Developing a Proximate Component Prediction Model of Biomass Based on Element Analysis

Thirty-nine different species of waste biomass materials that include woody or herbaceous resources as well as nut shells and juice pulps were used to develop empirical equations to predict the calorific value based on the proximate analysis results. Ten different linear/nonlinear equations that contain proximate analysis ingredients including or excluding the moisture content were tested by means of least-squares method to predict the HHV (higher heating value). Prediction performance of each equation was evaluated considering the experimental and the predicted values of HHV and the criteria of MAE (mean absolute error), AAE (average absolute error), and ABE (average bias error). It was concluded that the presence of moisture as a parameter improves the prediction performance of these equations. Also, the samples were classified into two subsets according to their fixed carbon (FC)/ash values and then the correlations were repeated for each subset. Both the full set of samples and the subsets showed a similar trend that the presence of moisture in equations enhances the prediction performance. Also, the FC content may be disregarded from the equation of the calorific value prediction when the FC/ash ratio is lower than a given value.

ABSTRAK
 Batubara high – low volatile bituminous Formasi Tanjung di Kalimantan Tengah dikenal berpotensi sebagai coking coals. Maksimum fluiditas merupakan ssalah satu parameter penting yang sangat berkaitan dengan kualitas kokas yang dihasilkan. Penelitian ini bertujuan untuk menganalisis distribusi kandungan zat terbang dan abu serta pengaruhnya terhadap maksimum fluiditas batubara khususnya untuk batubara dari Formasi Tanjung. Batubara diambil dari lapangan di Daerah Sekako dengan channel sampling ply by ply berdasarkan litotipenya kemudian dilakukan analisis laboratorium meliputi analisis proksimat dan analisis Gieseler plastometer. Batubara yang diambil dari 2 seam Formasi Tanjung yang diproduksi tersusun dominan atas litotipe bright coals dan banded bright coals dengan memiliki kandungan abu (ash) 2,79 – 9,05 (wt%, adb), kandungan zat terbang (volatile matter) 35,14 – 39,50 (wt%, adb) dan maksimum fluiditas 22263 – 49029 (ddpm). Kandungan abu berkorelasi negatif kuat (r = -0,656; R2 = 0,431) dan tidak berpengaruh signifikan (sig. 0,055 > 0,05) terhadap maksimum fluiditas batubara. Sementara itu, kandungan zat terbang berkorelasi positif sangat kuat (r = 0,794; R2 = 0,6301) dan berpengaruh signifikan (sig. 0,003 < 0,05) terhadap maksimum fluiditas batubara. Semakin tinggi kandugan abu maka maksimum fluiditas batubara semakin rendah dan sebaliknya semakin tinggi kandungan zat terbang maka maksimum fluiditas batubara semakin tinggi. Hubungan kandungan zat terbang dengan maksimum fluiditas batubara dapat dinyatakan dengan persamaan y = 6327,9x - 200248.

Many correlations are available in the literature to predict the higher heating value (HHV) of raw biomass using the proximate and ultimate analyses. Studies on biomass torrefaction are growing tremendously, which suggest that the fuel characteristics, such as HHV, proximate analysis and ultimate analysis, have changed significantly after torrefaction. Such changes may cause high estimation errors if the existing HHV correlations were to be used in predicting the HHV of torrefied biomass. No study has been carried out so far to verify this. Therefore, this study seeks answers to the question: “Can the existing correlations be used to determine the HHV of the torrefied biomass”? To answer this, the existing HHV predicting correlations were tested using torrefied biomass data points. Estimation errors were found to be significantly high for the existing HHV correlations, and thus, they are not suitable for predicting the HHV of the torrefied biomass. New correlations were then developed using data points of torrefied biomass. The ranges of reported data for HHV, volatile matter (VM), fixed carbon (FC), ash (ASH), carbon (C), hydrogen (H) and oxygen (O) contents were 14.90 MJ/kg–33.30 MJ/kg, 13.30%–88.57%, 11.25%–82.74%, 0.08%–47.62%, 35.08%–86.28%, 0.53%–7.46% and 4.31%–44.70%, respectively. Correlations with the minimum mean absolute errors and having all components of proximate and ultimate analyses were selected for future use. The selected new correlations have a good accuracy of prediction when they are validated using another set of data (26 samples). Thus, these new and more accurate correlations can be useful in modeling different thermochemical processes, including combustion, pyrolysis and gasification processes of torrefied biomass.

