The Experts below are selected from a list of 1668 Experts worldwide ranked by ideXlab platform
Chehreh S Chelgani - One of the best experts on this subject based on the ideXlab platform.
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modeling of Gross Calorific Value based on coal properties by support vector regression method
Modeling Earth Systems and Environment, 2017Co-Authors: Esmaeil Hadavandi, James C Hower, Chehreh S ChelganiAbstract:Gross Calorific Value (GCV) is one the most important coal combustion parameters for power plants. Modeling of GCV based on coal properties could be a key for estimating the amount of coal consumption in the combustion system of various plants. In this study, support vector regression (SVR) as a powerful prediction method has been used to investigate relationships among coal sample properties with their GCVs for a wide range of records. Variable importance measurement by the SVR method throughout various coal analyses (proximate, ultimate, different sulfur types, and petrography) indicated that carbon, ash, moisture, and hydrogen contents are the most effective variables for the GCV prediction. Two models based on all variables and four the most effective ones are conducted. Outputs in the testing stage of both models verified that SVR can predict GCV quite satisfactorily where the correlations of determination (R2) for models was 0.99. Based on these results, development of a variable selection system among wide range of parameters, and also application of an accurate predictive model such as SVR, can potentially be further employed as a reliable tool for evaluation of complex relationships in earth and energy problems.
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estimation of coal Gross Calorific Value based on various analyses by random forest method
Fuel, 2016Co-Authors: S S Matin, Chehreh S ChelganiAbstract:Abstract The last decade has witnessed of increasing the application of random forest (RF) models that are known as an exhibit good practical performance, especially in high-dimensional settings. However, on the theoretical side, their predictive ability markedly remains unexplained, especially in coal preparation. RF as a predictive model can tend to work well with large dimensional databases and rank predictors through its inbuilt variable importance measures. In this study, relationships among ultimate and proximate analyses of 6339 US coal samples from 26 states with Gross Calorific Value (GCV) have been investigated by multivariable regression (MVR) and random forest (RF) models. RF method has been used for the variable importance. Models have shown that the ultimate analysis parameters are the most suitable estimators for GCV and that RF can predict GCV quite satisfactory. Running of the best arranged RF structures for the input sets and assessment of errors have suggested that RF models are suitable for complicated relationships.
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estimation of Gross Calorific Value based on coal analysis using regression and artificial neural networks
International Journal of Coal Geology, 2009Co-Authors: Sh Mesroghli, E Jorjani, Chehreh S ChelganiAbstract:Abstract Relationships of ultimate and proximate analysis of 4540 US coal samples from 25 states with Gross Calorific Value (GCV) have been investigated by regression and artificial neural networks (ANNs) methods. Three set of inputs: (a) volatile matter, ash and moisture (b) C, H, N, O, S and ash (c) C, H exclusive of moisture, N, O exclusive of moisture, S, moisture and ash were used for the prediction of GCV by regression and ANNs. The multivariable regression studies have shown that the model (c) is the most suitable estimator of GCV. Running of the best arranged ANNs structures for the models (a) to (c) and assessment of errors have shown that the ANNs are not better or much different from regression, as a common and understood technique, in the prediction of uncomplicated relationships between proximate and ultimate analysis and coal GCV.
P A Tarantili - One of the best experts on this subject based on the ideXlab platform.
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evaluation of thermal degradation mechanisms and their effect on the Gross Calorific Value of abs pc organoclay nanocomposites
Journal of Thermal Analysis and Calorimetry, 2015Co-Authors: Marianna I Triantou, E M Chatzigiannakis, P A TarantiliAbstract:In this work, nanocomposites of ABS/PC blends reinforced with organically modified montmorillonite (OMMT) were prepared by melt blending in a twin screw extruder, and an assessment of the thermal degradation mechanisms was performed by thermogravimetric analysis. The incorporation of PC improves the thermal resistance of ABS/PC blends, with respect to pure ABS. The addition of OMMT alters the degradation mechanism and modifies the properties of blends with higher PC content, where an increase of the degradation temperature corresponding to PC becomes obvious, in comparison with the respective unreinforced blends. The Gross Calorific Value was calculated using an oxygen bomb calorimeter, and in most of the examined nanocomposites, an inverse trend was observed between this property and the residue calculated after thermogravimetric analysis in inert atmosphere. Based on the above results, the thermal degradation behavior of ABS/PC nanocomposites was interpreted by the heat barrier effect, caused via the formation of a carbonaceous silicate char, which insulates the underlying material creating a protective barrier to heat and mass transfer.
