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Prediction of Coal Proximate Parameters and Useful Heat Value of Coal from Well logs of Bishrampur Coalfield, India using Regression and Artificial Neural Network Modeling Sayan Ghosh, Rima Chatterjee, and Prabhat Shanker Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01259 • Publication Date (Web): 03 Aug 2016 Downloaded from http://pubs.acs.org on August 10, 2016

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Title Page

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Prediction of Coal Proximate Parameters and Useful Heat Value of

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Coal from Well logs of Bishrampur Coalfield, India using Regression

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and Artificial Neural Network Modeling

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Sayan Ghosh1, Rima Chatterjee*2 and Prabhat Shanker1

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1

Central Mine Planning and Design Institute Limited, Bilaspur, India

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Department of Applied Geophysics, Indian School of Mines, Dhanbad, India

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*Corresponding Author: Rima Chatterjee

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Professor

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Dept. of Applied Geophysics

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Indian School of Mines, Dhanbad – 826004

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India

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Email: [email protected]

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Sayan Ghosh: Email : [email protected]

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Prediction of Coal Proximate Parameters and Useful Heat Value of

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Coal from Well logs of Bishrampur Coalfield, India using Regression

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and Artificial Neural Network Modeling

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Sayan Ghosh1, Rima Chatterjee2 and Prabhat Shanker1

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1

Central Mine Planning and Design Institute Limited, Bilaspur, India

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2

Department of Applied Geophysics, Indian School of Mines, Dhanbad, India

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Abstract

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The energy of coal is expressed by its Useful Heat value (UHV) and it is the major key player

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in coal pricing. Objectives of this paper are to obtain (a) regression relationship between coal

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proximate parameters and UHV, and (b) multi-layered feed forward neural network (MLFN)

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models between geophysical log responses and UHV. Six wells are used for training the

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networks and three wells are used for validating the obtained results in Bishrampur Coalfield.

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Mean Square Error (MSE) of MLFN models along with correlation (R2) values at their training,

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validation and testing stages are the criteria for selecting best model for estimation of coal

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proximate parameters and UHV values using geophysical log responses. Final model is

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selected based on low MSE (≤0.07) and high R2 values (≥0.80) at training, validation and

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testing stages. The predicted UHV obtained from best MLFN model has excellent correlation

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(R2 = 0.98) with the laboratory determined UHV of three major coal seams. The predicted

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UHV is further implemented to grade the three coal seams of this coalfield.

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Keywords: Coal Proximate Parameters, Well Log, Multiple Regression analysis, MLFN

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model, UHV.

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1. Introduction

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The quality of coal depends on its chemical composition and heat value. The measure of the

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amount of energy while burning coal is known as calorific value or heating value. The rank of

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coal is believed to be a function of its elemental composition, maceral composition and

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mineral composition [1]. There are two measures of describing the coal grades: Gross

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Calorific value (GCV) and Net Calorific Value (NCV).Gross Calorific Value (GCV) is the heat

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units liberated at constant volume when a unit weight of the fuel is burnt at constant volume in

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oxygen, saturated with water vapour the original material and final products are at 25°C

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whereas the Net Calorific Value (NCV) is the heat units liberated at constant volume in oxygen

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saturated with water vapour less the heat of any steam below 100°C.

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Proximate and ultimate analyses of coal samples are the two methods of obtaining GCV and

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NCV. The proximate analysis of coal samples determines the relative amounts of ash,

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moisture content, volatile matter and fixed carbon whereas ultimate analysis is used to

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determine the chemical constituents of coal samples: carbon, hydrogen, oxygen, sulphur and

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other elements. Many regression equations and nonlinear models including artificial neural

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network method have been developed for predicting the calorific values of a coal sample

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based on proximate and ultimate analyses [1,2,3].

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The proximate parameters and Useful Heat Values (UHVs) are obtained from the

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conventional analysis of the coal core samples. Statistical and neural network methods are

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introduced by the researchers for estimation of proximate parameters from geophysical logs

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[2,3]. Previously authors [4] had indicated that the coal seams in Bishrampur coalfield are of

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high moisture content and of banded type. The overall quality of an Indian non-coking coal

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seam is determined from proximate analysis and UHV of coal samples obtained from existing

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bands in that seam. The energy content of non-coking Indian coal is commonly expressed in

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terms of UHV. UHV (Qh) is used for grading and pricing of coals and a simple equation as


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given below had been developed by the Central Institute of Mining and Fuel Research

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(CIMFR), Dhanbad, India [5,6]:

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Qh = 4.184[8900 – 138(Ca +Cm)] in MJ/kg ...................................... (1), where Ca and Cm

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refers the overall ash% and moisture content% respectively of coal samples at an

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environment of 40°C and 60% relative humidity (RH).

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Moreover, the proximate parameters (as received basis) are also related to the Gross Calorific

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Values (GCV in MJ/kg) [6]:

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GCV = -0.03*A – (0.11xM) + (0.33xVM) + (0.35xFC)NNNNNNN. (2a), where A, M,

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VM and FC are ash, moisture, volatile matter and fixed carbon.

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The units specified in equation 1 (MJ/kg) can be converted to the unit kCal/kg (used in this

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study) as:

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1 MJ/kg= 1 kCal/kg x 0.004187NNNN(2b)

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Previous studies had already suggested linear relationship between the geophysical log

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responses and the proximate parameters [4]. Here in this paper, the study focuses on (a)

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investigation on linear multiple regression relationship between coal proximate parameters

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and UHV as well as (b) development of non-linear multi-layered feed forward neural network

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(MLFN) models between geophysical log responses and UHV of coals from selected coal

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seams of Bishrampur Coalfield. The MLFN models are developed using software codes in

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Matrix Laboratory (MATLAB).