Self-heating of coal-shales has been reported in some South African coal mines to be one of the causes of spontaneous combustion but not the coal alone. Spontaneous combustion tests were conducted on a series of coal-shale samples collected from affected coal mines using a newly developed liability index referred to as the Wits-CT index. The ash content, volatile matter and the carbon content of the samples were investigated, and their effects towards spontaneous combustion liability were established. The study indicated that as ash content increases, the liability towards spontaneous combustion decreased, while as the volatile matter and carbon content increases, the liability towards spontaneous combustion increased. It was further found that the ash content showed a negative effect on the liability index, while volatile matter and carbon content showed positive effect on the liability index. Coal-shales with low ash content and high volatile matter and carbon content are more likely to be prone to spontaneous combustion.

This study investigated the thermochemical characterization of oil palm fronds (OPF), coconut shells (CS) and wood for their use as a solid fuel for thermal conversion processes. The ultimate analysis, proximate analysis, calorific values, and elemental contents through energy dispersive X-ray spectroscopy of OPF, CS, and wood samples were measured. The results of OPF and CS were compared with wood considered as benchmark solid fuel. Proximate analysis was performed as per ASTM standard procedure in a muffle furnace and used thermos-gravimetric analysis technique. The ultimate analysis was used to determine the weight percentage of carbon, hydrogen, and nitrogen in CHNS analyzer. Elements analysis was done using energy dispersive X-ray spectroscopy. The ultimate analysis results show carbon content was higher in CS as compared to OPF and wood. The hydrogen content was higher in OPF. Proximate analysis results revealed that volatile matter was higher in wood, whereas fixed carbon and higher heating value were found higher in CS while ash content was lower in CS. From EDX results found that the OPF has Al, Si, Cl, and K, while, in wood and CS these elements are absent. The thermochemical characterization results of OPF and CS were comparable with the wood. Therefore, it concluded that OPF and CS have the potential to be used as renewable energy source by using appropriate energy conversion technologies, such as gasification, pyrolysis, and torrefaction.

The heating value is one of the most important properties of biomass materials, as it can be used for design calculations or numerical simulations of the thermal conversion systems of biomass. The heating value can be determined experimentally or numerically. The determination of the heating value experimentally (by using a bomb calorimeter) involves laborious measurements while an ultimate analysis can be performed using automatic equipment. In this study, a multiple linear regression analysis was used to develop an empirical equation for the estimation of the higher heating value of Oil Palm Fronds (OPFs) using an ultimate analysis. An empirical equation based on the main elements carbon, hydrogen and oxygen (HHV = 0.879C+0.321H+0.056O-24.826) was the most accurate model, with a correlation coefficient (R2) of 0.92, Average Absolute Error (AAE) of 1.4% and Average Bias Error (ABE) of 0.16%.

The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75%) and the validation (25%) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors.

Diving dynamics identification and motion prediction for marine crafts using field data

The price of coal in the market is largely determined by the caloric value. To find out what parameters affect the calorific value, it is necessary to do a correlation between the calorific value, inherent moisture, ash content and volatile matter contained in the coal where these contents are found in the proximate analysis and the carbon, nitrogen and hydrogen content in the ultimate analysis. In this study, apart from correlating the proximate data and ultimate data with calorific value, these two data were also correlated with total sulfur because sulfur greatly impacts the environment, so it is necessary to ensure that the amount is below the set threshold. The correlation used is Pearson Correlation. Determination of coal classification using standards Americans Society For Testing & Materials (ASTM). Correlation test results showed that total sulfur had a very strong correlation with inherent water content, volatile matter content, fixed carbon and hydrogen content. The calorific value correlates very strongly with the inherent water content, fixed carbon and nitrogen content. However, total sulfur and calorific value did not correlate with ash content. The coal classification of the five samples is included in the sub-bituminous class with a calorific value range of 10,500 Btu/lb - 8,300 Btu/lb.