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Evaluation of thermal degradation mechanisms and their effect on the Gross Calorific Value of ABS/PC/organoclay nanocomposites
Journal of Thermal Analysis and Calorimetry, 2014Co-Authors: Marianna I Triantou, E M Chatzigiannakis, P A TarantiliAbstract:In this work, nanocomposites of ABS/PC blends reinforced with organically modified montmorillonite (OMMT) were prepared by melt blending in a twin screw extruder, and an assessment of the thermal degradation mechanisms was performed by thermogravimetric analysis. The incorporation of PC improves the thermal resistance of ABS/PC blends, with respect to pure ABS. The addition of OMMT alters the degradation mechanism and modifies the properties of blends with higher PC content, where an increase of the degradation temperature corresponding to PC becomes obvious, in comparison with the respective unreinforced blends. The Gross Calorific Value was calculated using an oxygen bomb calorimeter, and in most of the examined nanocomposites, an inverse trend was observed between this property and the residue calculated after thermogravimetric analysis in inert atmosphere. Based on the above results, the thermal degradation behavior of ABS/PC nanocomposites was interpreted by the heat barrier effect, caused via the formation of a carbonaceous silicate char, which insulates the underlying material creating a protective barrier to heat and mass transfer.
Marianna I Triantou - One of the best experts on this subject based on the ideXlab platform.
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evaluation of thermal degradation mechanisms and their effect on the Gross Calorific Value of abs pc organoclay nanocomposites
Journal of Thermal Analysis and Calorimetry, 2015Co-Authors: Marianna I Triantou, E M Chatzigiannakis, P A TarantiliAbstract:In this work, nanocomposites of ABS/PC blends reinforced with organically modified montmorillonite (OMMT) were prepared by melt blending in a twin screw extruder, and an assessment of the thermal degradation mechanisms was performed by thermogravimetric analysis. The incorporation of PC improves the thermal resistance of ABS/PC blends, with respect to pure ABS. The addition of OMMT alters the degradation mechanism and modifies the properties of blends with higher PC content, where an increase of the degradation temperature corresponding to PC becomes obvious, in comparison with the respective unreinforced blends. The Gross Calorific Value was calculated using an oxygen bomb calorimeter, and in most of the examined nanocomposites, an inverse trend was observed between this property and the residue calculated after thermogravimetric analysis in inert atmosphere. Based on the above results, the thermal degradation behavior of ABS/PC nanocomposites was interpreted by the heat barrier effect, caused via the formation of a carbonaceous silicate char, which insulates the underlying material creating a protective barrier to heat and mass transfer.
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Evaluation of thermal degradation mechanisms and their effect on the Gross Calorific Value of ABS/PC/organoclay nanocomposites
Journal of Thermal Analysis and Calorimetry, 2014Co-Authors: Marianna I Triantou, E M Chatzigiannakis, P A TarantiliAbstract:In this work, nanocomposites of ABS/PC blends reinforced with organically modified montmorillonite (OMMT) were prepared by melt blending in a twin screw extruder, and an assessment of the thermal degradation mechanisms was performed by thermogravimetric analysis. The incorporation of PC improves the thermal resistance of ABS/PC blends, with respect to pure ABS. The addition of OMMT alters the degradation mechanism and modifies the properties of blends with higher PC content, where an increase of the degradation temperature corresponding to PC becomes obvious, in comparison with the respective unreinforced blends. The Gross Calorific Value was calculated using an oxygen bomb calorimeter, and in most of the examined nanocomposites, an inverse trend was observed between this property and the residue calculated after thermogravimetric analysis in inert atmosphere. Based on the above results, the thermal degradation behavior of ABS/PC nanocomposites was interpreted by the heat barrier effect, caused via the formation of a carbonaceous silicate char, which insulates the underlying material creating a protective barrier to heat and mass transfer.
Jidong Lu - One of the best experts on this subject based on the ideXlab platform.
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feasibility study of Gross Calorific Value carbon content volatile matter content and ash content of solid biomass fuel using laser induced breakdown spectroscopy
Fuel, 2019Co-Authors: Zhimin Lu, Xiaoxuan Chen, Lifeng Zhang, Ziyu Yu, Jidong LuAbstract:Abstract Rapid determination of the solid biomass fuel properties is essential for optimizing the combustion process of biomass. In this work, a feasibility study on using laser-induced breakdown spectroscopy (LIBS) in conjunction with partial least squares (PLS) for simultaneous measurement of Gross Calorific Value, carbon content, volatile matter content and ash content was carried out for 66 wood pellet samples. The best quantitative analysis results were obtained with the PLS model based on spectra that combined baseline correction with Z-score standardization. The root mean square error of prediction (RMSEP) of the Gross Calorific Value, carbon content, volatile matter content and ash content were 0.33 MJ/kg, 0.65%, 1.11% and 0.38% respectively, while the average standard deviation (ASD) were 0.08 MJ/kg, 0.15%, 0.43% and 0.16% respectively.
