbon emissions and established emission factors (EFs) for carbon dioxides (CO2), carbon monoxide (CO), and methane (CH4) across climate zones and vegetation types. In grass and shrub savannas, fires could burn intensely due to the lower moisture content and continuous spatial distribution of biomass fuel, causing greater carbon emissions with 1.61 ± 0.13 t C ha−1 and 1.01 ± 0.13 t C ha−1, respectively. Despite their low carbon emissions, tree savannas (1658.17 ± 11.13 g kg−1) and woodlands (1629.94 ± 12.23 g kg−1) have the highest EFs, which can be attribute to the high carbon content of biomass fuel in these vegetation types. Vegetation types and their interaction with climate zones have a substantial impact on carbon emissions and carbon species EFs, and should therefore be considered in assessing GHG emissions from fires. The findings from this study provide a useful basis for improving the national measurement, reporting, and verification of GHG emissions and for improving the measurement of the global balance of GHG emissions from fires.

The Determination of Woody Biomass Resources and Their Energy Potential from Hazelnut Tree Cultivation

The aim of this study was to estimate the shoot weight of four selected hazelnut cultivars and to see if the morphological characteristics of the cultivar and the age of the shoots affect their quality when used as fuel. This study shows that the cultivar ‘Olga’ generated the highest amounts of woody biomass (6507 t·ha−1), while ‘Olbrzymi z Halle’ generated the lowest (3843 t·ha−1). ‘Olbrzymi z Halle’ had the highest calorific values (HHVs) (18.08 MJ·t·ha−1 for annual shoots and 18.03 MJ·kg−1 for perennial shoots) and ‘Olga’ had the lowest calorific values (16.64 MJ·kg−1 for annual shoots and 16.39 MJ·kg−1 for perennial shoots). The age of the shoots had a minimal effect on the chemical and energy parameters. Emissions were the highest for ‘Olbrzymi z Halle’ (CO: 57.74 MJ·kg−1 for perennial shoots, CO2: 1414.05 MJ·kg−1) and lowest for ‘Olga’ (CO: 50.57 MJ·kg−1, CO2: 1238.46 MJ·kg−1). The cultivar ‘Olbrzymi z Halle’, which generated the least amount of biomass compared to the other cultivars, stands out for its high energy value due to its low moisture and ash contents and its high carbon and hydrogen contents, making it attractive for the purposes of biofuel production and supporting sustainable agriculture. The practical implications of the research findings include the selection of suitable varieties for biofuel production, the management of biomass moisture content, and the optimisation of combustion techniques to reduce emissions. The potential for using hazelnut shoots as a biofuel highlights the importance of sustainable agriculture and renewable energy production. The results provide valuable information that can support decisions regarding the cultivation and use of hazelnut shoots for biofuel production while minimising negative environmental impacts.

Steady and transient state behavior of a gasification process under fixed-bed downdraft configuration

Gasification is a thermochemical process that has gained significant interest in the field of biomass energy conversion. Despite the level of technological maturity of the process, the dynamic variation of the process as a result of changes in both the properties of the gasifying agent and biomass has not been analysed in sufficient depth. Therefore, the present study characterizes the process dynamically as a function of step-type changes in rice husk biomass moisture content and gasifying airflow. To identify stability conditions and the range for inducing disturbances, steady-state tests were carried out using a 32-factorial design. The experimental results demonstrate that within the tested range of airflow, the gasification process operates in the oxygen-limited zone. Despite increasing the airflow from 20 to 40 standard liters per minute (SLPM) and driving the reaction towards the combustion zone, the high temperatures achieved resulted in the gas reaching a peak Lower Heating Value (LHV) of 2.6 MJ/Nm3 and a gas power of 2.6 kW, with a Cold Gas Efficiency (CGE) of 62%. In contrast, the effect of biomass moisture content was negligible due to the thermal inertia of the reactor and the natural variation of the process. Dynamic evaluation revealed that the oxidation temperature and gas concentration were the variables that took the longest to return to stability after air disturbances. It took approximately 1200 s for the hydrogen (H2) concentration to stabilize, while the gas power required about 300 s. No clear results were observed regarding the impact of the dynamic disturbance in moisture content, which varied between 12.3% w.t and 21.5% w.t.

