Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture
Ritu Gupta1, Ekata2, C. M. Batra3

1Ritu Gupta, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.
2Ekata, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.
3C. M. Batra, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.

Manuscript received on 21 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 440-445 | Volume-8 Issue-11, September 2019. | Retrieval Number: K13920981119/2019©BEIESP | DOI: 10.35940/ijitee.K1392.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The world’s ever increasing demand for energy and abating global warming, suitable renewable sources of energy are highly in demand. The wastes from industries such as plant’s biomass could meet the energy requirements. In this paper authors analyze bio fuel plant system which produces ethanol fuel. This system is divided into various subsystems considering multiple phases in the production of ethanol. The structure of this system consists of interconnected networks of components on very large dimensional scales escalates the complexity of systems that can increase the degradation of system’s functioning. In view of this, one of the computational intelligence approach, neural network (NN), is useful in predicting various reliability parameters. To improve the accuracy and consistency of parameters, Feed Forward Back Propagation Neural Network (FFBPNN) is used. All types of failures and repairs follow exponential distributions. System state probabilities and other parameters are developed for the proposed model using neural network approach. Failures and repairs are treated as neural weights. Neural network’s learning mechanism can modify the weights due to which these parameters yield optimal values. Numerical examples are included to demonstrate the results. The iterations are repeated till the convergence in the error tends up to 0.0001 precision using MATLAB code. The reliability and cost analysis of the system can help operational managers in taking the decision to implement it in the real time systems.
Keywords: Neural Network, Back Propagation, Neural Weights, Profit Function, Reliability, Stochastic Process
Scope of the Article: