scholarly journals Correlations to Predict Elemental Compositions and Heating Value of Torrefied Biomass

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2443 ◽  
Author(s):  
Mahmudul Hasan ◽  
Yousef Haseli ◽  
Ernur Karadogan
Keyword(s):  
Carbon Content ◽  
Hydrogen Content ◽  
Woody Biomass ◽  
Coefficient Of Determination ◽  
Mass Yield ◽  
Solid Mass ◽  
Empirical Correlations ◽  
Heating Value ◽  
Torrefied Biomass ◽  
Data Points

Measurements reported in the literature on ultimate analysis of various types of torrefied woody biomass, comprising 152 data points, have been compiled and empirical correlations are developed to predict the carbon content, hydrogen content, and heating value of a torrefied wood as a function of solid mass yield. The range of torrefaction temperature, residence time and solid yield of the collected data is 200–300 °C, 5–60 min and 58–97%, respectively. Two correlations are proposed for carbon content with a coefficient of determination ( R 2 ) of 81.52% and 89.86%, two for hydrogen content with R 2 of 79.01% and 88.45%, and one for higher heating value with R 2 of 92.80%. The root mean square error (RMSE) values of the proposed correlations are 0.037, 0.028, 0.059, 0.043 and 0.023, respectively. The predictability of the proposed relations is examined with an additional set of experimental data and compared with the existing correlations in the literature. The new correlations can be used as a useful tool when designing torrefaction plants, furnaces, or gasifiers operating on torrefied wood.

scholarly journals An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3992
Author(s):  
Nasriani ◽  
Khan ◽  
Graham ◽  
Ndlovu ◽  
Nasriani ◽  
...  
Keyword(s):  
Flow Rate ◽  
Parameter Variation ◽  
Gas Condensate ◽  
The Other ◽  
High Rate ◽  
Production Data ◽  
Empirical Correlations ◽  
New Correlation ◽  
Production Wells ◽  
Data Points

There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a small number of fields. Therefore, these correlations are valid within the parameter variation ranges of those fields. If such correlations are used elsewhere for the prediction of the subcritical choke flow performance of the other fields, significant errors will occur. Additionally, there are only a few empirical correlations for sub-critical choke flow performance in high rate gas condensate wells. These led the authors to develop a new empirical correlation based on a wider production data set from different gas condensate fields in the world; 234 production data points were collected from a large number of production wells in twenty different gas condensate fields with diverse reservoir conditions and different production histories. A non-linear regression analysis method was applied to their production. The new correlation was validated with a new set of data points from some other production wells to confirm the accuracy of the established correlation. The results show that the new correlation had minimal errors and predicted the gas flow rate more accurately than the other three existing models over a wider range of parameter variation ranges.

Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

Microwave Torrefaction of Moist Municipal Solid Waste (MSW) Pellets with the Addition of Char from Agricultural Residues

2017 ◽  
Vol 757 ◽  
pp. 156-160
Author(s):  
Prodpran Siritheerasas ◽  
Phichayanan Waiyanate ◽  
Hidetoshi Sekiguchi ◽  
Satoshi Kodama
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Bituminous Coal ◽  
Volatile Matter ◽  
Agricultural Residues ◽  
Microwave Oven ◽  
Microwave Absorber ◽  
Mass Yield ◽  
Heating Value ◽  
Microwave Absorbers

An investigation of the effect of the addition of char from agricultural residues on the torrefaction of moist municipal solid waste (MSW) pellets (40 wt.% moisture) was carried out in a microwave oven (500-800 W for 4-12 minutes). Char from agricultural residues, including corncob, palm shell, straw, and bagasse, was used as the microwave absorbers to enhance the absorption of microwave irradiation. It was found that the addition of char from bagasse yielded the lowest remaining mass (or mass yield) and volatile matter (VM) content, but the highest temperature and heating value, of the torrefied MSW pellet. Moisture in the MSW pellet with or without the addition of microwave absorber was completely removed after being torrefied for 8-12 minutes. The VM contents remained in the MSW pellets with the addition of microwave absorbers were lower than that in the MSW pellet without the addition of microwave absorber. The addition of microwave absorbers led to an increase in carbon (C) content but a decrease in oxygen (O) content of the torrefied MSW pellets, compared to those of the raw MSW pellet. The heating values of the torrefied MSW pellets with the addition of microwave absorbers were equivalent to that of sub-bituminous coal, enhanced from that of the raw MSW pellet, which was lower than that of lignite.

scholarly journals Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

2016 ◽  
Vol 56 (3) ◽  
pp. 258 ◽  
Author(s):  
Amélie Vanlierde ◽  
Marie-Laure Vanrobays ◽  
Nicolas Gengler ◽  
Pierre Dardenne ◽  
Eric Froidmont ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
High Throughput ◽  
Methane Emissions ◽  
Calibration Coefficient ◽  
Coefficient Of Determination ◽  
Mid Infrared ◽  
First Derivative ◽  
Data Points ◽  
Mir Spectra

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions.