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2. Study area

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The present study is covering an area of about 1036 sq. km shown in Bishrampur Coalfield of

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Central India (Figure 1).

A generalised geological stratigraphy of Bishrampur coalfield is

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described in Table 1, where the coal seams belong to the Barakar formation. Three major

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coal seams namely; Dhejagir, Masan and Pasang of Barakar formation, Lower Permian age

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are the targets for the coal exploration in the study area showing a gentle dip of 2° to 3° [4,7].

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The coal seams in this coalfield usually occur within a depth range of 34.5 to 300.80m [4].

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The major seams are named as seam 1, 2, 3 and 4 in a sequence from bottom to top.

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The topmost major seam in this sequence under the study area is seam 4 with average 4 ACS Paragon Plus Environment

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thickness of 10m. The following seam 3 occurs at about 40 to 60m parting from seam 4 and

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the average thickness of the seam is 10m. Parting refers to the vertical distance between the

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floor of a seam and roof of the succeeding seam. The seam 2 is found beneath the previously

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said seam 3 at a parting of about 40 to 60m with an average thickness of 10-12m. Finally, the

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lowermost seam of the sequence (seam 1) with an average thickness of 2.5m occurs at a

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parting varying from 55 to 65m from seam 2 in this coalfield.

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A total nine numbers of wells are considered for analysis of the overall quality of the

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seams and UHVs from laboratory and geophysical logs. The nine wells is spread over four

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blocks of the coalfield namely; Bilara, Biharpur, Brijnagar and Gangapur which are at the final

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stage of exploration programme. These wells are mostly located at the central part of the

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coalfield, hence the results obtained from these wells may not be applicable for estimation of

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UHV for the entire coalfield. Coal seam correlation from these nine wells using natural

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gamma, density and single point resistance (SPR) logs is shown in Table 2. Major faults are

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not observed under the study area excepting minor faults with throw ranging from 30 to 40m.

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Identification of same seams is not difficult from the nine wells distributed in four blocks. The

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wells: B1 to B6 penetrating the major coal seams 1, 2, 3 and 4 have been considered as

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training wells for development of MLFN models. The rest three wells: B9, B12 and B13

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penetrating these same coal seams have been used as validation wells for prediction of coal

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quality and UHV of these seams. Typical log responses from wells B2 and B12 are illustrating

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the major seams 1, 2, 3, 4, local seam and uncorrelated seam (UC) (Figure 2a and 2b). The

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lithology of roof and floor of these seams is shown in Figure 2. The log data of well B2 and

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B12 shows the drop of density against major seams while SPR log does not show high

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resistivity throughout these seams. The seams are highly banded with non-coal (dirt) bands

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which usually, shale except seam 1. Non-coal (dirt) bands are showing the decrease of SPR

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within major seam. For example, seam 2 in well B2, consists of 4 dirt bands; log responses

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against seam 2 showing SPR value ranging from 44.25 ohm for dirt band to 345 ohm for coal

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(Figure 2c). The ranges of coal proximate parameters and the UHV of the coal seams in the

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nine wells are shown in Table 3.


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NNN.NNNNN..NNNN

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Figures 1, 2 Tables 1, 2 and 3

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NNN.NNNNN..NNNN

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3. Methodology

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The geophysical logs represent in-situ physical and radioactive properties of the formations

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whereas the overall proximate parameter of coal seam describes the quality and maturity.

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Although, the density log holds a linear relationship with ash% but the same does not happen

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with percentage of moisture content [8]. Previously; authors [2] observe a strong negative

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correlation between proximate parameters (like volatile matter%, fixed carbon %) and

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geophysical logs (like gamma ray, density and sonic logs) for coal seams in Southland Lignite

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Region, New Zealand. Few studies show that estimation of volatile matter and fixed carbon

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from well logs using neural network approach is better than the regression analysis [3].

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However, regression analysis is more effective in estimating the ash% and moisture%

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considering large numbers of samples or data.

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regression relationship among the coal proximate parameters with geophysical logs [1,2,4,11].

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Therefore, in this study we shall analyse the regression models between coal proximate

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parameters and UHV as well as develop MLFN models for prediction of UHV using well log

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responses.

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3.1. Multiple Regression Models

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The behaviour of overall coal proximate parameters viz., ash, moisture, volatile matter and

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fixed carbon with respect to UHV is the matter of interest in this model. Majumder et al. (2008)

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explain the negative impact of two proximate parameters such as: ash% and moisture% on

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UHV. The rest two proximate parameters (volatile matter% and fixed carbon%) shows a

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positive correlation with the UHV [6]. For purposes of regression modelling two combinations

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of proximate parameters are considered for predicting UHV. Table 4(a) is showing the two

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combinations of predicting UHV with 37 samples for combination 1 (COMB 1) and 19 samples

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for combination 2 (COMB 2) respectively. Analysis of variance (ANOVA), a technique

Researchers have established a strong


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analyzes the relationship between a dependent variable and two independent variables using

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MATLAB. The independent variables with two proximate parameters are two different groups

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in this analysis. The ANOVA one-way approach has been adopted with group means mi = 1, 2

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and variance.This two combinations each with two of the proximate parameters as the

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independent variables are considered for the regression analysis whereas UHV (in kCal/kg) is

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considered as the dependent variable. The quality of fit is evaluated by R2 values, standard

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error, probability significance and the F Statistic which is the ratio of the Mean Square Model

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(MSM) and MSE (Mean Square Error). The null hypothesis for ANOVA signifies m1 = m2 = 0,

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and the alternative hypothesis contradicts specifying that at least one of the parameters mj =

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0, j = 1, 2. [4,9]. Model summary and coefficients of regression analysis for two combinations

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are listed in Table 4(b). COMB 1 feeds ash and moisture as the independent parameters

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whereas COMB 2 feeds volatile matter and fixed carbon as independent parameters for

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correlating with the UHV values. The numbers of samples or the number of observation in the

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COMB 2 is relatively lesser then COMB 1 due to non-availability of data for all samples. As

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shown in Table 4(b), both combinations; COMB 1 and COMB 2, indicate the value of F is

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more than Fcritical ,leading to rejection of null hypothesis.

