Ash Content Of Biomass Research Articles

Critical insights into ensemble learning with decision trees for the prediction of biochar yield and higher heating value from pyrolysis of biomass

Pyrolysis is an efficient thermochemical conversion process, but accurate prediction of yield and properties of biochar presents a significant challenge. Three prominent ensemble learning methods, viz. Random Forest (RF), eXtreme Gradient Boosting (XGB), and Adaptive Boosting (AdaBoost) were utilized to develop models to predict yield and higher heating value (HHV) of biochar. Dataset comprising 423 observations from 44 different biomasses was curated from peer-reviewed journals for predicting biochar yield. RF regressor achieved a test R2 of 0.86 for biochar yield, while XGB regressor achieved a test R2 of 0.87 for biochar HHV prediction. The SHapley Additive exPlanations (SHAP) analysis was conducted to assess influence of each feature on the model’s output. Pyrolysis temperature and ash content of biomass were identified as the most influential features for the prediction of both yield and HHV of biochar. The partial dependence plots (PDPs) revealed nonlinear relationships, interpreting how the model formulates its predictions.

Utilisation of biomass ash after high-temperature treatment for strengthening purple soil under various curing conditions

As a renewable energy source, biomass has the potential to replace non-renewable, fossil fuels. However, the disposal of the waste biomass ash (generated during energy generation) needs to be studied. While prior studies attempted to utilise composite additives containing biomass ash for soil, the introduction of other additives, such as cement, was an environmental burden. By employing biomass ash composition as the sole additive for strengthening purple soil under various curing conditions using high-temperature treatment, this study maximised its utilisation. The results showed that the unconfined compressive strength (UCS) varied across different curing conditions as the biomass ash content increased. After high temperature treatment at 800 ℃, the biomass ash consistently reinforced purple soil under all the curing conditions. However, the biomass ash stabilisation mechanism differed between dry and humid curing conditions. Under dry curing conditions, the UCS increase depended on the cementing effect of soluble salt and/or insoluble calcite; under humid curing conditions, the UCS change was attributed to the damage to clay minerals, contact mode, and cementing effects of multiple components. Therefore, the 800 ℃ temperature-treated biomass ash can be used alone to reinforce purple soil, inhibiting the soil-water loss. This study presents a novel avenue for utilising waste, biomass ash, with considerable implications for environmental protection and soil stabilisation.

The possibility of modeling agricultural biomass ash by neural networks concerning proximate analysis inputs

Agricultural biomass is an important renewable energy source with significant environmental and economic benefits. However, high ash content in biomass can lead to problems such as slagging, fouling, and corrosion and can reduce the efficiency of energy systems. This study analyzes the proximate composition of different biomass samples, focusing on ash content, and uses machine learning to model ash content based on other components. Six biomass types, including rapeseed, barley, wheat, corn, soybean and sunflower, were examined to analyze the content of coke, fixed carbon (FC), volatile matter (VM) and ash. The results showed considerable variability, with ash content ranging from 8.25 % for rapeseed to 12.3 % for soybean. Artificial neural networks (ANN) were used to model ash content with a high accuracy of R? = 0.92. The model effectively estimated the ash content based on the input parameters and demonstrated the potential of machine learning to optimize biomass selection for energy production.

Optimization of liquefaction process based on global meta-analysis and machine learning approach: Effect of process conditions and raw material selection on remaining ratio and bioavailability of heavy metals in biochar

<abstract> <p>Although liquefaction technology has been extensively applied, plenty of biomass remains tainted with heavy metals (HMs). A meta-analysis of literature published from 2010 to 2023 was conducted to investigate the effects of liquefaction conditions and biomass characteristics on the remaining ratio and chemical speciation of HMs in biochar, aiming to achieve harmless treatment of biomass contaminated with HMs. The results showed that a liquefaction time of 1–3 h led to the largest HMs remaining ratio in biochar, with the mean ranging from 84.09% to 92.76%, compared with liquefaction times of less than 1 h and more than 3 h. Organic and acidic solvents liquefied biochar exhibited the greatest and lowest HMs remaining ratio. The effect of liquefaction temperature on HMs remaining ratio was not significant. The C, H, O, volatile matter, and fixed carbon contents of biomass were negatively correlated with the HMs remaining ratio, and N, S, and ash were positively correlated. In addition, liquefaction significantly transformed the HMs in biochar from bioavailable fractions (F1 and F2) to stable fractions (F3) (<italic>P</italic> < 0.05) when the temperature was increased to 280–330 ℃, with a liquefaction time of 1–3 h, and organic solvent as the liquefaction solvent. N and ash in biomass were positively correlated with the residue state (F4) of HMs in biochar and negatively correlated with F1 or F2, while H, O, fixed carbon, and volatile matter were negatively correlated with F4 but positively correlated with F3. Machine learning results showed that the contribution of biomass characteristics to HMs remaining ratio was higher than that of liquefaction factor. The most prominent contribution to the chemical speciation changes of HMs was the characteristics of HMs themselves, followed by ash content in biomass, liquefaction time, and C content. The findings of this meta-analysis contribute to factor selection, modification, and application of liquefied biomass to reducing risks.</p> </abstract>

