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Control predictivo basado en modelo para procesos de tratamiento de aguas residuales con restricciones e intervalos

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    Una nueva estrategia de control predictivo basado en modelo basado en datos (DDMPC) estabiliza la concentración de oxígeno disuelto (DOC) en plantas de tratamiento de aguas residuales (PTAR) a pesar de los tiempos de muestreo impredecibles. Este enfoque garantiza una operación estable bajo las restricciones del sistema.

    Palabras clave:
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    Área de la Ciencia:

    • Ingeniería Ambiental
    • Ingeniería de Sistemas de Control
    • Inteligencia Artificial

    Sus antecedentes:

    • Los procesos de tratamiento de aguas residuales (PTAR) enfrentan desafíos en el control estable de la concentración de oxígeno disuelto (DOC) debido al muestreo estocástico y las restricciones operativas.
    • Las estrategias de control existentes luchan con la suposición de adquisición de datos periódica, lo que afecta el rendimiento del sistema.

    Objetivo del estudio:

    • Proponer una estrategia de control predictivo basado en modelo basado en datos (DDMPC) para el control estable de PTAR con restricciones y con intervalos de muestreo estocásticos.
    • Abordar las dificultades para lograr un control estable de DOC bajo adquisición de datos variable y limitaciones operativas.

    Principales métodos:

    • Se diseñó un marco DDMPC con una función objetivo que considera la expectativa matemática de la salida predicha y las restricciones del sistema.
    • Se desarrolló una estructura de predicción multimodelos basada en datos que utiliza redes neuronales difusas (FNN) para manejar intervalos de muestreo estocásticos.
    • Un algoritmo de resolución de controladores basado en el método del multiplicador generalizado reformuló el problema de optimización con funciones de penalización para las restricciones.

    Principales resultados:

    • La estrategia DDMPC propuesta maneja eficazmente la adquisición de datos estocásticos causada por intervalos de muestreo aleatorios.
    • Las simulaciones en el modelo de simulación de referencia n.º 1 (BSM1) demostraron la capacidad de la estrategia para garantizar una operación estable del sistema bajo restricciones.
    • La estrategia DDMPC logró con éxito el control estable de DOC en PTAR con restricciones y con intervalos de muestreo estocásticos.

    Conclusiones:

    • La estrategia DDMPC desarrollada proporciona una solución robusta para controlar la DOC en PTAR con estocasticidad inherente y restricciones operativas.
    • El enfoque mejora la estabilidad y confiabilidad de los procesos de tratamiento de aguas residuales.
    • Este método basado en datos ofrece una dirección prometedora para el control avanzado de procesos en ingeniería ambiental.