Modeling of a heat-integrated biomass downdraft gasifier: Influence of feed moisture and air flow

A model for a heat-integrated biomass downdraft gasifier is developed and used to study the influence of changes in biomass moisture content and gasifier air flow. This one-dimensional steady-state model accounts for pyrolysis, combustion and gasification reaction kinetics as well as transport phenomena occurring within the gasifier and heat integration system. The gasifier is divided into four zones for solving the ordinary differential equations (ODEs), because each zone has different geometry for the reactor or heating system. The material and energy balance ODEs are solved as a boundary value problem (BVP), ensuring that conditions for the producer gas at the bottom of the reactor match the conditions of the countercurrent annulus gas, which is used for heating. The model also accounts for the preheating of the biomass using exhaust gas from an associated engine used to generate electricity from the producer gas. The model predicts the process gas temperature, flow rate and composition and was validated using two experimental runs with different control inputs. The model predictions show good agreement with the data. Simulations with the highest feed moisture result in lower reactor temperatures and simulations with the highest air flow result in the highest reactor temperatures.

Relationship Between Fine Root Biomass and Soil Physico-chemical Properties of Grassland Ecosystem in Bhadrapur Municipality of Jhapa district, Eastern Nepal

This study investigated the distribution of fine root biomass and various soil properties in a grassland ecosystem within Bhadrapur Municipality, Nepal. Soil samples were systematically collected from randomly selected blocks in undisturbed, disturbed, and roadside areas during the late rainy season. Analyses included soil texture, moisture content, temperature, pH, and quantification of fine root biomass (FRB; <5 mm diameter) within the top 15 cm soil depth. Results showed the mean FRB was significantly higher in undisturbed areas (7.77 t/ha) compared to disturbed (7.053 t/ha) and roadside (1.76 t/ha) areas. Across all sites, FRB exhibited a positive correlation with soil moisture content. Soil pH was slightly more acidic in undisturbed versus disturbed and roadside areas. Soil temperature demonstrated a positive correlation with FRB in undisturbed and roadside areas but a negative correlation in disturbed areas. Additionally, FRB negatively correlated with sand content in disturbed and roadside areas. The findings highlight how soil moisture is a key driver of fine root proliferation in this grassland ecosystem. Variations in soil texture, pH, and temperature between sites reveal their influence on root growth dynamics. Understanding these root-soil interactions has implications for sustainable grassland management, biodiversity conservation, and ecosystem resilience under changing environmental conditions.

Self-heating and spontaneous ignition of biomass storage piles: Towards a reliable prediction tool

Large-scale biomass storage for modern bioenergy introduces potential safety concerns due to the intrinsic self-heating of biomass. Despite this, very limited research has been conducted in this area. This project fills a critical gap by developing a comprehensive modelling framework for self-heating in biomass piles and conducting a series of experimental studies to explore the intricate sub-processes within these piles. This paper presents only a small portion of the modelling and testing work. It successfully demonstrates the model's usefulness in predicting self-heating in coal piles, guiding safety measures for coal storage. Among various storage parameters, the pile height, particle size and ambient wind velocity have been identified as having substantial impacts on self-heating and self-ignition within the coal pile. This paper also illustrates the pronounced effects of initial biomass moisture content on microbial reactivity and oxygen consumption rate. An increase in initial moisture content significantly enhances the overall microbial reactivity and oxygen consumption rate. Wheat straw, compared to rice straw, is more prone to self-heating under identical storage conditions, as evidenced by higher heat generation, faster oxygen consumption, and a shorter time to peak temperature. Moreover, microbial activity is found to play a critical role in biomass self-heating, particularly in the temperature range of 0–75 °C, during the initial stages of heat accumulation. For the modelling discussed here, the flow within the porous pile must be considered laminar, despite the highly turbulent flow around the pile. This is based on the Reynolds number, calculated from the in-pile volume average of the velocity and the mean diameter of the fuel particles, which is significantly below the critical threshold (Recr=200). The insights and sub-models to be derived from the relevant sub-processes in biomass pile can be integrated into the model framework. This integration will create a more comprehensive and robust model for predicting self-heating and self-ignition in biomass storage piles, enhancing both theoretical and practical management of these phenomena.