Preparation of Biomass Biochar with Components of Similar Proportions and Its Methylene Blue Adsorption.

Rapeseed straw, bagasse, and walnut peel have a large amount of resource reserves, but there are few technologies for high value-added utilization. In the research of biochar, walnut green husk is rarely used as raw material. In addition, the three main components of biomass (lignin, cellulose, and hemicellulose) are present in similar proportions, and the differences between the physical and chemical properties of biochar prepared with similar amounts of biomass raw materials are not clear. Using three kinds of biomass of the same quality as raw materials, biochar was prepared via pyrolysis at 400 °C, and activated carbon was prepared via CO2 activation at 800 °C. The results showed that the pore numbers of the three kinds of biochar increased after activation, resulting in the increase of the specific surface area. The resulting numbers were 352.99 m2/g for sugarcane bagasse biochar (SBB)-CO2, 215.04 m2/g for rapeseed straw biochar (RSB)-CO2, and 15.53 m2/g for walnut green husk biochar (WGB)-CO2. Ash increased the amount of carbon formation, but a large amount of ash caused biochar to form a perforated structure and decreased the specific surface area (e.g., WGB), which affected adsorption ability. When the three main components were present in similar proportions, a high content of cellulose and lignin was beneficial to the preparation of biochar. The adsorption value of MB by biochar decreased with the increase of biomass ash content. After activation, the maximum adsorption value of MB for bagasse biochar was 178.17 mg/g, rapeseed straw biochar was 119.25 mg/g, and walnut peel biochar was 85.92 mg/g when the concentration of methene blue solution was 300 mg/L and the biochar input was 0.1 g/100 mL at room temperature. The adsorption of MB by biochar in solution occurs simultaneously with physical adsorption and chemical adsorption, with chemical adsorption being dominant. The optimal MB adsorption by SBB-CO2 was dominated by multimolecular-layer adsorption. This experiment provides a theoretical basis for the preparation of biochar and research on its applications in the future.

Unlocking the potential of Dongting Lake-grown Miscanthus lutarioriparius biomass: A comprehensive quality analysis and bioproduct application study

Miscanthus lutarioriparius grown in Dongting Lake has an annual biomass yield potential of 1 million tons. However, with the shutdown of its previous utilization for paper-making, abandoning this huge amount of biomass has caused serious economic, ecological, and social problems. Constructing an industrial cluster to continuously convert biomass into various bioproducts is a win-win measure to address this dilemma. With the increasing confirmation of the importance of biomass quality affecting the conservation process, fully understanding the biomass characteristics of Dongting Lake-grown M. lutarioriparius is crucial for building a scientific industrial cluster. The present work is designed to explore the variation in biomass quality across the entire Dongting Lake area. Results show that the biomass contented with Cd, Mn, Zn, and Cr has significant geographical differences, with a general trend of Southern Dongting Lake-grown biomass having a higher concentration than that from Eastern and Western Dongting Lake areas. Moreover, significant differences are found in terms of biomass ash content, lignin content, and the degree of polymerization of cellulose (DP). The biomass with low ash content is generally from the entire Eastern Dongting Lake area and the northern part of the Western Dongting Lake area. Virtually all Western Dongting Lake-grown biomass has a low lignin content (approximately 18 %). Regarding the spatial variation of DP, Eastern Dongting Lake-grown biomass has a higher DP (average at 585.33) than that in Southern (575.15) and then Western Dongting Lake (529.16). Based on these quality indicators, the biomass production potentials for bioethanol, biochar, and xylo-oligosaccharide were calculated and visualized. Results show that biomass from almost the entire Western and Eastern Dongting Lake area is suitable for bioethanol and xylo-oligosaccharide production, while biomass from the Southern Dongting Lake area for biochar production. These results provide scientific guidance for the future utilization of Dongting Lake-grown M. lutarioriparius biomass.

