### 1. Introduction

### 2. Material and Methods

### 2.1. Materials

### 2.2. Experimental Setup

### 2.3. Sample Analyses

### 2.4. Experimental Design

### 3. Results and Discussion

### 3.1. Characterization of Low Grade Coal

### 3.2. Model Fitting and ANOVA

^{2}, B

^{2}, A

^{2}B, and AB

^{2}are statistically significant at 94% confidence level. Only the term AB is statistically insignificant [18]. It is considered in the model to correct the hierarchy of the model since the A

^{2}B and AB

^{2}are significant model terms. The R

^{2}(0.985), adjusted R

^{2}(0.979), and predicted R

^{2}(0.960) values are consistently above 0.95 indicating the goodness of fit of the model [17–19]. The values of R

^{2}and adjusted R

^{2}are quite close (difference = 0.006) which indicates the significance of terms in the model. The adjusted R

^{2}decreases if the added terms cease to add value to the model. The high value of predicted R

^{2}and its close agreement with the adjusted R

^{2}depicts the high predictability of the model. The high adequate precision value (34.85), signal to noise ratio, further adds to the confidence in the model [37]. The ANOVA results, therefore, statistically approve the model to represent the effect of environmental factors on the GCV of the coal. Similarly, the ANOVA analyses of the models developed to mathematically describe the effects of environmental factors on moisture, fixed carbon, volatile matter, and ash are statistically significant with reasonable regression co-efficient and adequate precision values as shown in Table 4. The experimental versus model predicted values are shown in Fig. 1. Fig. 1(a) shows the distribution of experimental vs model predicted GCV distributed around perfect prediction line (dotted line, RSM = experimental). A high correlation (R

^{2}= 0.9849) is indicative of the robustness of the model. Similarly, the models developed for moisture (R

^{2}= 0.9911), fixed carbon (R

^{2}= 0.9633), volatile matter (R

^{2}= 0.8387), and ash (R

^{2}= 0.8499) correlate well with the experimental data as shown in Fig. 1. The ANOVA (Table 4) and correlation (Fig. 1) statistically established the models to be used to explore the effect of environmental factors on GCV and proximate analysis.

### 3.3. Influence of Temperature and Humidity on GCV

^{2}B, AB

^{2}in the model (Eq. (1)). The contours are not straight lines which shows the non-linear relationship between the GCV and combined effect of temperature and humidity. However, as shown in Fig. 2(a), (b), a pattern emerges indicating the gradual decrease in GCV with the increase of humidity at all studied temperatures.