Estimation of gross calorific value based on coal analysis using regression and artificial neural networks

Mesroghli, Sh.; Jorjani, E.; Chelgani, S. Chehreh

doi:10.1016/j.coal.2009.04.002

Cited by 110 publications

(41 citation statements)

References 16 publications

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“…In addition, Table 6, which presents the deviations of the ANFIS model predictions from targets values, shows that the errors and deviations from experimentally calculated GCVs in ANFIS models are less than those produced by regression models. Although Mesroghli et al (2009) reported that artificial neural network is not better or very different from regression results when the proximate and ultimate analyses are the GCV predictors. However, in the current work, a suitable, structured ANFIS model predicted GCV with a high precision that has not been reported in previous published works.…”

Section: Technical Considerationsmentioning

confidence: 99%

“…They found that the input set of moisture, ash, volatile matter, fixed carbon, carbon, hydrogen, sulfur, and nitrogen yielded the best prediction and generalization accuracy. Mesroghli et al (2009) investigated the relationships of ultimate analysis and proximate analysis with GCV of U.S. coal samples by regression analysis and artificial neural network methods. The input set of C, H exclusive of moisture (H ex) , N, O exclusive of moisture (O ex ), S, moisture, and ash was found to be the best predictor.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Neural Networks -Industrial and Control Engineering Applications 170 1991; Chen et al, 1991;Wasserman, 1993;Chehreh Chelgani et al, 2008;Hansen and Meservy, 1996;Patel et al, 2007;Mesroghli et al, 2009;Bagherieh et al, 2008;Jorjani et al, 2008;Chehreh Chelgani et al, 2010;Khandelwal and Singh, 2010 ;Sahu et al, 2010;Yao et al, 2005;Patel et al, 2007;Salehfar and Benson, 1998;Wu et al, 2008;Karacan, 2007). Patel et al (2007) predicted the GCV of coal utilizing 79 sets of data using neural network analyses based on proximate analysis, ultimate analysis, and the density of helium.…”

Section: Introductionmentioning

confidence: 99%

“…It can be determined experimentally using a calorimeter. Many equations have been developed for the estimation of gross calorific value (GCV) based on proximate analysis and/or ultimate analysis (Mason and Gandhi, 1983;Mesroghli et al, 2009;Given et al, 1986;Parikh et al, 2005;Custer, 1951;Spooner, 1951;Mazumdar, 1954;Channiwala and Parikh, 2002;Majumder et al, 2008). Regression analyses and data for 775 U.S. coal samples (with less than 30% dry ash) were used by Mason and Gandhi (1983) to develop an empirical equation that estimates the calorific value (CV) of coal based on its C, H, S, and ash contents (all on dry basis).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

Rafezi¹,

Jorjani²,

Karimi³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Section: Technical Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

Rafezi¹,

Jorjani²,

Karimi³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

Self Cite

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“…Mesroghli et al [32] used the regression and artificial neural networks for estimating gross calorific value based on coal analysis. Sahoo et al [33] developed the models for predicting stream water temperature, using three techniques, namely the regression analysis, an artificial neural network, and also combining them with chaotic non-linear dynamic models.…”

Section: Introductionmentioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

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Abstract:Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.

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Prediction of Coal Calorific Value Based on a Hybrid Linear Regression and Support Vector Machine Model

Sun

Zhou

2013

Proceedings of the Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

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The gross calorific value (GCV) is an important property defining the efficiency of coal. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear, which has made artificial intelligence models as a useful tool for a more accurate GCV prediction. This paper focuses on an innovative method of GCV prediction using combination of Multivariate Linear Regression (MLR) as predictor and Support Vector Machine (SVM) as an error correction tool based on proximate and ultimate analyses. The GCV have been predicted using the MLR, ANN and the hybrid MLR-SVM models. In the analysis root mean squared error have been employed to compare performances of the models. Results demonstrated that both models have good prediction ability; however the hybrid MLR-SVM has better accuracy.

show abstract

Estimation of gross calorific value based on coal analysis using regression and artificial neural networks

Cited by 110 publications

References 16 publications

Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

Prediction of Coal Calorific Value Based on a Hybrid Linear Regression and Support Vector Machine Model

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