Effect of biomass ash vermicompost on Sorghum bicolor var. saccharatum (L.) Mohlenbr under hot and dry agro ecological condition

Generation of the huge amount of bio-waste and their residues, including incineration ash, is a major technical and sustainability problem. To solve this problem, incorporating nutrient-rich residues into crop production has become an efficient practice to increase crop production. Vermicomposting of these wastes could be a viable option to manage both biowastes and their products in an environmentally friendly manner and close the material loop in bioenergy production. Therefore, the main objective of this study was to investigate the effect of vermicompost from biomass ash under hot and dry climatic conditions in summer on growth, yield and yield components of sweet sorghum (Sorghum bicolor var. saccharatum (L.) Mohlenbr). The high photosynthetic activity of sweet sorghum is important for biofuel production under conditions of high solar energy and water scarcity. This study provides a general overview of the feasibility of biomass ash vermicomposting processes and their potential use as a nutrient source for C4 sorghum under Bitlis ecological conditions of high solar potential and low water availability. Under Bitlis climatic conditions, the best yield was obtained when vermicompost was applied with a biomass ash content of 10.0% (T3). Plant height, plant weight, sugarcane and juice yields were reported as 133 cm, 146 g, 180 kg/da and 105 L/da, respectively.

Effect of Biomass Ash on the Properties and Microstructure of Magnesium Phosphate Cement-Based Materials

The disposal of biomass ash (BA) will be of great importance for environmental protection and sustainability, and the aim of this study is to analyze the feasibility of the resourceful use of biomass ash in civil engineering materials. The effects of the content and type of biomass ash on the flowability, setting time, compressive strength, flexural strength, bonding strength, and drying shrinkage of magnesium phosphate cement (MPC) mortar were investigated. In addition, the effects of BA on the hydration and microstructure of MPC were investigated by X-ray diffraction (XRD), thermogravimetry, mercury intrusion porosimetry (MIP), and scanning electron microscope (SEM). The results showed that BA significantly affects the flowability and setting time of MPC mortar. The compressive and flexural strength of MPC mortars decreases with increasing amounts of BA. The drying shrinkage of MPC mortar specimens increases exponentially with the increase of BA content. The incorporation of BA will reduce the bonding strength of the MPC mortar, which is associated with increased drying shrinkage. The incorporation of BA into MPC results in low hydration product generation and poor pore structure. The incorporation of BA into MPC has a significant effect on the microstructure morphology and the hollow columnar-like hydration product may be formed by the reaction of BA with MgO in the paste.

Impact of biomass ash content on biocomposite properties

Owing to its low cost and sustainable nature, lignocellulosic biomass has been utilized for reinforcing polymers, but it is crucial to understand the impact of high-ash concentrations in biomass on composite strength and processing. Biomass is not only desirable for biofuel production but could also have a strong market, if high-ash biomass is acceptable, for biocomposites. In this work, natural fibers (switchgrass and corn stover) were used to reinforce polylactic acid (PLA) to produce biocomposites. Natural fibers were pretreated to obtain fibers that contain different percentages of ash. The mechanical properties (such as Young's modulus, tensile strength, failure strain, storage modulus) of corn stover/PLA composites remained largely unaffected by the ash concentration of the biomass fibers, despite the large range of ash contents (2.2–11.9 wt%). However, the tensile strengths of switchgrass/PLA composites were slightly negatively affected by the ash concentration of the switchgrass fibers (0.7–2.1 wt%). Both the switchgrass/PLA and the corn stover/PLA composites exhibited a high-enough tensile strength (49–57 MPa) and suitable complex viscosity (2.0−7.0 kPa·s at the frequency of 3.2 rad/s). They are expected to be 3D-printable through an extrusion-based additive manufacturing process.

Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)

The slagging problem that occurs during the combustion of biomass fuels is difficult to deal with, affecting the safe operation of the boiler and reducing the combustion efficiency. Predicting the slagging tendency of biomass combustion can guide the selection of fuel, reduce the experimental cost and improve the production efficiency of the boiler. In this paper, 114 kinds of biomass are selected as the data set. Through the visualization of the overall distribution of the data set, it is found that the data distribution has certain rules, but the whole overlap and cross distribution. The quantitative relationship between slagging degree classification and each element was obtained by Spearman correlation analysis. The higher the content of Ca, Mg and P elements, the smaller the slagging degree, while the more Si, Cl and Ash content, the more serious slagging. Taking 13 kinds of chemical elements and biomass ash content (%) as input and slagging type as output, and the network models of Deep Neural Network, Recurrent Neural Network and Long Short Term Memory Neural Network were built by python 3.6 in TensorFlow virtual environment. The four indexes of Accuracy, Precision, Recall and F1_score were used to evaluate the model. The accuracy of Deep Neural Network model was highest, which was 0.8260869565217391, 0.827380523809524, 0.8425925925926, 0.8322021116138764, respectively. The particle swarm optimization algorithm is used to optimize the highest precision DNN model, and the optimization accuracy is improved to 0.9130434. The optimized model was used to predict the slagging types of other 571 biomass, and the results provide guidance for the actual operation of biomass fuel boilers, thus saving experimental time and reducing experimental costs. At the same time, this study provides a new method for predicting the slagging trend of biofuels.

Exploiting Data Uncertainty for Improving the Performance of a Quantitative Analysis Model for Laser-Induced Breakdown Spectroscopy.

The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposes a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed a lower relative standard deviation (RSD) value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.

Properties of Upgraded Bio-oil from Pyrolysis of Waste Corn Cobs

Technologies for conversion of waste solid materials to liquid fuel and bio-crude oil have been researched widely for the production of renewable energy as substitute to fossil fuel oil. However, ash composition of biomass affects the pyrolysis process and the bio-crude oil product has unsatisfactory properties compared to conventional petroleum oil, such as, low heating value, high viscosity, corrosiveness, and the presence of oxygenated compound which causes bio oil ageing. This paper investigated the total waste materials; corn cobs and paper sludge obtained in municipal areas of Abuja, Nigeria, employed in pyrolysis of demineralized corn cobs and the upgrade of crude bio oil via thermal cracking using zeolite prepared from waste paper sludge, with expectation to improve bio oil properties. Demineralization of corn cob removed most of the ash content of biomass allowing for pyrolysis process. The prepared zeolite with mesoporous cage-like crystals analyzed using SEM was able to effectively catalyze thermal cracking of the crude bio oil and reduce the quantity of less desired high molecular weight oxygenated compounds. The bio oil chemical composition obtained from GC-MS analysis indicated the bio oil consisted of oxygenated compounds and hydrocarbons such as aliphatic hydrocarbons (28.768%), alcohols (-0.001%), amines (10.472%), carboxylic acids (0.144), phenols (0.047%), and esters (60.57 %), which significantly influenced the bio oil properties. The physical and chemical properties of the corn cob bio oil was determined for density (0.852 ± 0.03), viscosity (1.66 ± 0.01), cloud point (-34.0 ± 0.02) and calorific value (30.9 ± 0.01). With the exception of Flash point (58 ± 0.01) and acid value (13.1 ± 0.03). In comparison, the produced bio oil had properties likened to petroleum fraction of conventional gasoline than diesel. In conclusion, pyrolysis of corn cob and upgrade of the crude bio oil using prepared zeolite was found as a promising process in improving bio oil quality. The pyrolysis study has potential in the management of environmental wastes to help resolve the challenge of solid waste disposal.

Machine learning prediction and analysis of commercial wood fuel blends used in a typical biomass power station

Biomass can be utilised as a near carbon neutral fuel for power generation. Biomass may come in a variety of forms, such as woods or agricultural wastes, with highly variable compositions. It is well known that biomass ash content and composition can introduce severe operational challenges to power stations, such as slagging and corrosion. Machine learning approaches have been successfully applied in a variety of contexts to generate predictive models and identify relationships in large data sets. However, such approaches have not previously been applied to biomass trace element or ash content data, in part due to limited large data set availability. In this work, 5 years of fuel blend analysis data (3500+ data sets) was analysed from a 35MWe biomass power station burning a 60/40% blend of virgin wood and recycled (waste) wood. Variation to ash content and key trace elements (K, Na, Pb, Zn, Cl) was analysed and compared versus a literature average benchmark. Identification of underlying relationships between these key components and others was attempted with principal component analysis and random forest regression machine learning. Ash and chlorine exceeded the literature average benchmark over many time periods and would have a negative impact on boiler operation. Potassium and sodium were only above literature average levels intermittently. No significant underlying relationships for the key components could be identified with principal component analysis or random forest regression, nor could an accurate predictive model be created, though some minor trends were noted. This is likely due to the high degree of heterogeneity seen in the fuel data, as it is a blend of virgin and recycled wood. It is suggested that future studies applying machine learning methods in this context either use singular fuel data sets, or that additional information is recorded within the blended data set when analysis fuel composition (e.g. fuel sources, suppliers, blend ratio). Suggestions were also made regarding improvements to waste wood sampling approaches.