Biochar has important biogeochemical functions in soil—first as a means to sequester carbon, and second as a soil conditioner to potentially enhance soil quality and fertility. Volatile matter (VM) content is a property of biochar that describes its degree of thermal alteration, which can have a direct influence on carbon and nitrogen dynamics in soil. In this study, we characterized the VM in biochars derived from two locally sourced feedstocks (corncob and kiawe wood) and evaluated the relationship of VM content to nitrogen transformations and culturable fungal biomass. Using 13C nuclear magnetic resonance (NMR) spectroscopy, we found that the VM content of biochar primarily consisted of alkyl (5.1–10.1%), oxygen-substituted alkyl (2.2–6.7%), and phenolic carbon (9.4–11.6%). In a series of laboratory incubations, we demonstrated that corncob biochars with high VM (23%) content provide a source of bioavailable carbon that appeared to support enhanced viable, culturable fungi (up to 8 fold increase) and cause nitrogen immobilization in the short-term. Corncob biochar with bioavailable VM was nitrogen-limited, and the addition of nitrogen fertilizer resulted in a four-fold increase in total hydrolytic enzyme activity and the abundance of culturable fungal colonies. In contrast, kiawe biochar with an equivalent VM content differed substantially in its composition and effect on these same biological parameters. Therefore, the rapid measurement of VM content is too coarse to differentiate chemical composition and to predict the behavior of biochars across feedstocks and production methods.

Water hyacinth is a locally available biomass with the potential to be converted into biochar, serving as a renewable energy source. In this report, response surface methodology (RSM) was employed to optimize the carbonization temperature during the preparation of water hyacinth biochar. Water hyacinth was carbonized in a furnace at varied temperatures (400 °C, 500 °C, 600 °C) for 90 minutes. Characterization of the biochar derived from water hyacinth by proximate analysis was determined, including moisture content, ash content, volatile matter, fixed carbon, and calorific value. The results of the biochar before optimization indicated that moisture content and volatile matter decreased with increasing carbonization temperature, while ash content, fixed carbon, and calorific value increased. After optimization, the proximate analysis of the biochar was determined, with the optimal carbonization temperature found to be 533.54 °C. At this temperature, the optimal moisture content was 6.50%, ash content was 25.53%, volatile matter was 24.80%, and fixed carbon was 43,16%. These findings demonstrate the feasibility of using RSM to optimize the preparation conditions of water hyacinth biochar.

Following a significant production breakthrough in the deep coalbed methane (CBM) reservoir located in the Ordos Basin, developing deep CBM reservoirs has become a strategic focus for various operators across different basins in China. These deep CBM reservoirs exhibit far more intricate intrinsic mechanisms and heterogeneous properties than other unconventional reservoirs. The mineral composition and content in deep CBM are highly diverse. The storage capacity of deep CBM reservoirs is a complex interplay of cleats and various types of pores. The way gas is stored in deep CBM reservoirs is also markedly different from others. These complex and heterogeneous characteristics pose significant challenges in evaluating the mineralogy, porosity, and saturation levels of deep CBM reservoirs. To gain a thorough understanding of these intricate systems, an integration of two-dimensional nuclear magnetic resonance (2D NMR) and gamma-ray spectroscopy measurements was employed in several basins. These techniques were used to characterize the mineral composition, pore structure, porosity, saturation, and gas in place (GIP) for the deep CBM reservoirs. The 2D NMR measurements were employed to characterize pore structures, porosity, saturation, and fluid components using a customized T1-T2 interpretation model. Gamma-ray spectroscopy was used to analyze the mineralogy and perform proximate analysis. The T1-T2 map provided a schematic illustration of fluid components, combined with NMR core analysis, enabling the development of a customized T1-T2 interpretation model. This model facilitated the accurate partitioning of porosity and identification of fluid components, enabling precise estimation of total porosity, effective porosity, gas volume, water volume, saturation, etc. Furthermore, X-ray diffraction analysis on core samples contributed to the establishment of a customized mineral evaluation model. This model enabled the accurate estimation of mineral components, especially for coal, their contents, and proximate analysis metrics, including fixed carbon, ash, volatile matter, and moisture content. By integrating the porosity and saturation data obtained from 2D NMR measurements with the mineral components and proximate analysis results derived from gamma-ray spectroscopy, the studies successfully quantified the GIP for the deep CBM reservoirs. Meanwhile, this integration provides a comprehensive understanding of the deep CBM reservoir's characteristics. The studies provide a comprehensive characterization of deep CBM reservoirs, detailing their porosity, fluid components, saturation levels, mineral composition, proximate analysis, and GIP. This was achieved through the integrated application of 2D NMR and gamma-ray spectroscopy logging techniques.