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The predicted UHV are obtained from equation (3a) for COMB 1 and equation (3b) for COMB

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2 regression models respectively.

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UHV (kCal/kg)= 100.25 – 2.86xM – 1.39xA ; R2= 0.89 NNNNNNNNNN(3a)

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UHV (kCal/kg)= -28.02 + 0.8xVM + 1.26xFC ; R2= 0.85 NNNNNNNNNN(3b)

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where A, M, VM and FC stand for ash%, moisture%, volatile matter% and fixed

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carbon% in 40°C and 60% RH. The estimated UHV using the regression models COMB 1 and

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COMB 2 are validated with that obtained in the laboratory shown in Figures 3a, b respectively

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showing an R2 value of 0.98.

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------------------------

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Figures 3a, b

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-----------------------


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3.2. Multi-layered Feed Forward Neural Network (MLFN) Model

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Three geophysical log parameters: density (g/cc), natural gamma (cps) and single point

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resistance (Ω) are opted as inputs whereas the proximate parameters: ash%, moisture%,

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volatile matter%, fixed carbon% and UHV (kCal/kg) are considered as desired outputs/targets

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for the development of MLFN model.

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The designing of the MLFN models includes 13 combinations of input and output

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parameters as shown in Table 5. Out of these, 9 combinations utilize two input (geophysical)

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parameters whereas the 4 combinations utilize 3 input parameters. Seven models (C6 to

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C12) provide single output, three models (C1 to C3) generate 2 outputs and two models (C4

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and C13) predict 3 outputs. The model C5 has predicted five parameters out of these 13

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models. The performance of the network corresponding to each combination or model is also

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monitored using the correlation coefficient (R2) through training, validation and testing stages

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with the specified number of neurons in the hidden layer.

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NNNNN..NNNN

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Table 5

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NNNNN..NNNN

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3.3. Training of the MLFN models

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MLFN models constitute single hidden layer with an input and output layer linked by transfer

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functions [10,11]. The transfer functions used are hyperbolic tangent transfer function (tansig)

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as the connection between layers. The MLFN models are trained with the geophysical logs

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parameters as inputs and the coal proximate parameters and UHV as the desired targets

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using Levenberg-Marquardt based back propagation algorithm [12]. A general multilayer

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perceptron network architecture of j hidden neurons connected to r inputs through weights.

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The input layer of the designed MLFN models are linked with the hidden layers using

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hyperbolic tangent transfer function (tansig) which is defined by the equation (4) [10,11,12,13]:

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tansig(w)= f1=f(w)= 2/(1+e-2w) – 1 NNNNNN.(4).


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where w is the weighted sum of the inputs. Similarly, the pure linear transfer function (f2) is

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used as the transfer function from the hidden layer to the output layer.

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f2=f(z) = z............... (5) where z is the output of the hidden layer.

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The multilayer perceptron learning algorithm can be briefly explained as follows [10,11,12]

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a) The weights W11,1, W11,2,....,W1r,j are initialized with initial biases b1(1), b1(2),N b1(j) for

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the first transfer function whereas weights W21,1, W21,2,....,W2j,n with initial biases b2(1),

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b2(2),N b2(n) for the second transfer function.

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b) The inputs x1, x2, x3.....,xr (representing the geophysical log responses) are included for

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the hidden layer. The desired output (y) while training is represented by the vector d1,

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d2,.....,dn (representing the core analyzed proximate parameters and UHV) for the wells B1 to

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B6.

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c) The actual output is calculated by using the equation

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yk= f2(W2(f1(Σ(W1x)+b1))+b2) ......(6), k = 1 to n

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The weights and biases for the next epoch are updated according to the Levenberg-Marquardt

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algorithm [10,11,12]. The weights and biases in the network are updated in each epoch until

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the desired target is achieved. The MLFN reads the input and output values in the training

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data set (Table 5) and changes the value of the weighted links to reduce the difference

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between the predicted and observed values of proximate parameters and UHV. A complete

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cycle of forward–backward passes including weight updating in the data set is called an epoch

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or iteration [13].

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The output of the MLFN models for estimation of proximate parameters and UHV can be

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expressed as:

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y= f2(w2(f1(∑(w1x)+b1))+b2)NNNNNN(5)

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where w1

and b1 represents the weights and biases of the first transfer

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function and w2 and b2 represents the weights and biases of the second

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transfer functions.


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The actual outputs (proximate parameters and UHV) are estimated using the selected trained

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MLFN models represented by the equation (6).

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The performance of every model corresponding to the 13 combinations is evaluated with the

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specified number of neurons in the hidden layer in the three different stages viz., training,

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validation and test stages. Mean Square Error (MSE) [11] is evaluating the performance of the

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MLFN models for prediction of coal proximate parameters and UHV.

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The data set used for training of the MLFN models shown in Table 6 consist of three

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geophysical logs and set of coal proximate parameters (ash, moisture, volatile matter and

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fixed carbon) and UHV values from the six training wells.