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rapid determination of the Gross Calorific Value of coal using laser induced breakdown spectroscopy coupled with artificial neural networks and genetic algorithm
Energy & Fuels, 2017Co-Authors: Zhimin Lu, Juehui Mo, Jingbo Zhao, Jidong LuAbstract:Online measurement for the Gross Calorific Value (GCV) of coal is important in the coal utilization industry. This paper proposed a rapid GCV determination method that combined a laser-induced breakdown spectroscopy (LIBS) technique with artificial neural networks (ANNs) and genetic algorithm (GA). Input variables were selected according to the physical mechanism and mathematical significance to improve the prediction of the ANN. GA was applied to determine an optimal architecture for the network instead of a trial and error method. As a result, the mean standard deviation (MSD) of the GCV for four prediction set samples is 0.38 MJ/kg in 50 trials (repetitions of training the ANN with the same input data but different random initial weights and biases), proving that the ANN model is able to provide high modeling repeatability in the GCV analysis. The mean absolute error (MAE) of the GCV for the prediction set is 0.39 MJ/kg. The result meets the requirements (0.8 MJ/kg) for coal online analyses using the n...
David A. Wood - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity analysis and optimization capabilities of the transparent open-box learning network in predicting coal Gross Calorific Value from underlying compositional variables
Modeling Earth Systems and Environment, 2019Co-Authors: David A. WoodAbstract:Prediction performance and optimization attributes of the transparent open-box learning networks (TOB) applied to a large database of US coals are further explored to complement recently published base-case analysis. Nine sensitivity cases configured with the 6339 data records allocated in different ways to the TOB’s tuning, training, and testing subsets are developed. These cases demonstrate for this data set that the TOB algorithm provides robust, reliable, and repeatable predictions provided that the tuning subset contains 40 or so data records. On the other hand, increasing the number of records in the testing subset to numbers much greater than 100 does not lead to improved prediction accuracy. A comparison of the prediction performance of three optimizers applied with the TOB for each of the sensitivity cases reveals that a memetic firefly optimizer matches the optimized solutions found by Excel’s GRG Solver optimizer. The functionality of the memetic firefly optimizer enables it to be used effectively in a fully coded version of the TOB optimizer (not involving Excel cell formula for the optimizer to operate). This is an advantage when evaluating larger data sets with larger tuning subset requirements. The memetic firefly optimizer also introduces further transparency, flexibility, and control to the TOB optimization process by facilitating metaheuristic profiling that aids the tuning and customization of the optimizer for deployment with a range of data sets involving complex, non-linear feasible-solution spaces.
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Transparent open-box learning network provides auditable predictions for coal Gross Calorific Value
Modeling Earth Systems and Environment, 2019Co-Authors: David A. WoodAbstract:Auditing and forensic analysis of how each prediction is calculated are key attributes of transparent open-box learning networks (TOB). It provides the full calculation and input metric contributions for each of the predictions it derives. There are two stages in executing TOB predictions (stage 1 matches and ranks using squared-error analysis; stage 2 optimizes and conducts sensitivity analysis). Neither stage involves generating or extrapolating correlations between the input variables. Both stages of the calculation generate accurate predictions for datasets with multiple, highly-dispersed and non-linear influencing inputs. The transparent way in which generates predictions leads to better understanding of the interplays between the input variables. Such attributes have direct relevance to the complex systems modelled in the coal industry [e.g., gas Calorific Value (GCV) prediction and coal petrology–grindability relationships]. The algorithm is applied here to predict GCA for a large published database (6339 records) of US coals including proximate and ultimate analysis metrics. The TOB predicts GCV with accuracy (RMSE ≤ 0.3 MJ/kg; R^2 > 0.99). The transparency of the TOB method contrasts with the hidden relationships involved in many neural-network based prediction systems. Worked examples are provided to show the detailed prediction calculations associated with individual data points. The TOB approach applied to predicting coal GCV can help to verify the source of specific samples (e.g. specific mines or coal basins) using readily understandable underlying calculations available for audit and display. The TOB is therefore also suitable for identifying the provenance of specific coal samples based on proximate and/or ultimate analysis.
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predictions of Gross Calorific Value of indian coals from their moisture and ash content
Journal of The Geological Society of India, 2019Co-Authors: Priya Kumari, David A. Wood, Ashok K Singh, Bodhisatwa HazraAbstract:India, the world’s third largest coal producing country, is expected to maintain strong dependency on coal for the coming few decades. The Gross Calorific Value (GCV) of coal is a key yardstick for many end users purchasing coal from suppliers, as it provides a clear measure of the useful energy content of a coal. Several methods are available to predict GCV using either moisture content and/or ash yield or using entire proximate and ultimate coal analysis data. These correlations and machine learning prediction tools tend to be too complex and hence are of limited utility for the end-user. For Indian coals, the most widely used simple correlation, based only on moisture content and ash yield, proposed by Mazumdar (1954), is less applicable to the coals being mined today, which have higher ash yields. This and other existing correlations used to predict GCV of Indian coals from moisture content and ash yield is linear in nature. Here the application of non-unitary exponents to the ash yield and moisture content terms and introducing other non-linear terms to the equation to provide empirical correlations with improved prediction accuracy for local and national application is evaluated. This evaluation is achieved in Excel using its built-in optimizer “Solver” to evaluate a generic equation and optimize its coefficients when applied to a dataset of 756 coals from three Indian coal basins and to locally focused subsets of that dataset.
