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Investigating Jordan Oil Shale Properties Using Artificial Neural Network (ANN)

The Artificial Neural Network (ANN) is a functional imitation of simplified model of the biological neurons and their goal is to construct useful computers for real-world problems and reproduce intelligent data evaluation techniques like: Pattern recognition, classification and generalization by using simple, distributed and robust processing units called artificial neurons. ANNs are fine-grained parallel implementation of non-linear static-dynamic systems. The intelligence of ANN and its capability to solve hard problems emerges from the high degree of connectivity that gives neurons its high computational power through its massive parallel-distributed structure. The current resurgent of interest in ANN is largely because ANN algorithms and architectures can be implemented in VLSI technology for real time applications. The number of ANN applications has increased dramatically in the last few years, fired by both theoretical and application successes in a variety of disciplines. Among the potential areas of application is how to use ANN in analyzing El-Lajjun oil shale. So the major first objective of this paper is to build a neural network to investigate the effect of composition of EL-lujjum oil shale. Our designed neural network was learned using already collected data from 100 samples of oil shale from El-lajjun deposit in the south of Jordan. The presented neural network in this paper has a capability of predicting the gross calorific value for new samples based on the composition values of: Calcium Carbonate, Organic Carbon and sulfur. The second objective of this paper lies in comparison study between the output results that are obtained from the ANN and that is obtained from some empirical formula, i.e. comparing between the estimated output result that are obtained from ANN with the other similar estimated results obtained using some mathematical formula. The output results show and indicate that ANN outperforms better the mathematical formula with 3%, this means that ANN has a better certainty factor with respect to other traditional approach. An ANN result converges with the experimental results with a percentage of 99.7%.