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NNNNN..NNNN

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Table 6

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NNNNN..NNNN

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The objective here is to select the best model with maximum possible output parameters or

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targets. The combinations involving volatile matter and fixed carbon utilize 15 numbers of

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data, whereas the rest combinations incorporate 22 numbers of data. Performances of 13

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MLFN models along with correlation (R2) values at their training, validation and testing stages

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are the criteria for selecting best model for estimation of coal proximate parameters and UHV

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values using geophysical log responses (Table 5). Final model is selected based on less MSE

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(≤0.07) and high R2 values (≥0.80) at training, validation and testing stages. For example, the

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model C9 involving three input log parameters estimating moisture content indicates MSE of

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0.0007 with R2 values of 0.74 at validation stage. Hence this model is not considered for

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moisture prediction. Using similar logic different models are selected for prediction of coal

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proximate parameters and UHV.

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An example of prediction of five outputs utilising three input log responses are

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demonstrated through model C5. MLFN model C5 predicts all the five parameters (ash,

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moisture, volatile matter, fixed carbon and UHV) with 5 hidden neurons with MSE of 0.02 with

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low R2 values in validation and testing stages. For MLFN model C5, all the five outputs are


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utilized in the network showing MSE of 0.02 at 100 epochs, shown in Table 5. The trained

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MLFN model C5 has been applied to estimate the proximate parameters and UHV values in

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the rest three wells B9, B12 and B13 by using all the three geophysical input parameters such

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as: density, natural gamma and single point resistance (SPR) for predicting proximate

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parameters and UHV.

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Further, the next example is looking for a combination with 3 output parameters and the three

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geophysical parameters as inputs. Hence, MLFN model C4 with all the three geophysical

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parameters as inputs and ash, moisture and UHV as outputs with 7 hidden neurons is chosen

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for prediction of UHV. The MSE value at epoch 100 reaches 0.00085 and the R2 values are

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0.99, 0.53 and 0.94 in the three stages respectively shown in Table 5. MLFN model C4

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resulting the three output parameters (ash, moisture and UHV) shows MSE of 0.00085 at 100

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epochs (Table 5). The MLFN model C4 has been used to estimate the ash, moisture content

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and UHV values in the rest three wells B9, B12 and B13 by using all the three geophysical

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input parameters (density, natural gamma and SPR) as indicated in Table 7.

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The models C1, C2 and C3 predict ash and UHV as shown in Table 5. The models

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C7, C8 and C9 predict moisture content only. The models C10, C11, C12 and C13 predict

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volatile matter. It is observed that the model C1 predicts ash and UHV well other the models

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C2 and C3 with high R2 values (Table 5). Similarly the models C7 and C11 are selected for

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predicting moisture content% and volatile matter% respectively with relatively higher R2 values

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using three geophysical input parameters. Fixed carbon content is estimated subtracting the

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total predicted ash, moisture and volatile matter content from 100%. Similarly, models C1, C7

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and C11 show MSE minimal values at 50 epoch.

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4. Results and Discussion

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Three seams namely 1, 2 and 3 are the major seams in the three testing wells. One local

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seam and a seam named 2A is intersected in well B13 between seam 2 and 1. Regression

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models COMB 1 and COMB 2 provide UHV using laboratory determined coal proximate data. 11 ACS Paragon Plus Environment

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MLFN models are developed a non-linear relation between geophysical log responses and

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laboratory test data (coal proximate parameters and UHV). Five MLFN models namely; C5,

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C4 and C1, C7, C11 have been chosen for predicting coal proximate parameters and UHV

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using three log responses for the three test wells.

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MLFN model C5, estimates the four proximate parameters (ash, moisture, volatile

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matter and fixed carbon) and the UHV values for total numbers of 14 seams selected from

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wells B9, B12 and B13 as shown in Table 7. The average error % for this model are 24.57,

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16.82, 15.90, 18.26 and 38.07 % for ash, moisture, volatile matter, fixed carbon and UHV

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respectively which are significantly higher and not to be considered. Thus, the MLFN model

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C5 is not suitable for estimating the output parameters.

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The next MLFN model C4 predicts ash, moisture and UHV as the three outputs

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parameters (Table 7) showing the average error of 6.02% for estimated moisture, relatively

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lower than the average percentage error predicted by model C5. The average errors of ash

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and UHV are 21.82% and 28.03% respectively indicating non acceptable value. Hence, MLFN

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model C4 is also inefficient for estimation of the output parameters.

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The combination of MLFN models: C1, C7 and C11 for estimating (ash, moisture,

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volatile matter, fixed carbon and UHV) as shown in Table 7 are indicating least average error

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among other models. These models estimate the five output parameters with average error%

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of 4.56, 6.17, 8.49, 12.25 and 5.68% respectively. It is observed that the MLFN model C1 for

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ash% and UHV, model C7 for moisture% and model C11 for volatile matter% exhibits the best

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performing results. Since, the networks are trained with the laboratory test data at 40⁰ C and

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60% RH, so the predicted results are validated with the laboratory measured data determined

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in the same environment as shown in Table 7. Hence, for estimating the proximate

325

parameters and the UHV values from well logs, MLFN models C1, C7 and C11 are finalized.

326

Further, the UHV estimated using the designed MLFN model C1 is validated with that

327

obtained in the laboratory in Figure 3c showing an R2 value of 0.98.

328

NNNNN..NNNN

329

Figure 3c


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Energy & Fuels

NNNNN..NNNN

330 331

4.1. Impacts of UHV on Coal Seam Grading

332

The grades of seam for Indian non-coking coal are usually based on two factors: UHV values

333

and the sum of ash% and moisture%. The grading of seam are analysed in each of the three

334

testing boreholes and the variation in the grades of each seam is observed among the three

335

boreholes. Basically three major seams namely; 1, 2 and 3 are encountered where the seams

336

3 and 2 further splits into 3/3; 3/2; 3/1 and 2/3; 2/2; 2/1 whereas the seam 1 is found to be

337

intact in all the wells.