The removal of Pb ions by activated carbons prepared from velvet tamarind (Dialium indum) shells was studied to investigate its uptake potentials using column sorption at different operating conditions (flow rates, initial concentrations and bed height). The prepared adsorbent was characterized by determining the physicochemical properties, proximate analysis, carbon, Hydrogen, Nitrogen and Sulpur analysis, Fourier Transform-Infra Red, Potentiometric titration. Different dynamic models were used to describe the sorption processes. The FTIR analysis results suggested the presence of functional groups such as hydroxyl, carbonyl, carboxyl and amine which could bind the metals and remove them from the solution. The values of moisture content, volatile matter, fixed carbon and ash content as obtained from % proximate analysis are 3.43, 27.07, 65.05, 4.45 for activated carbons prepared from velvet tamarind shells. Ultimate analysis revealed that activated carbons prepared from velvet tamarind shells contained 75% carbon. The surface area and Iodine number of activated carbon from velvet tamarind shell are 570 m2g-1 and 614.7 mgg-1 respectively. The column experimental data revealed that an increase in bed height and initial metal concentration or a decrease of flow rate enhances the longevity of column performance by increasing both breakthrough time and exhaustion time thereby delaying bed saturation. Low ash content and high surface areas are indication of good mechanical strength and microporosity of the activated carbons prepared from this precursor. The activated carbons are inexpensive and appeared to be effective and can be explore for future commercial application for environmental sustainability.

The lack of adequate sanitation in Brazil overloads the health system and causes deaths. The utilization of sewage sludge hinders advancements in water treatment. This study aimed to assess the feasibility of producing briquettes by blending sewage sludge with Pinus sp. sawdust. The sewage sludge was sourced from a water treatment facility, while the Pinus sp. sawdust was obtained from a sawmill. Elemental analysis, proximate analysis, and calorific value were evaluated for both biomasses. Briquettes were manufactured using a hydraulic press, varying the proportion of sewage sludge from 0% to 95%, followed by thermogravimetric analysis. Pinus sp. sawdust exhibited higher carbon, oxygen, and hydrogen content, whereas sewage sludge contained more nitrogen and sulfur. The sawdust had greater fixed carbon content, volatile matter, and calorific value, while the sewage sludge had higher ash content. Samples with higher sewage sludge content showed better thermal resistance, with 100% sewage sludge retaining 63.3% mass after exposure to 950 °C. Briquettes with higher sewage sludge content had increased energy density. Considering the elevated nitrogen, sulfur, and ash content, sewage sludge should be limited to 5% in briquette production with Pinus sp. sawdust. This research underscores a significant avenue for sewage sludge utilization and sustainable bioenergy production.

Statistical models were developed that predict male and female femur geometry as functions of age, body mass index (BMI), and femur length as part of an effort to develop lower-extremity finite element models with geometries that are parametric with subject characteristics. The process for developing these models involved extracting femur geometry from clinical CT scans of 62 men and 36 women, fitting a template finite element femur mesh to the surface geometry of each patient, and then programmatically determining thickness at each nodal location. Principal component analysis was then performed on the thickness and geometry nodal coordinates, and linear regression models were developed to predict principal component scores as functions of age, BMI, and femur length. The average absolute errors in male and female external surface geometry model predictions were 4.57 and 4.23mm, and the average absolute errors in male and female thickness model predictions were 1.67 and 1.74mm. The average error in midshaft cortical bone areas between the predicted geometries and the patient geometries was 4.4%. The average error in cortical bone area between the predicted geometries and a validation set of cadaver femur geometries across 5 shaft locations was 2.9%.