Optimizing Hydrogen-Rich Biofuel Production: Syngas Generation from Wood Chips and Corn Cobs

This study investigates gasification using wood chips (WC) and corn cobs (CC) for hydrogen-rich syngas production. A simulation model developed in Aspen Plus was used to evaluate the performance of biomass gasification. The model incorporates a system of Fortran subroutines that automate the definition of input parameters based on the analysis of biomass composition. Furthermore, the model’s equilibrium constants were adjusted based on experimentally measured gas concentrations, increasing the precision of the variations. The numerical results predicted hydrogen yields of 65–120 g/kg biomass, with 60–70% energy efficiency for steam gasification (versus 40–50% for air gasification). The hydrogen concentration ranged from 34% to 40%, with CO (27–11%), CO2 (9–20%), and CH4 (<4%). The gasification temperature increased hydrogen production by up to 40% but also increased CO2 emissions by up to 20%. Higher biomass moisture content promoted hydrogen production by up to 15% but reduced energy efficiency by up to 10% if excessive. Steam gasification with wood chips and corn cobs shows promising potential for hydrogen-rich syngas production, offering benefits such as reduced emissions (up to 30% less CO) and sustainability by utilizing agricultural residues.

Superstructure optimization of microalgae pretreatment for direct combustion or gasification

Pretreatment of microalgae is a vital step preceding thermochemical valorization, necessitating an adjustable approach for its design. This study focuses on the development of a methodology for optimizing this stage by the arrangement of key unit operations, such as washing, drying, and torrefaction, with the primary goal of enhancing energy efficiency. Employing a precise methodology, we utilized simplified mathematical models that incorporated mass and energy yields for a range of technologies. Central to our approach was the application of Mixed Integer Non- Linear Programming (MINLP) to determine the optimal organization of these unit operations within a comprehensive superstructure, accounting for potential interconnections. Methodological rigor was demonstrated through a comparative analysis, contrasting our MINLP-based results with a systematic examination encompassing all possible unit operation combinations. We explored three distinct study cases, investigating the effects on optimal configurations of variations in the initial moisture content of raw biomass and introducing additional constraints on the final ash content and Lower Heating Value (LHV) of the pretreated biomass. Remarkably, results from both approaches exhibited consistent agreement across all cases, validating the optimization methodology. This validation underscores the potential for future studies in microalgae thermochemical valorization, with an emphasis on integrating an optimized pretreatment stage. In summary, our study not only sheds light on the intricacies of microalgae pretreatment but also highlights the versatility of an optimization-based approach in shaping a more energy-efficient biomass conversion process.

Extraction of neutral lipids and phospholipids from marine biomasses using subcritical and supercritical fluids

Supercritical carbon dioxide (scCO2) and subcritical dimethylether (DME) were used to extract lipids from partially to fully dry marine biomass - whole jack mackerel (WJM), Hoki frames and heads (HFH), and Greenshell mussel meat (GSM). scCO2 gave a lower extraction yield of total lipids from GSM (42%) compared to WFH and HFH (86 - 88% yield) due to the higher concentration of phospholipids present in GSM. DME gave a higher extraction yield of total lipids and phospholipids than scCO2 for all marine biomass types. Phospholipid yield from GSM using DME was dependent on moisture content of the feed marine biomass and decreased with decreasing moisture content. Our results suggested that phospholipid extraction yield can be optimised by partly drying biomass to 30 - 50% moisture prior to extraction. DME extracts contained good levels of omega-3 fatty acids with minimal hydrolytic phospholipid degradation, thus providing a potential processing technology option to recover them from marine biomass.

