## 1 Introduction  
Coal is one of the non-renewable natural resources, like oil and gas [1]. It is a fossil fuel that is widely used in the metallurgical industry and in thermal power plants [2; 3]. Besides, it is also used for cement industries, industrial chemicals, and aluminum, to name a few [4]. So far, coal still accounts for 40% to 50% of energy generation in the world [5]. The main components of coal include carbon (C), hydrogen (H), nitrogen (N), sulfur (S) and oxygen (O). For the chemical parameters, fixed carbon (FC), ash (A), volatile matter (VM), and moisture (M) are the necessary components of coal [6]. These are the main parameters that decide the heat value of coal [7; 8]. A literature review indicated that most of coal applications are related to heating value (gross calorific value-GCV) [9]. Depending on each specific field (e.g., steel industry, cement, power plant, to name a few), the GCV requirements are different [10]. Therefore, determination of GCV in coal seams essential for calculating energy demand, and improving economic effectiveness, as well as selective extraction. In recent years, scientific and technological issues related to energy and fuel have achieved many new achievements. Simulation and optimization methods for the use of fuel have also been proposed [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Environmental issues due to fuel impacts have also been considered and studied [21; 22; 23; 24; 25]. Among the types of fuel, the GCV of coal is considered as a primary fuel type for thermal plans. For determination coal GCV, many scholars have been carried out to investigate the relationship between GCV and proximate analysis (i.e., FC, A, VM, and M) as well as coal properties [26; 27; 28]. For instance, many equations and soft computing models have been proposed. Empirical methods for estimating GCV of coal have been introduced very early [29; 30; 31; 32]. However, they usually take a long time with low accuracy. In recent years, artificial intelligence (AI) is well-known as an advanced technique to solve most of the complex problems relevant engineering, especially in the field of energy and fuels [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. For predicting the GCV of coal, [PERSON] [52] applied multiple nonlinear regression models for predicting GCV of coal with high reliability. [PERSON] et al. [28] also developed a soft computing model for predicting GCV based on support vector regression (SVR) algorithm with a promising result. In another study, an artificial neural network (ANN) successfully studied and applied in predicting GCV by [PERSON] et al. [53]. They concluded that the ANN model was suitable for estimating GCV with high accuracy. [PERSON] [54] also developed ANN and an adaptive neuro-fuzzy inference mode (ANFIS) to simulate the GCV of coal. In their study, the ANFIS model exhibited accuracy better than the ANN model. Another technique using wavelet neural networks (WNNs) was also introduced and was applied to estimate GCV by [PERSON] et al. [55]. They compared their WNNs model with previous methods (employed and redeveloped previous models) to develop a complete conclusion. Their results indicated that the WNNs model yielded higher accuracy than the earlier methods in their study. By a new approach, [PERSON] [56] developed a transparent open-box learning networks (TOB) algorithm for predicting the GCV of coal. He found that the TOB algorithm can predict GCV with a tiny error. In addition, many similar works were also carried out to predict GCV using AI techniques, such as [57; 58; 59; 60; 61; 62; 63; 64; 65]. As in our review of GCV prediction, many soft computing models were widely developed and applied during recent years. However, their performance was not confirmed in other areas since the coal quality in each region/area/seam was different. Furthermore, new soft computing/AI models, with the capability of accurate prediction of GCV, have become the goal of scholars.