Study on Strength and Deformation Characteristics of Expansive Soils Treated with Lime-biomass Ash

This paper investigates the strength deformation of the lime-biomass ash treated expansive soils. The unconfined compressive strength, one-dimensional compression of the modified expansive soil of compaction were studied. The results showed that the maximum dry density and optimum moisture content of expansive soil decreased with the increase of lime and biomass ash content; The unconfined compressive strength test results show that there is no remarkable change in the unconfined compressive strength of the soils immediately treated with biomass ash, but the sample after 7 days of curing period of strength has been greatly improved, especially after the addition of lime is more obvious; the compression coefficient, the rebound coefficient of the saturated expansive soil with lime-biomass ash is significantly smaller than saturated expansive soil. The compressive modulus of the modified expansive soil increases with the increase of vertical pressure, which reflects the hardness of the improved expansive soil.

Comparative Research of Thermochemical Conversion Properties of Coarse-Energy Crops

In the world, as in Lithuania, there is a costant search for new crops suitable for energy conversion. The coarse-energy crops and their biomass studied for this paper were assessed in a comprehensive manner, i.e., not only their calorific value and ash content but also their ash melting properties and pollutants emitted during the thermochemical conversion. The calorific value of energy crops varies from 17.92 ± 0.32 to 18.50 ± 0.66 MJ kg−1 and decreases in the following order: A. dubia > M. giganteus > C. sativa. Ash content varies from 1.51 ± 0.03 to 3.36 ± 0.23% and decreases in the following order: C. sativa > A. dubia > M. giganteus. The lowest primary ash deformation (648 ± 8 °C) was recorded for C. sativa. Taking into account the specificity of our research and the changes in biomass ash content due to mineral nitrogen fertilization, it has been found that that higher levels of nitrogen fertilizers in the combustion products reduce CO and increase the total CO2 content of the combustion product. Significant changes in fertilization were usually 170 kg ha−1 for A. dubia and 90 kg ha−1 for M. giganteus. In summary, A. dubia, M. giganteus and C. sativa biomass should be used for thermochemical conversion.

An eco-friendly approach for blending of fast-pyrolysis bio-oil in petroleum-derived fuel by controlling ash content of loblolly pine

This paper describes the effect of the ash content in biomass on the distribution of pyrolysis products and the miscibility of bio-oil in diesel. Ash content of loblolly pine wood (0.5 wt %, 1.1 wt %, and 1.5 wt %) was systematically varied by impregnating the wood with potassium carbonate solution. Variation in the ash content did not create a significant change in the chemical composition of the impregnated biomass. However, the response to a variety of thermal treatments changed significantly. The volatile matter content decreased from 88.3% to 78.2%, while the bio-oil yield declined from 45.7% to 29.9% as the ash content increased. Although the total organic yield decreased with increased biomass ash content, the total concentration of phenolic monomers increased from 2.8 mg/g to 20.2 mg/g, and bio-oil miscibility with a commercial diesel fuel increased from 6.7% to 13.4% based on wet bio-oil. The miscibility of guaiacol and 4-methyl guaiacol in diesel was higher than that of catechol, due to the lower polar and hydrogen bonding contribution. Test in a diesel engine showed a simultaneous reduction of HC and NOx emissions using diesel extracts.

The effects of biomass ash on soil evaporation and cracking

Biomass materials have been rapidly used in generating power, heating, and other industrial applications; the remains from the combustion of biomass materials, biomass ash, are being produced in large quantities rapidly. Accordingly, a method of consuming biomass ash, expanding new applications, and achieving resource utilization is urgently required. This research studies the basic properties of adding biomass ash in soils by examining the effects of water evaporation and the development of shrinkage cracks in soils. Laboratory evaporation tests have been carried out on soil samples with different quantities of biomass ash. The loss of mass is recorded to evaluate the effect of biomass ash on the behavior of water evaporation in the soil. Image processing technology and fractal theory are used to quantitatively analyze the development of shrinkage cracks on the soil surface. The results show that biomass ash can effectively reduce evaporation and crack development in soil. With increases in the biomass ash content, although the difference of residual water content is small, the evaporation time is increased with decreasing crack ratio. The addition of biomass ash greater than 5% has a significant impact (p ≤ 0.05) on soil evaporation, and the addition greater than 2.5% has a significant impact (p ≤ 0.05) on soil cracking. The time of evaporation with biomass ash addition amount of 2.5%, 5%, 10%, 15%, and 20% increased by 10%, 22%, 32%, 43%, and 49% in turn, the crack ratio of which decreased by 12%, 42%, 65%, 72%, and 74%, respectively, over control samples.