Zmiany parametrów mieszaniny gazu ziemnego z wodorem w trakcie eksploatacji komory magazynowej w kawernie solnej

Nafta-Gaz ◽  
2020 ◽  
Vol 76 (11) ◽  
pp. 799-806
Author(s):  
Paweł Budak ◽  
◽  
Tadeusz Szpunar ◽  
Keyword(s):  
Hydrogen Content ◽  
Distribution Network ◽  
Calorific Value ◽  
Gas Reservoirs ◽  
Heating Value ◽  
Salt Domes ◽  
Depleted Gas Reservoirs ◽  
Salt Caverns ◽  
Short Time ◽  
Water Gas

Underground gas stores are built in depleted gas reservoirs or in salt domes or salt caverns. In the case of salt caverns, the store space for gas is created by leaching the salt using water. Gas stores in salt caverns are capable to provide the distribution network with large volumes of gas in a short time and cover the peak demand for gas. The salt caverns are also capable to store large volumes of gas in case when there is too much gas on a market. Generally, the salt caverns are used to mitigate the fluctuation of gas demand, specifically during winter. The gas provided to the distribution network must satisfy the requirements regarding its heating value, calorific value, volumetric content of hydrogen and the Wobbe number. Large hydrogen content reduces the calorific value as well as the heating value of gas and thus its content must be regulated to keep these values at the acceptable level. One should also remember that every portion of gas which was used to create the gas/hydrogen mixture may have different parameters (heating value and calorific value) because it may come from different sources. The conclusion is that the hydrogen content and the heating value must be known at every moment of gas store exploitation. The paper presents an algorithm and a computer program which may be used to calculate the hydrogen content (volumetric percentage), heating value and calorific value (plus the Wobbe number) of gas collected from the salt cavern at every moment of cavern exploitation. The possibility of the presence of non-flammable components in the mixture and their effect on the heat of combustion / calorific value were considered. An exemplary calculation is provided.

scholarly journals Systematically Exploring Associations among Multivariate Data

2020 ◽  
Vol 34 (04) ◽  
pp. 6786-6794
Author(s):  
Lifeng Zhang
Keyword(s):  
Interaction Effect ◽  
Functional Relationship ◽  
Multivariate Data ◽  
Coefficient Of Determination ◽  
High Dimensional ◽  
Data Sets ◽  
Statistical Tool ◽  
Wide Range ◽  
Main Effect ◽  
Data Points

Detecting relationships among multivariate data is often of great importance in the analysis of high-dimensional data sets, and has received growing attention for decades from both academic and industrial fields. In this study, we propose a statistical tool named the neighbor correlation coefficient (nCor), which is based on a new idea that measures the local continuity of the reordered data points to quantify the strength of the global association between variables. With sufficient sample size, the new method is able to capture a wide range of functional relationship, whether it is linear or nonlinear, bivariate or multivariate, main effect or interaction. The score of nCor roughly approximates the coefficient of determination (R2) of the data which implies the proportion of variance in one variable that is predictable from one or more other variables. On this basis, three nCor based statistics are also proposed here to further characterize the intra and inter structures of the associations from the aspects of nonlinearity, interaction effect, and variable redundancy. The mechanisms of these measures are proved in theory and demonstrated with numerical analyses.

scholarly journals Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Osama Siddig ◽  
Hany Gamal ◽  
Pantelis Soupios ◽  
Salaheldin Elkatatny
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Total Organic Carbon Content ◽  
Functional Network ◽  
Organic Carbon Content ◽  
Empirical Correlations ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy

Abstract This paper presents the application of two artificial intelligence (AI) approaches in the prediction of total organic carbon content (TOC) in Devonian Duvernay shale. To develop and test the models, around 1250 data points from three wells were used. Each point comprises TOC value with corresponding spectral and conventional well logs. The tested AI techniques are adaptive neuro-fuzzy interference system (ANFIS) and functional network (FN) which their predictions are compared to existing empirical correlations. Out of these two methods, ANFIS yielded the best outcomes with 0.98, 0.90, and 0.95 correlation coefficients (R) in training, testing, and validation respectively, and the average errors ranged between 7 and 18%. In contrast, the empirical correlations resulted in R values less than 0.85 and average errors greater than 20%. Out of eight inputs, gamma ray was found to have the most significant impact on TOC prediction. In comparison to the experimental procedures, AI-based models produces continuous TOC profiles with good prediction accuracy. The intelligent models are developed from preexisting data which saves time and costs. Article highlights In contrast to existing empirical correlation, the AI-based models yielded more accurate TOC predictions. Out of the two AI methods used in this article, ANFIS generated the best estimations in all datasets that have been tested. The reported outcomes show the reliability of the presented models to determine TOC for Devonian shale.

scholarly journals Impact of Pretreatment on Hydrothermally Carbonized Spruce

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2984
Author(s):  
Anna Partridge ◽  
Ekaterina Sermyagina ◽  
Esa Vakkilainen
Keyword(s):  
Treated Wood ◽  
Hydrothermal Carbonization ◽  
Proximate Analysis ◽  
Thermochemical Conversion ◽  
Energy Yield ◽  
Strongly Correlated ◽  
Mass Yield ◽  
Biomass Waste ◽  
Heating Value ◽  
Lignin Extraction

Upgrading biomass waste streams can improve economics in wood industries by adding value to the process. This work considers use of a hydrothermal carbonization (HTC) process for the residual feedstock after lignin and hemicelluloses extraction. Batch experiments were performed at 200–240 °C temperatures and three hours residence time with an 8:1 biomass to water ratio for two feedstocks: Raw spruce and spruce after lignin extraction. The proximate analysis and heating value showed similar results for both feedstocks, indicating that the thermochemical conversion is not impacted by the removal of lignin and hemicelluloses; the pretreatment processing slightly increases the heating value of the treated feedstock, but the HTC conversion process produces a consistent upgrading trend for both the treated and untreated feedstocks. The energy yield was 9.7 percentage points higher for the treated wood on average across the range temperatures due to the higher mass yield in the treated experiments. The energy densification ratio and the mass yield were strongly correlated with reaction temperature, while the energy yield was not. Lignocellulosic composition of the solid HTC product is mainly affected by HTC treatment, the effect of lignin extraction is negligible.

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