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An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

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Abstract

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R 2), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R 2 values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R 2 values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.

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References

  • Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13(2):1085–1098

    Article  Google Scholar 

  • Atashpaz-Gargari, E (2008) Developing a socially inspired optimization algorithm, Master thesis (in persian), University of Tehran, Iran

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation pp 4661–4667

  • Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aid Civ Infrastruct Eng 5(6):445–463

    Article  Google Scholar 

  • Balan HO, Gumrah F (2009) Assessment of shrinkage-swelling influences in coal seams using rank-dependent physical coal properties. Int J Coal Geol 77:203–213

    Article  Google Scholar 

  • Demuth H, Beale M, Hagan M (2009) MATLAB Version 7.14.0.739; Neural Network Toolbox for Use with Matlab. The Mathworks

  • Dreyfus G (2005) Neural networks: methodology and application. Springer, Berlin

    Google Scholar 

  • Du KL, Lai AKY, Cheng KKM, Swamy MNS (2002) Neural methods for antenna array signal processing: a review. Signal Process 82:547–561

    Article  Google Scholar 

  • Eberhart RC, Shi Y (1998) Evolving artificial neural networks. In: Proceedings of the international conference on neural networks and brain, pp PL5–PL13

  • Ebrahimi E, Mollazade K, Babaei S (2014) Toward an automatic wheat purity-measuring device: a machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement 55:196–205

    Article  Google Scholar 

  • Fausett LV (1994) Fundamentals of neural networks: architecture, algorithms and applications. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770

    Article  Google Scholar 

  • Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32:85–97

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886

    Article  Google Scholar 

  • Haykin S (1999) Neural Networks, 2nd edn. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, USA, pp 11–14

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Hush DR (1989) Classification with neural networks: a performance analysis. In Proceedings of the IEEE international conference on systems engineering. Dayton, OH, USA, pp 277–280

  • Jahed Armaghani D, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2016a) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32:109–121

    Article  Google Scholar 

  • Jahed Armaghani D, Hasanipanah M, Mohamad ET (2016b) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171

    Article  Google Scholar 

  • Jahed Armaghani D, Raja Shoib RSNS, Faizi K, Rashid ASA (2017) Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405

    Article  Google Scholar 

  • Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  • Kalinli A, Acar MC, Gunduz Z (2011) New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng Geol 117:29–38

    Article  Google Scholar 

  • Kalkreuth W, Sherwood N, Cioccari G, da Silva Correa, Silva M, Zhong N et al (2004) The application of FAMM (fluorescence alteration of multiple macerals) analyses for evaluating rank of Parana Basin coals, Brazil. Int J Coal Geol 57:165–185

    Article  Google Scholar 

  • Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  • Khamesi H, Torabi S, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng 29(6):05014010

    Article  Google Scholar 

  • Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27(3):193–200

    Article  Google Scholar 

  • Khandelwal M, Jahed Armaghani D (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34(2):605–620

    Article  Google Scholar 

  • Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2010) Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks. Fuel 89(5):1101–1109

    Article  Google Scholar 

  • Kuo RJ, Wang YC, Tien FC (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18(12):1161–1170

    Article  Google Scholar 

  • Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226

    Article  Google Scholar 

  • Lynch LJ (1989) Introductory perspective: characterization of sedimentary organic matter through true definition of macerals. In: Thomas CG, Strachan MG, (eds) The effect of macerals on the utilization of coal and their significance in petroleum exploration: a symposium, p 1–3

  • Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci. World. J, Article ID 643715

  • Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston

    Google Scholar 

  • Maulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39

    Article  Google Scholar 

  • Mohanty JK, Misra SK, Nayak BB (2001) Sequential leaching of trace elements in coal: a case study from Talcher coal field, Orissa. J Geol Soc India 58:441–447

    Google Scholar 

  • Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19(1):85–93

    Article  Google Scholar 

  • Mukherjee AK, Alam MM, Ghose S (1988) Gondwana coals of Bhutan Himalaya –occurrence, properties and petrographic characteristics. Int J Coal Geol 9:287–304

