Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies Suhas B. Ghugare a, b, c , Sanjeev S. Tambe a, * a Artificial Intelligence Systems Group, Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India b Chemical Engineering Department, All India Shri Shivaji Memorial Society's College of Engineering (AISSMSCOE), Kennedy Road, Pune 411001, India c Savitribai Phule Pune University (SPPU), Ganeshkhind Road, Pune 411007, India article info Article history: Received 14 December 2015 Received in revised form 23 February 2016 Accepted 1 March 2016 Available online xxx Keywords: Coal Higher heating value Genetic programming Proximate analysis Ultimate analysis abstract The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal's HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlationsdwhich are mostly lineardhave been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by utilizing the computa- tional intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capa- bility as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal- specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals. © 2016 Energy Institute. Published by Elsevier Ltd. All rights reserved. 1. Introduction The potential energy yield (total heat content) of a unit mass of coal is determined in terms of the higher heating value (HHV) (also known as gross calorific value). It is defined as the amount of heat evolved when a unit weight of the fuel is burnt completely and the combustion products cooled to a standard temperature of 298 K and at a standard pressure of 101.33 kPa [1]. The proximate and/or ultimate analyses of coals and their HHVs are strongly correlated. While the proximate analysis determines the individual content of moisture, volatile matter , ash, and fixed carbon in a coal, the ultimate analysis measures the amounts of various elements, namely, carbon, hydrogen, nitrogen, sulphur , and oxygen. Since HHV is a major indicator of coal's quality, it is used extensively in: (a) the efficiency and optimal design calculations of coal combustion and gasification equipment [2,3], (b) defining coal's rank (over much of the rank range), and (c) evaluating the pollution compliance of coal-based processes [4]. There exist mostly linear models that correlate the constituents of a coal's proximate and/or ultimate analysis to its HHV [5e9]. The early HHV prediction models were developed for the coals with specific ranks or from particular geographical regions; these were also based on limited amounts of data [5e16]. Owing to its importance in designing, operating and optimizing coal-based processes, attempts to develop HHV predicting models with ever increasing prediction accuracies still continue. In recent years, generalized correlations encompassing * Corresponding author. Tel.: þ91 20 2590 2156; fax: þ91 20 2590 2621. E-mail address: ss.tambe@ncl.res.in (S.S. Tambe). Contents lists available at ScienceDirect Journal of the Energy Institute journal homepage: http://www.journals.elsevier.com/journal-of-the-energy- institute http://dx.doi.org/10.1016/j.joei.2016.03.002 1743-9671/© 2016 Energy Institute. Published by Elsevier Ltd. All rights reserved. Journal of the Energy Institute xxx (2016) 1e9 Please cite this article in press as: S.B. Ghugare, S.S. Tambe, Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies, Journal of the Energy Institute (2016), http://dx.doi.org/10.1016/ j.joei.2016.03.002