338

The grade of a seam is dependent upon the UHV value and is classified as per the

339

Table 8 following standard norms of Indian non-coking coal [13]. The variations of the grades

340

depending upon the UHV predicted from MLFN model C1 are shown in Figure 4(a), (b) and

341

(c) for seam 3, 2 and 1 respectively by the predicted and laboratory estimated UHV values.

342

The variation in the grades of each split of individual seams are analysed among the testing

343

wells. Although, the laboratory analysed data for all the splits of the three major seams are not

344

available however the grades for each seam classified on the basis of the UHV are almost

345

consistent with a minor variation from one well to another well.

346

The seam 3 constitutes into three splits viz. 3/3, 3/2 and 3/1, where the split 3/2 is

347

encountered in all three wells and the rest two are encountered in two wells. The split 3/2

348

analysed in all the three wells shows a variation from F to G grade whereas 3/3 analysed in

349

well B09 is of grade G in both the predicted and analysed data. Split 3/1 in well B09 is

350

classified as grade D and E from predicted and analysed data respectively. The three splits of

351

seam 3 almost preserve the grades F to G except 3/1 which, exhibits a better quality grade D.

352

Seam 2 constitutes 3 splits viz. 2/3, 2/2 and 2/1 in the three testing wells where 2/3

353

and 2/2 is analysed in two of the wells and 2/1 is analysed in one well. The split 2/3 preserves

354

its grade E in both of the wells. The grade of split 2/2 declines to grade G but in the well B12,

355

the laboratory analysed UHV suggests the grade F for split 2/2. Moving onto split 2/1 which is

356

analysed in well B09, the UHV values predicted from MLFN models and estimated from


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

357

laboratory suggests grade F. Hence, seam 2 demonstrates grades E to G encountered in the

358

three wells.

359

Seam 1 is of better quality with relatively higher UHV values than seam 3 and 2. This

360

seam is analysed in all three testing wells B09, B12 and B13 and indicates grade from B to D

361

as per the predicted and laboratory analysed UHV values. The seam is of grade B in well B09

362

whereas this grade declines to D as observed in wells B12 and B13. Hence, seam 1 is of

363

higher quality not only as per the UHV values but also from the overall proximate analysis

364

values.

365

NNNNNNNNNNNNNNNN..................

366

Figure 4 and Table 8

367

NNNNNNNNNNNNNN..N.N...............

368

5. Conclusions

369

The methodology demonstrates the regression and MLFN models for prediction of UHV of

370

coal seams. MLFN model predicted UHV using log data as well as regression model predicted

371

UHV using laboratory tested coal proximate data are in excellent agreement with the

372

laboratory determined UHV from coal samples. Therefore, well log data calibrated with coal

373

proximate parameters can be used for UHV estimation using MLFN model. The quality and

374

grade of a seam is dependent upon the overall proximate parameters as well as on UHV.

375

This study focuses on the dependence of the UHV on the overall proximate parameters and

376

the geophysical log parameters. Multiple regression analysis is implemented for analysing the

377

best combination of independent proximate parameters with UHV as in equation 3. In

378

contrast, MLFN models are tested for obtaining the best model for predicting the coal quality

379

and UHV. The conventional method of determination of UHV and coal proximate parameters

380

is costly as well as time consuming. Regression analysis acts as the tool to identify the best fit

381

relationship among proximate parameters and the UHV whereas an alternative method such

382

as MLFN is approached to conclude the grades and quality parameters using the geophysical

383

logs as inputs. The code in MATLAB feeds the geophysical parameters as inputs and places

384

the quality parameters and UHV as desired target outputs for the limited training wells. The


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Energy & Fuels

385

code selects three best performing networks and uses the networks to predict the overall

386

quality parameters and UHV for the rest validating wells. This makes the job faster and

387

independent of the cost of the laboratory analysis for the validating wells. Hence, the

388

geophysical logs is utilized in the study area to enlighten the quality and grade of the seams

389

with an acceptable error% in a relatively faster method as compared to the time taking and

390

costlier conventional laboratory determined method.

391

Acknowledgments

392

The authors express their sincere gratitude to Mr. A. K. Debnath, Chairman and Managing

393

Director (CMPDI, Ranchi), Mr. S. Saran, Director (T/CRD) (CMPDI, Ranchi) and Mr. A. Das,

394

General Manager, Exploration (CMPDI, Ranchi) for their consistent motivation and

395

encouragement for completion of this task. The authors are also grateful to Mr. M. Kumar,

396

Regional Director; Mr A. K. Mohanty, HOD (Exploration) CMPDI RI-V, Bilaspur for their

397

contribution in improving the ideas regarding the work.

398

References

399

1. Mesroghli, Sh., Jorjani, E., Chehreh Chelgani, S., 2009. Estimation of gross

400

calorific value based on coal analysis using regression and artificial neural

401

networks. International Journal of Coal Geology 79, 49–54.

402 403

2. Kayal, J.R., Christoffel, D.A., 1989. Coal quality from Geophysical logs, Southland lignite region, New Zealand. The Log Analysts, 343-352.

404

3. Webber, T., Costa, J. F. C. L and Salvadoretti, P., 2013, Using borehole

405

geophysical data as soft information in indicator kriging for coal quality estimation,

406

International Journal of Coal Geology, 112, 67-75.

407

4. Ghosh, S., Chatterjee, R., Paul S. and Shanker, P., 2014, Designing of plug-in for

408

estimation of coal proximate parameters using statistical analysis and coal seam

409

correlation, Fuel, 134, 63-73.