    Article  Google Scholar 

  • Mukherjee AK, Alum MM, Mazumdar SK, Haque R, Gowrisankaran S (1992) Physicochemical properties and petrographic characteristics of the Kapurdi lignite deposit, Barmer Basin, Rajasthan, India. Int J Coal Geol 21:31–44

    Article  Google Scholar 

  • Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading

    Google Scholar 

  • Ornek M, Laman M, Demir A, Yildiz A (2012) Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soil Found 52(1):69–80

    Article  Google Scholar 

  • Panigrahi DC, Sahu HB (2004) Application of hierarchical clustering for classification of coal seams with respect to their proneness to spontaneous heating. Min Technol (Trans Inst Min Metal A) 113:97–106

    Google Scholar 

  • Paola JD (1994) Neural network classification of multispectral imagery. M.Sc. thesis, The University of Arizona, USA

  • Pareek HS (1983) Chemico-petrographic studies of some lignite samples from kalol oilfield, Cambay Basin, Gujarat, Western India. Int J Coal Geol 3:183–204

    Article  Google Scholar 

  • Parikh J, Channiwala SA, Ghosal GK (2005) A correlation for calculating HHV from proximate analysis of solid fuels. Fuel 84:487–494

    Article  Google Scholar 

  • Pearson DE (1991) Probability analysis of blended coking coals. Int J Coal Geol 19:109–119

    Article  Google Scholar 

  • Rai KL, Shukla RT (1979) Depositional environment and origin of coal in Pench–Kanhan valley coalfield, M.P., India. In: Proceedings of the 4th international Gondwana symposium, Geological Survey of India, Calcutta, vol. 1, p 256–74

  • Ravi V, Reddy PJ (1999) Ranking of Indian coals via fuzzy multi attribute decision making. Fuzzy Sets Syst 103:369–377

    Article  Google Scholar 

  • Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff- Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123

    Chapter  Google Scholar 

  • Sari M, Ghasemi E, Ataei M (2014) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mech Rock Eng 47(2):771–783

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron Eng 128(9):785–793

    Article  Google Scholar 

  • Sharma NL, Ram KSV (1966) A handbook of introduction to the geology of coal and Indian coal fields, 2nd edn. Indian School of mines, Dhanbad

    Google Scholar 

  • Simpson PK (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York

    Google Scholar 

  • Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  • Takahashi R, Sasaki M (1989) Automatic macerals analysis of low-rank coal (brown coal). Int J Coal Geol 14:103–118

    Article  Google Scholar 

  • Tonnizam Mohamad E, Armaghani DJ, Noorani SA, Saad R, Alvi Nezhad SV (2012) Prediction of flyrock in boulder blasting using artificial neural network. EJGE 17:2585–2595

    Google Scholar 

  • Vasconcelos Lopo de Sousa (1999) The petrographic composition of world coals: statistical results obtained from a literature survey with reference to coal type (maceral composition). Int J Coal Geol 40:27–59

    Article  Google Scholar 

  • Wang C (1994) A theory of generalization in learning machines with neural application. Ph.D. thesis, The University of Pennsylvania, USA

  • Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3–4):141–158

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Science and Technology, Federation University Australia, PO Box 663, Ballarat, Victoria, 3353, Australia

    Manoj Khandelwal

  2. Department of Structure and Material, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia

    Amir Mahdiyar

  3. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Danial Jahed Armaghani & Ahmad Fahimifar

  4. Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India

    T. N. Singh

  5. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Roohollah Shirani Faradonbeh

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  1. Manoj Khandelwal
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  5. Ahmad Fahimifar
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Correspondence to Manoj Khandelwal.

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Khandelwal, M., Mahdiyar, A., Armaghani, D.J. et al. An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals. Environ Earth Sci 76, 399 (2017). https://doi.org/10.1007/s12665-017-6726-2

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