Natural Gas Leak Detection System using AI: Fuzzy Logic,

Neural Network,GABy: Edgar Caburatan Carrillo II

Master of Science in Mechanical Engineering

De La Salle University Manila, Philippines

Natural Gas Industry

Natural Gas Production

Natural Gas Pipeline System

Gulf of Mexico 2010 BP Gulf of Mexico

Oil companies

Oil Companies

Oil Companies

1. Fuzzy LogicFuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1.

2. Neural NetworksIn computer science and related fields, artificial neural networks are computational models inspired by an animal's central nervous systems which is capable of machine learning as well as pattern recognition.

3. Genetic AlgorithmIn the computer science field of artificial intelligence, genetic algorithm is a search heuristic that mimics the process of natural selection. This heuristic is routinely used to generate useful solutions to optimization and search problems

When GA is applied to a problem:

Following must be addressed:1. How to represent the individual? ( Genetic Algorithm Structure)

2. What is the fitness function? ( to measure the performance of each individual)

3.What is the criterion for selection? ( best or fittest individuals in the population)

4. How to end the search? ( loop termination condition)

Genetic Algorithm Structure

EC Clone Genetic Algorithm

Conclusion

In our Discussion, we are able to learn:

1.Natural gas pipeline system

2. Fuzzy logic

3. Neural Networks

4. Genetic Algorithm

5. GA Application

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7. References[1] http://en.wikipedia.org/wiki/Imperial_College_London[2]http://www.nytimes.com/imagepages/2012/10/25/world/asia/25iht-sreducemerging25-graphic.html?ref=asia[3] http://www3.imperial.ac.uk/aboutimperial[4] http://www.ee.ic.ac.uk/gelenbe/[5] http://en.wikipedia.org/wiki/Random_neural_network[6] https://www.academia.edu/3875250/Analysis_of_single_and_networked_auctions[7] http://rspa.royalsocietypublishing.org/content/464/2096/2219.full[8] E. Gelenbe, Random neural networks with negative and positive signals and product form solution, Neural Computation, vol. 1, no. 4, pp. 502–511, 1989.[9] E. Gelenbe, Stability of the random neural network model, Neural Computation, vol. 2, no. 2, pp. 239–247, 1990. E. Gelenbe, A. Stafylopatis, and A. Likas, Associative memory operation of the random network model, in Proc. Int. Conf. Artificial Neural Networks, Helsinki, pp. 307–312, 1991. [10] E. Gelenbe, F. Batty, Minimum cost graph covering with the random neural network, Computer Science and Operations Research, O. Balci (ed.), New York, Pergamon, pp. 139–147, 1992.

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