410

5. Patel, S.U., Kumar, B.J., Badhe, Y.P., Sharma, B.K., Saha, S., Subhasish, B.,

411

Chaudhury, A., Tambe, S.S., Kulkarni, B.D., 2007. Estimation of gross calorific

412

value of coals using artificial neural networks, Fuel, 86, 334–344.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

413

6. Majumder, A.K., Jain, R., Banerjee, J.P., Barnwal, J.P., 2008. Development of a

414

new proximate analysis based correlation to predict calorific value of coal, Fuel 87,

415

3077–3081.

416 417

7. Ball, V., 1873. The Bishrampur Coalfield. Records Geological Survey of India, Vol. VI, Part-2.

418

8. Saghafi, A., Hatherly, P., Pinetown, K., 2011. Towards an optimal gas sampling

419

and estimation guideline for GHG emissions of open cut coal mines. Australian

420

Coal Research Limited, 41-50.

421 422 423 424

9. Koch Jr., G. S. and Link, R. F., 1970, Statistical analysis of Geological data, vol. 1, John Wiley & Sons, Inc., Newyork, USA, 1-375. 10. Demuth, H. and Beale, M., 2002, Neural Network Toolbox for Use with Matlab®, User’s Guide, Version 4. Mathworks Inc.

425

11. Ghosh, S., Chatterjee, R. and Shanker, P., 2016, Estimation of Ash, Moisture

426

Content and Detection of Coal Lithofacies from Well logs using Regression and

427

Artificial Neural Network Modelling, Fuel,177, 279-287.

428 429 430

12. Hagan, M. T. and Menhaj M. B., 1994, Training Feedforward Networks with the Marquardt Algorithm, IEEE Transaction on Neural Networks, 5(6), 989-993. 13.

Ghaffari, A., Abdollahi, H., Khoshayand, M. R., Bozchalooi, I. S., Dadgar, A.

431

and Rafiee-Tehrani, M., 2006, Performance comparison of neural network training

432

algorithms in modeling of bimodal drug delivery, International Journal of

433

Pharmaceutics, 327, 126–138.

434 435

14. Deb, T. K., 1984, Coal Grading and Pricing in Coal Mining in India, Report CMPDI, Ranchi, p.190.

436 437 438 439 440


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

441 442 443 444

Figure Captions

445

Figure 1: Showing well location under the study area of Bishrampur Coalfield. Inset is showing the

446

location of Bishrampur coalfield in India.

447 448

Figure 2: Illustrates typical log responses for major coal seams, local seams and uncorrelated

449

seams in (a) well B2 and (b) well B12.(c) Shows log signatures for seam 2 in well B2 with lithology.

450

Seam roof and floor are marked with lithology.

451 452

Figure 3: Regression model predicted UHV vs. Laboratory derived UHV using (a) equation (3a)

453

and (b) equation 3(b). (c) MLFN model predicted UHV vs. Laboratory derived UHV.

454 455

Figure 4: (a) The variation of the grades of seam 3 in the three testing wells from the predicted

456

as well as the laboratory determined UHV values, (b) The variation of grades of seam 2 from

457

the two methods and (c) The variation of grade of seam 1 in the three testing wells B9, B12

458

and B13.

459 460 461 462 463 464 465 466 467 468


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

469

Page 18 of 27

Table 1: Generalised geological stratigraphy of the Bishrampur Coalfield [4,7] Age

Formation

Recent/Sub Recent

Alluvium

Thickness (m)

Lithology

Soil & sub-soil.

Dolerite. Early Eocene/ Cretaceous

Dolerite Intrusive

Ferruginous conglomeratic sandstone. Upper Permian

Kamthi

100

i) Medium to coarse grained feldspathic sandstones, carbonaceous shale, clay beds and coal seams.

Lower Permian

Upper Carboniferous

Barakar

45-422

ii) Feldspathic coarse grained sandstones with pellets of Talchir material, lenses of conglomerates, angular fragments of quartz, quartzites, granite, shale, carb shale and thin coal seams.

Karharbari

5 to 105

Coarse conglomeratic sandstone.

Talchir

Fine grained sandstones, olive 1.5 to 269 green shale, silt stones and boulder beds.

Archaean

Metamorphic Basement

Granite, gneisses, quartzites, slates, phyllites and basic rocks.


Page 19 of 27

1 2 470 3 4 471 5 6 472 7 8 Well 9 No. 10 11 B1 12 B2 13 B3 14 B4 15 B5 16 B6 17 18 B9 19 B12 20 B13 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

Table 2: Coal Seam Correlation of Nine wells, Bishrampur Coalfield.

Seam 4 Reduced Level From To Thickness Floor (RL) (m) (m) (m) RL (m) 547.68 538.21 40.90 49.44 8.54 488.77 561.51 550.67 556.58 544.44 553.88 543.82 32.00 43.36 11.36 500.46 545.98 -

Seam 3 From (m) 155.70 105.80 89.04 48.21 49.49 114.53 46.80 96.35 75.40

Floor To (m) Thickness RL (m) 161.70 6.00 385.98 110.64 4.84 427.57 94.50 5.46 467.01 53.90 5.69 496.77 55.03 5.54 501.55 119.78 5.25 424.66 49.78 2.98 504.10 102.80 6.45 441.02 81.94 6.54 464.04

Seam 2

Seam 1

Floor From To Thickness RL (m) 209.90 219.00 9.10 328.68 160.26 169.45 9.19 368.76 139.96 149.70 9.74 411.81 97.56 106.89 9.33 443.78 98.20 107.10 8.90 449.48 166.27 177.37 11.10 367.07 108.16 119.01 10.85 434.87 151.11 162.11 11.00 381.71 127.10 139.16 12.06 406.82

Floor From To Thickness RL (m) 265.94 268.25 2.31 269.96 249.41 251.08 1.67 310.43 210.55 212.58 2.03 338.09 219.04 221.00 1.96 332.88 248.95 250.77 1.82 293.05 229.86 232.00 2.14 313.98


Energy & Fuels

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473 474

Page 20 of 27

Table 3: Statistical analysis showing the range of coal proximate parameters and UHV of nine wells under study area of Bishrampur Coalfield 475

Moisture % *

Ash %*

VM %*

FC%*

UHV (kCal/kg)*

Min

4.43

12.00

19.26

22.87

609.93 476

Max

8.11

55.64

31.10

52.90

6153.80 477

Mean

6.45

36.05

24.94

33.12

3187.07 478

St. Dev.

