Design of Intelligent Expert System with inference by nanoinstrumentation
Muñoz Moner, Antonio
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Abstract- This work shows a technology of artificial cloning for industrial sensors by means of the use of neural networks and genetic mapping. The neural networks allow develop to the intelligent structure of the micro- nanosensors, for it, the method of activation of random values is used to train the sensors and to carry out the learning starting from real devices, the genetic mapping allows the generation of codes for the cloning procedure, for it the mutation processes, crossing, reproduction and investment are used also, an example of a cloned sensor that determines the index of viscosity of lubricant oils with phenol for a monitoring system is briefly explained. The present article shows the results of the research carried out in the project on the development of a real time monitoring technology for the content of phenols in industrial wastewater. In - line dump flows, treatment tanks, stabilization pools In the open water and in the discharge of the water spill as part of the challenge presented by ECOPETROL on the need for decontamination of the wastewater in the Barrancabermeja Oil Refinery where they include the results on the design, development and implementation of a methodology that Through the detection of contaminants in real time, uses a network of sensors based on micro-nanobioinstrumentation with electronic nose, artificial tongue and spectrophotometric eye, supported in mobile technology for the monitoring of parameters of water quality (phenol content) in lines of Pipe in vertimient and effluents, the article presents the results development of a real-time monitoring system and on-line control by mobile technology of water quality parameters (phenols) in pipeline line for shedding and effluents that emulate by functional replication of the senses of smell, taste and spectrophotometric vision by artificial cloning that is applied in the design of the sensor network and control systems.