Sustainable Production of Monoraphidium Microalgae Biomass as a Source of Bioenergy

Microalgae are a renewable source of unconventional biomass with potential application in the production of various biofuels. The production of carbon-neutral fuels is necessary for protecting the environment. This work determined the possibility of producing biomass of microalgae belonging to Monoraphidium genus using saline wastewater resulting from proecological salmon farming in the recirculating aquaculture system. The tests were carried out in tubular photobioreactors using LED light. As a part of the analyses, the growth and productivity of microalgal biomass, cell density in culture, and lipid concentration and ash content in biomass were determined. In addition, the concentration of selected phosphorus and nitrogen forms present in wastewater corresponding to the degree of their use by microalgae as a nutrient substrate was determined. The biomass concentration estimated in the tests was 3.79 g·L−1, while the maximum biomass productivity was 0.46 g·L−1·d−1. The cells’ optical density in culture measured at 680 nm was 0.648. The lipid content in biomass was 18.53% (dry basis), and the ash content was 32.34%. It was found that microalgae of the genus Monoraphidium effectively used the nitrogen as well as phosphorus forms present in the wastewater for their growth. The total nitrogen content in the sewage decreased by 82.62%, and total phosphorus content by over 99%. The analysis of the individual forms of nitrogen showed that N-NO3 was reduced by 85.37% and N-NO2 by 78.43%, while orthophosphate (V) dissolved in water was reduced by 99%. However, the content of N-NH4 in wastewater from the beginning till the end of the experiment remained <0.05 mg·L−1.

The risk analysis of rice husk of co-firing fuel for boilers in sugar mills

Biomass is a very important energy source in the sugar mill industry as the main fuel in the power plant boilers. The efficiency of boilers for most sugar mills in Indonesia is quite low and the availability of bagasse is lacking so that in its operations, co-firing has to be applied with an alternative biomass fuels such as rice husk, cane trash, wood and saw dust. Based on the analysis, rice husk has a lower calorific (3248 cal/gr) value than that of bagasse (4076 cal/gr), but it has a higher ash content (20.67%) than that of bagasse (3.66%), so adjustments in boiler operation is required when using rice husk as co-firing. The higher ash content in biomass can affect the performance of the furnace and superheater in the boiler and increase the potential for deposit formation. The composition of rice husk ash was dominated by silica (SiO2) which reached 95.52%. It is higher than the silica content of bagasse which reached 74.99%. The high silica content in rice husks has the potential to conduce melting ash in boiler grate. The alkali content of rice husk which was around 3.64% is less than that found in bagasse which reached 17.33%. Even though, the ash composition analysis shows quite small potential for slagging, fouling, melting, sintering, and agglomeration, but the large amount of ash content in rice husks can increase the potential for the occurrence of the risks.

Energy potential and thermogravimetric study of pyrolysis kinetics of biomass wastes

The use of agricultural wastes for energy conversion has been widely studied as renewable and carbon neutral energy sources. This paper aims to evaluate the energetic potential of six agricultural wastes—sugarcane bagasse, bean pods, corn stover, pineapple crown leaves, white cotton and natural coloured cotton stalks, through their characterization and pyrolysis kinetic study. The energetic potential of biomasses was evaluated by ultimate and proximate analysis, higher heating value (HHV), apparent density, and kinetic parameters of conversion and apparent activation energy (Ea) determined by Model-Free kinetics though thermogravimetric analysis data. The results indicate energetic density for dry basis biomasses, such as moisture content less than 7%, volatiles higher than 77% and moderate ash content. The HHVs were higher for the biomass with low O:C ratio. The Ea values increased with increasing O:C ratio and were also influenced by the biomass ash content. Among the studied biomasses, PCL are less explored for energy application, although the results confirm its potential for application in thermochemical processes such as pyrolysis or combustion.