0.88

10.18

3.26

8.78

1270.85 479

480 481

* Determined at 40°C and 60% RH.

482 483

Table 4(a): Combinations between geophysical log parameters as inputs and laboratory parameters as desired target output used for regression models.

484 Combination X1 * COMB 1 MOISTURE COMB 2 VM

X2 * ASH FC

Y** UHV UHV

485

* Independent variables

486

** Dependent variable

487

Table 4(b): Results of Regression models using different combination Combinations R2 No. of Obs. St. error* F Fcritical P Coefficient Inter St. error** Coefficient X1 St. error*** Coefficient X2 St. error****

COMB 1 0.89 37 4.36 107.16 3.08 2.28E-26 100.25 15.09 -2.86 1.62 -1.39 0.14

COMB 2 0.85 19 5.64 5.02 3.16 0.0099 -28.02 5.64 0.80 0.48 1.26 0.18

488 489 490 491

St. error*: standard error of models, St. error** : standard error of Coefficient intercept, St. error*** , standard error of constant X1 of variable 1, , St. error**** : Standard error of constant X2 of variable 2 used in these models, Coefficient Inter: The coefficient of the intercept (constant value in the model).

492 493


Page 21 of 27

1 2 494 3 495 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 496 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

Table 5: MLFN models listing geophysical log parameters as inputs and coal proximate parameters, UHV as outputs with mean squared error (MSE) and R2 values for training, validation and testing stages.

MLFN Models

No. of Inputs

Input 1

Input 2

Input 3

No. of Datapoint

No. of Outputs

Output 1

Output 2

Output 3

Output 4

Output 5

No of Neurons

Epochs

MSE

R2 Training

R2 Validation

R2 Test

C1

2

Density

Natural Gamma

-

22

2

Ash

UHV

-

-

-

6

50

0.07

0.96

0.92

0.83

C2

2

Density

SPR

-

22

2

Ash

UHV

-

-

-

6

50

0.05

0.99

0.89

0.51

-

22

2

Ash

UHV

-

-

-

5

50

0.90

0.97

0.90

0.91

22

3

Ash

Moisture

UHV

-

-

100

0.000 85

0.99

0.53

0.94

C3

2

SPR

Natural Gamma

C4

3

Density

Natural Gamma

SPR SPR

15

5

Ash

Moisture

VM

FC

UHV

5

100

0.02

1.00

0.76

0.63

7

C5

3

Density

Natural Gamma

C6

2

Density

Natural Gamma

-

22

1

Moisture

-

-

-

-

5

50

0.02

0.98

0.96

0.88

C7

2

Density

SPR

-

22

1

Moisture

-

-

-

-

4

50

0.01

0.96

0.96

0.98

-

22

1

Moisture

-

-

-

-

4

50

0.001

0.91

0.91

0.87

-

-

-

4

100

0.000 7

0.99

0.74

0.99

C8

2

SPR

Natural Gamma

C9

3

Density

Natural Gamma

SPR

22

1

Moisture

C10

2

Density

Natural Gamma

-

15

1

Volatile Matter

-

-

-

-

4

50

0.37

0.99

0.85

0.37

C11

2

Density

SPR

-

15

1

Volatile Matter

-

-

-

-

6

50

0.02

0.99

0.96

0.82

C12

2

SPR

Natural Gamma

-

15

1

Volatile Matter

-

-

-

-

7

50

0.01

0.99

0.92

0.76

C13

3

Density

SPR

Natural Gamma

15

3

Volatile Matter

FC

UHV

-

-

7

100

0.026

1.00

0.85

0.001


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Page 22 of 27

Table 6: Training data from six wells B1 to B6 used to train the MLFN models constituting the geophysical log responses, coal proximate parameters and UHV values for three seams of Bishrampur Coalfield.

Seam name Well No. B1

B2

B3 B4 B5

B6

3/2 3/1 4/1 3/3 3/2 2/3 2/2 2/1 LOCAL UC 2A 1/1 3/2 2/1 LOCAL 2/3 2/2 2/1 LOCAL 3/3 3/2 3/1 2/2

Thickness 1.24 1.10 2.34 1.00 0.75 0.90 0.54 0.38 0.95 0.50 1.20 2.09 1.87 1.37 1.10 1.44 0.97 0.39 0.75 2.45 1.01 0.95 0.60

density (g/cc) 1.78 1.60 1.69 2.16 1.64 1.53 2.01 1.52 1.29 1.63 1.45 1.32 1.77 1.57 1.33 1.52 1.74 1.52 1.62 1.72 1.83 1.69 1.99

Single point resistance (Ω) 307.70 451.04 232.48 205.72 320.64 331.00 177.00 245.60 164.98 242.25 326.00 383.00 422.87 492.41 632.00 285.78 313.06 318.63 387.95 560.71 293.90 283.45 171.33

Natural gamma (cps) 78.58 75.39 102.87 149.52 86.40 72.33 110.00 84.74 46.23 136.19 53.74 31.00 100.65 95.79 32.71 74.01 129.63 72.51 37.41 92.32 127.28 95.46 115.09