Energy production and its characteristics from four tropical trees species planted in short rotation woody systems in Costa Rica

Short-rotation crop (SRC) systems with woody species have been planted in Costa Rica. However, information about different tree species and spacing is limited. The objective of the present study was to examine biomass production and the physical, energy, and chemical properties of feedstock of four tropical tree species (Cordia alliodora, Dipteryx panamensis, Gmelina arborea, and Tectona grandis) in 34-month-old plants planted at four spacings (0.5×1.0 m, 1.0×1.0 m, 1.0×2.0 m, and 2.0×2.0 m). The highest mortality rate (50%) was found in G. arborea; however, diameter, height, basal area, and biomass production in G. arborea plantations were higher than T. grandis, C. alliodora, and D. panamensis. Spacing effects on diameter, height, basal area, and biomass production were observed in 10-month-olds. Wide spacing presented the highest values in diameter and height, but the highest biomass production was found in the narrow spacing. Also, biomass distribution was different in D. panamensis in relation to other species. Specify gravity, density, and moisture content of biomass showed high variation between species and spacing, but the energy and chemical properties of biomass showed few differences. These results suggest that these four species were uniform in terms of their energy and chemical properties, regardless of the spacing used. Finally, three species (G. arborea, C. alliodora, and T. grandis) presented important potential for use in SRC systems. G. arborea was the species with the highest production but a high mortality rate.

Technoeconomic assessment and optimization of algal oil extraction from marine macroalgae Dictyota bartayresiana biomass

The present research focused on the extraction of algal oil from marine macroalgae Dictyota bartayresiana biomass, with the aim of optimizing the process using classical and statistical methods. The Soxhlet apparatus was used for the oil extraction and a design of experiments was developed to achieve the highest algal oil yield. Response surface methodology was used to determine the optimal conditions for the extraction process, which included a temperature of 80 °C, an extraction time of 120.40 min, solvent to biomass ratio of 4.74:1 (v:w), and a biomass moisture content of 6.36 %. The regression coefficient was determined to be of 0.97. The extraction kinetics was studied followed by the estimation of activation energy. The extracted algal oil was characterized using FT-IR and GC–MS, and a simulation of the extraction process was conducted for industrial scale production. A technoeconomic assessment was also performed, which revealed positive results with a 14.32 % return on investment, a payback period of 6.98 years, internal rate of return of 15.20 %, and net present value of 9,584,000 $. Overall, the study demonstrates the potential for sustainable extraction of algal oil from D. bartayresiana biomass for use in the production of biofuel.

Online measurement of moisture content of biomass based on LiDAR images

Fast real-time moisture content (MC) measurement of biomass feedstock is important for stable and efficient combustion control in biomass furnaces. In this study, a novel MC measurement method using LiDAR images was proposed based on the calibrated relationship between the signal echo intensity and sample (sawdust and straw) moisture. The experimental results showed that when the MC increased from 0% to 50%, the echo intensity decreased from 5% to 11%, and the relationship between the MC and echo intensity was well fitted by a linear or quadratic function. The influences of the measurement distance, composition, and temperature of the biomass on the measurement accuracy were explored. The absolute measurement error was <4%, and the maximum absolute error was 2.88% for pure sawdust. For the mixed sample (75% straw + 25% sawdust), the maximum absolute error was 3.60%. The optimal measurement distance between the sample and LiDAR was 64 cm, and the maximum absolute error was 3.27%. The influence of temperature between 20 °C and 50 °C on the measurement accuracy was negligible and the maximum absolute error was 3.23% at 30 °C. The verification results confirmed that the proposed LiDAR method can accurately perform online MC measurements.

A Comprehensive Review in Microwave Pyrolysis of Biomass, Syngas Production and Utilisation

Lignocellulosic and waste materials, such as sewage sludge, can be broken down into its useful constituents and converted into fuel for engines. This paper investigates microwave pyrolysis to decompose biomass into H2 and CO (syngas), which may be catalysed in the Fischer–Tropsch (F-T) process to liquid biofuels. Using microwave radiation as the heat source for pyrolysis proves to yield large quantities of gas with higher concentrations of H2 and CO compared to conventional heating methods. This is largely due to the energy transfer mechanism of microwaves. Pyrolysis parameters such as temperature (which increases with input power), feedstock type, microwave absorber, and biomass moisture content influence syngas yield. Several papers reviewed for this study showed differing optimal conditions for microwave pyrolysis, all being heavily dependent on the biomass used and its composition. However, all researchers agreed on the thermal efficiency of microwave heating and how its material-selective nature can increase syngas yield. Compared to diesel fuels (while processing a similar efficiency and a higher cetane number), FT fuels and specifically pyrolysis may yield the benefit of reduced nitric oxides (NOx), particulate matter (PM), unburnt hydrocarbons (HC) and carbon monoxide (CO) emissions.

Prediction of biocrude oil yields from hydrothermal liquefaction using a gradient tree boosting machine approach with principal component analysis

Biomass can be converted to bio-fuels and bio-chemicals via many thermo-chemical conversion platforms such as hydrothermal liquefaction (HTL) which occurs at high temperature and pressure conditions. HTL can be used for converting wet or high moisture content biomass to biocrude oils effectively. There are wide variety of relevant parameters affecting the system of biomass HTL, for example, operating conditions, biomass characteristics, solvent properties, or catalyst identities used. This brings about the difficulty in making highly accurate prediction of biocrude oil yields from biomass HTL. In this work, a gradient tree boosting machine learning (GTB-ML) model with principal component analysis (PCA) was applied to predict the yields of biocrude oils from biomass HTL, using 15 input variables from biomass characteristics, operating conditions, and solvent properties. PCA is a statistical method to find correlations in multivariate data, and it can be used to specify highly influential variables in input datasets for ML models. PCA results confirmed that input variables from biomass characteristics were as important as those from HTL operating conditions. The GTB-ML developed from principal components 1 to 8, representing about 90% of the whole original dataset can be used to predict the yields of bio-crude oils from biomass HTL at acceptable accuracy with correlation coefficient, R2=0.8 and root mean square error = 0.005.

A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification

Supercritical water gasification is a promising technology for renewable hydrogen production from high moisture content biomass. This work produces a machine learning surrogate model to predict the Levelised Cost of Hydrogen over a range of biomass compositions, processing capacities, and geographic locations. The model is published to facilitate early-stage economic analysis (doi.org/10.6084/m9.figshare.22811066). A process simulation using the Gibbs reactor provided the training data using 40 biomass compositions, five processing capacities (10–200 m3/h), and three geographic locations (China, Brazil, UK). The levelised costs ranged between 3.81 and 18.72 $/kgH2 across the considered parameter combinations. Heat and electricity integration resulted in low process emissions averaging 0.46 kgCO2eq/GJH2 (China and Brazil), and 0.37 kgCO2eq/GJH2 (UK). Artificial neural networks were most accurate when compared to random forests and support vector regression for the surrogate model during cross-validation, achieving an accuracy of MAPE: <4.6%, RMSE: <0.39, and R2: >0.99 on the test set.

Impact of Biomass Moisture Content on the Physical Properties of Briquettes Produced from Recycled Ficus nitida Pruning Residuals

Despite its vital importance for life and societal development, energy is a source of conflict and war in many places worldwide. It is also a source of pollution and harmful natural phenomena that affect human life and the environment. These factors have led scientists to consider alternative clean, cheap, and eco-friendly renewable energies. Examples include briquettes consisting of compressed agricultural waste, such as pruning residuals. Hence, this study aimed to evaluate the characteristics of briquettes derived from the pruning residuals of Ficus nitida trees. The results indicate that moisture content was the main factor affecting the physical properties of the produced briquettes. The ideal moisture content for producing high-quality briquettes was 8%. With this moisture content, the briquette durability was 96.9%, the bulk density was approximately 0.18 g·cm3, the compressive strength was 18.5 MPa, and its calorific value was 3250.7 Kcal/kg (17.38 MJ/kg). In conclusion, our research confirms the high quality of briquettes made from the pruning residuals of F. nitida and their promising potential as an energy source.

