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Advancements in Artificial Cloning Technology for Industrial Sensors and Neural Networks

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This project, led by Ph.D. Dr. Sc. Ing. Antonio Faustino Muñoz Moner from the Autonomous University of Bucaramanga, Colombia, explores the integration of artificial cloning technology in industrial sensors and controllers. Focusing on genetic mapping and neural network applications, it addresses the processes in petrochemical plants, specifically phenol production. Key methodologies include real-time data processing, signal treatment, and virtual sensing. The project aims to innovate sensor technology through genetic algorithms, enhancing system monitoring and control.

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Advancements in Artificial Cloning Technology for Industrial Sensors and Neural Networks

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  1. ARTIFICIAL CLONING TECHNOLOGY BASED IN INDUSTRIAL SENSORS AND CONTROLLERS IN NEURAL NETWORK AND GENETIC MAPPING. PROYECTO COLCIENCIAS Autonomous University of Bucaramanga.Colombia Ph.D.Dr.Sc.Ing.Antonio Faustino Muñoz Moner CONTENIDO Introducción Procesos Petroquímicos Planta de Fenol Variable a Inferir (Sensor Real) Analizadores y Medidores Sistemas de Monitoreo y Control de la Planta Redes Neuronales Variables de Entrada (P,L,T,F) Sensor Virtual Requerimientos Metodología Selección de Entradas Tratamiento de Señal Entrenamiento y ReEntrenamiento Bases de Datos de Tiempo Real (PI y DCS) Validación Aplicación (Sensor Clonado) Modelo Obtenido Conclusiones PROYECTO COLCIENCIAS

  2. SENSOR CLONADO – Patente de Invención CONTENIDO Introducción Procesos Petroquímicos Planta de Fenol Analizadores y Medidores Redes Neuronales Sensor Virtual Requerimientos Metodología Selección de Entradas Tratamiento de Señal Entrenamiento y ReEntrenamiento Validación Modelo Obtenido Conclusiones PROYECTO COLCIENCIAS Genetic evolution is a process that evolves a set ofindividuals, which constitutes the population, producing a new population. Here, individuals arehardware designs. The more the design obeys the constraints, the more it is used in thereproduction process. The design constraints could be expressed in terms of hardware area and/orresponse time requirements. The freshly produced population is yield using some genetic operators such as crossover and mutation that attempt to simulate the natural breeding process inthe hope of generating new design that are fitter i.e. respect more the design constraints. Geneticevolution is usually implemented using genetic algorithms.

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