Moisture % 5.46 5.90 6.30 4.43 6.14 7.48 4.56 6.56 7.10 6.45 6.51 7.90 5.00 7.76 6.77 8.11 6.47 6.76 6.40 6.20 6.05 5.86 5.73

Ash % 50.15 42.90 42.90 55.64 40.66 24.50 53.31 32.31 20.30 32.35 30.28 12.00 50.45 31.91 24.16 27.98 38.21 30.49 37.10 43.70 42.80 42.97 46.49


Volatile matter % 20.42 24.32 NA NA NA 27.51 19.26 NA 31.10 NA NA 27.20 21.05 24.71 26.18 NA NA NA 21.50 NA NA NA NA

Fixed Carbon % 23.97 26.88 NA NA NA 40.51 22.87 NA 41.50 NA NA 52.90 23.50 35.62 42.89 NA NA NA 35.10 NA NA NA NA

UHV (kCal/kg) 2833.00 3523.00 2111.00 609.93 2442.32 4486.76 913.94 3536.25 5118.80 3546.08 3821.87 6153.80 2900.00 3421.00 5266.00 3919.51 2733.85 3758.90 2897.00 2012.00 2159.20 2161.53 1693.96

Page 23 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 497 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

Table 7: Validation of the predicted ash, moisture, volatile matter, fixed carbon and UHV values from geophysical logs using MLFN models with the conventional laboratory determined data from three wells B9, B12 and B13 of Bishrampur Coalfield.

WELL NO.

B09

B12

B13

SEAM NAME 3/2 3/1 2/3 2/1 1/1 3/3 3/2 2/2 1/1 LOCAL 3/2 2/3 2/2 2A

Avg. Error %

MLFN model C5 predicted results ASH % 38.28 33.24 29.50 44.14 39.97 63.10 39.97 32.43 28.06 39.56 46.77 38.69 56.38 31.86 24.57

MOIST % 6.51 5.73 7.74 5.70 6.36 2.82 6.36 7.60 6.28 6.42 5.74 5.61 3.91 5.44 16.82

MLFN Model C4 predicted results

VM%

FC%

UHV*

22.26 31.01 24.54 21.07 21.32 18.05 21.32 23.56 30.96 21.6 20.09 24.26 19.06 26.51 15.90

32.95 30.02 38.22 29.09 32.35 16.03 32.35 36.41 34.70 32.42 27.4 31.44 20.65 36.19 18.26

4174.00 2167.00 5533.00 3621.00 5062.00 724.00 5062.00 2173.00 3161.00 4580.00 4059.00 3935.00 1604.00 4250.00 38.07

ASH % 29.05 20.18 25.22 30.69 15.05 42.58 26.38 37.98 17.31 28.5 32.01 25.79 37.51 13.28 21.82

MOIST % 6.41 7.69 6.80 6.23 8.95 5.10 6.48 5.92 7.48 6.41 5.81 6.69 5.64 7.42 6.02

VM%

FC%

NA NA NA NA NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA NA NA NA NA NA

NA

NA

MLFN models C1, C7 and C11 predicted results

UHV* 3673.70 5388.25 4203.90 3474.71 6398.17 2001.22 4090.84 2415.02 5328.11 3763.60 3918.07 4081.86 2604.28 6231.02 28.03

ASH % 34.67 27.06 31.33 36.01 18.98 48.47 34.01 38.50 26.04 32.26 39.50 31.79 39.77 24.77 4.56

MOIST % 6.78 6.71 6.76 5.95 6.89 5.23 6.74 6.47 6.50 6.98 6.40 6.94 6.36 6.76 6.17

VM%

FC%

UHV*

22.75 23.71 23.33 19.30 22.42 20.76 24.61 24.08 24.75 24.74 23.02 24.71 22.50 24.75 8.49

35.80 42.52 38.58 38.74 51.71 25.54 34.55 30.95 42.71 36.03 31.08 36.56 31.37 43.72 12.25

2862.20 4457.70 3563.30 2598.80 5740.00 1518.70 2978.90 2229.60 4657.40 3366.70 2068.40 3469.00 2293.70 4894.10 5.68

Overall analysis laboratory data at 40⁰⁰C and 60% RH ASH MOIST VM% FC% UHV* % % 35.40 6.40 NA** NA 3134.00 26.40 7.60 NA NA 4201.00 32.50 7.10 NA NA 3435.00 38.50 6.40 NA NA 2703.00 19.20 7.30 NA NA 5735.00 48.50 5.20 21.9 24.50 1500.44 35.10 7.10 27.6 30.20 3075.00 40.65 6.00 30.1 23.20 2458.16 25.28 6.80 25.9 42.10 4478.48 34.34 7.61 26.3 31.80 3110.90 41.40 5.80 25.8 27.00 2122.00 34.00 7.00 24.2 34.80 3248.00 45.70 5.63 21.9 26.77 1918.00 23.20 6.70 28.9 41.20 4783.00 NA NA NA NA NA

MOIST: Moisture content, VM: Volatile matter, FC: Fixed Carbon, Avg. Error: Average Error, RH: Relative Humidity, NA: Not Available Table 8: Grading of Non-Coking Coal Seams in terms of Useful Heat Value (UHV) [14]. GRADES

A

B

C

D

E

F

G

UHV (kcal/Kg)

>6200

5601-6200

4941-5600

4201-4940

3361-4200

2401-3360

1301-2400


Energy & Fuels

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1915x922mm (96 x 96 DPI)

ACS Paragon Plus Environment

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Energy & Fuels

849x799mm (96 x 96 DPI)


Energy & Fuels

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522x401mm (96 x 96 DPI)


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Energy & Fuels

139x260mm (96 x 96 DPI)


Prediction of Coal Proximate Parameters and Useful Heat Value of

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