Experimental Study on Drying Biomass with a Spherical Heat Carrier.

Due to the reduction of the thermal efficiency and output fluctuation of the boiler system caused by the high moisture in biomass, dewatering of fuels using low-cost processes is an important step in feedstock pretreatment for biomass power plants. In the present study, a steel ball was used as the spherical heat carrier (SHC). The effects of the SHC temperature on the dewatering of different biomasses were investigated by a mixture-drying device at 40% moisture content of biomass, and the drying process of peanut shells was analyzed. Results showed that the moisture content was effectively reduced, and the combustion performance of the biomass was significantly promoted. The work is likely to provide an economically feasible approach for biomass drying in further studies.

Dual-objective optimization of a novel hybrid power generation system based on hydrogen production unit for emission reduction

In this study, a biomass based power generation system is proposed. This system includes a BIG (BIG) system to produce syngas, a proton exchange membrane (PEM) type fuel cell (FC), a gas turbine (GT), and an organic Rankine cycle (ORC). In order to evaluate the system function, first a parametric study is conducted and the effect of the design variables on the production power, exergy efficiency, and the total cost rate (TCR) of the system is investigated. Design variables include biomass moisture content, gasification temperature, compressor pressure ratio, air heat exchanger temperature difference, pressure of FC, current density of FC, LP Stage PPTD, and HP stage pressure. It is observed that the determining factors in the TCR of the system are more affected by the cost of the gasifier and PEM FC system. It is also observed that the ORC plays a greater role in recovering wasted heat and generating power compared to GT. Finally, it is observed that in optimal operating conditions, the exergy efficiency of the system are 33.19% and TCR is 693 $/h, respectively.

Comprehensive parametric study of fixed-bed co-gasification process through Multiple Thermally Thick Particle (MTTP) model

Co-gasification technology provides a promising solution for the energetic valorization of various biomass feedstocks, especially those not directly applicable for gasification owing to their low-calorific values or high ash content, but the complexity of co-gasification technology has put forward challenges to model formulation when using numerical methods to evaluate the performance of the gasifier. In the present work, the Multiple Thermally Thick Particle (MTTP) model is developed and validated for fixed-bed co-gasification process. The sub-phase treatment that extends the conventional Eulerian-Eulerian framework allows calculating the inhomogeneity in the solid phase, and the intra- and inter-particle heat and mass transfer sub-models enable quantitative evaluation of the conversion of different solid fuels with nonnegligible intraparticle gradients. Parametric analyses of moisture content and particle size were performed to evaluate how the degree of difference between each fuel would influence the reaction processes, gasification performances and steady-state operating conditions. Upon changing the moisture content of the fuel mixtures (biomass and MSW) from both 9.0 wt% to 9.0 wt% for MSW and 27.0 wt% for biomass, the maximum temperature difference between the solid particle surfaces of different fuels was nearly 150 K, and the regions of different conversion processes became highly overlapped. When increasing the moisture content of biomass from 9.0 wt% to 27.0 wt% under 1:1 mixing ratio, the cold gas efficiency, volume fraction of H2 and higher heating value of syngas increased from 65.26%, 12.03%, and 6.29 MJ/Nm3 to 68.39%, 15.62%, and 6.44 MJ/Nm3, respectively. Therefore, the quality and efficiency can be improved when increasing the moisture content in a certain range although the overall capacity of the gasifier will decrease. The intraparticle temperature profiles calculated by the MTTP model also revealed that decreasing the particle size from centimeter- to millimeter-level will significantly reduce the temperature difference between the surface and center of the fuel particles, thus accelerating the in-bed reaction rates and increasing the overall capacity of the gasifier. The MTTP model is flexible for various working conditions and solid fuel mixtures, which is a versatile and convenient tool to optimize the complicated co-gasification process.