{"id":9300,"date":"2024-01-17T17:21:49","date_gmt":"2024-01-17T17:21:49","guid":{"rendered":"https:\/\/dailyai.com\/?p=9300"},"modified":"2024-01-17T20:53:30","modified_gmt":"2024-01-17T20:53:30","slug":"researchers-use-ai-to-reduce-carbon-capture-energy-consumption-by-a-third","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/01\/researchers-use-ai-to-reduce-carbon-capture-energy-consumption-by-a-third\/","title":{"rendered":"Los investigadores utilizan la IA para reducir un tercio el consumo de energ\u00eda en la captura de carbono"},"content":{"rendered":"

Un estudio publicado en Reaction Chemistry and Engineering por investigadores de la Universidad de Surrey utiliz\u00f3 la IA para mejorar la captura de carbono en centrales el\u00e9ctricas, reduciendo el consumo de energ\u00eda en m\u00e1s de un tercio.<\/b><\/p>\n

En investigaci\u00f3n<\/a>dirigido por el profesor Jin Xuan, catedr\u00e1tico de procesos sostenibles de la Facultad de Qu\u00edmica e Ingenier\u00eda Qu\u00edmica de la Universidad de Surrey, se centr\u00f3 en la optimizaci\u00f3n de los sistemas de captura de carbono.\u00a0<\/span><\/p>\n

Muestra un sistema modelo basado en una central el\u00e9ctrica de carb\u00f3n real que utiliza la IA para lograr un aumento de 16,7% en la captura de di\u00f3xido de carbono (CO2) y reducir al mismo tiempo el consumo no energ\u00e9tico de la red nacional del Reino Unido en 36,3%.<\/span><\/p>\n

El profesor Xuan destac\u00f3 la novedad de este planteamiento, <\/span>seg\u00fan un art\u00edculo de la Universidad de Surrey<\/span><\/a>Normalmente, los sistemas de captura de carbono funcionan constantemente, al mismo ritmo, independientemente de los cambios externos del entorno. Pero hemos demostrado que ense\u00f1ar al sistema a realizar peque\u00f1as adaptaciones puede producir grandes ahorros de energ\u00eda y, al mismo tiempo, capturar m\u00e1s carbono.\"<\/span><\/p>\n

La captura de carbono es un proceso fundamental para mitigar el impacto ambiental del CO2, el principal gas de efecto invernadero producido por la mayor\u00eda de las centrales el\u00e9ctricas.\u00a0<\/span><\/p>\n

Este m\u00e9todo consiste en hacer burbujear los gases de combusti\u00f3n a trav\u00e9s de agua que contiene piedra caliza, que reacciona con el carbonato c\u00e1lcico para formar bicarbonato inocuo.\u00a0<\/span><\/p>\n

Sin embargo, este proceso consume mucha energ\u00eda, ya que requiere energ\u00eda para bombear el agua y el CO2. La planta de captura de CO2 del estudio ten\u00eda su propia turbina e\u00f3lica para obtener energ\u00eda renovable, pero cuando el tiempo era m\u00e1s tranquilo y no era suficiente, utilizaba energ\u00eda de la red.\u00a0\u00a0<\/span><\/p>\n

Los logros del equipo de investigaci\u00f3n residen en el uso de la IA para permitir al sistema modelo predecir las fluctuaciones en la producci\u00f3n de CO2 y la disponibilidad de energ\u00eda renovable. Esto permiti\u00f3 al sistema ajustar el bombeo de agua en consecuencia, optimizando el uso de energ\u00eda a lo largo del tiempo.<\/span><\/p>\n

\"Aprendizaje
Optimizaci\u00f3n de las tecnolog\u00edas de captura de carbono con aprendizaje autom\u00e1tico. Fuente: Qu\u00edmica e ingenier\u00eda de las reacciones<\/a>.<\/figcaption><\/figure>\n

El Dr. Lei Xing, profesor de qu\u00edmica e ingenier\u00eda qu\u00edmica de la Universidad de Surrey, destac\u00f3 las implicaciones de sus hallazgos: \"Aunque hemos probado nuestro modelo en la meteorizaci\u00f3n mejorada, los principios se aplican de forma m\u00e1s amplia. Nuestro modelo podr\u00eda ayudar a cualquiera que intente capturar y almacenar m\u00e1s CO2 con menos energ\u00eda, sea cual sea el proceso que utilice\".<\/span><\/p>\n

Los investigadores esperan que estos hallazgos puedan contribuir a los Objetivos de Desarrollo Sostenible de las Naciones Unidas, que pretenden reducir la presi\u00f3n sobre las fuentes de energ\u00eda no renovables e invertir en tecnolog\u00edas innovadoras de captura de carbono.\u00a0\u00a0<\/span><\/p>\n

A principios de enero de este a\u00f1o, Microsoft utiliz\u00f3 la IA para crear un nuevo electrolito de bater\u00eda de litio capaz de reducir <\/span>contenido de litio en las pilas por unos 70%<\/span><\/a>.<\/span><\/p>\n

M\u00e1s informaci\u00f3n sobre el estudio<\/span><\/h2>\n

El estudio pretend\u00eda optimizar la captura de carbono para reducir el uso de fuentes de energ\u00eda no renovables y, al mismo tiempo, mantener o mejorar la captura de CO2.\u00a0<\/span><\/p>\n

Utilizaba la IA para ajustar el sistema en funci\u00f3n de peque\u00f1os cambios en lugar de funcionar continuamente al mismo ritmo.\u00a0<\/span><\/p>\n

As\u00ed es como funciona:<\/span><\/p>\n

    \n
  • Creaci\u00f3n de modelos para la captura de CO2<\/b>: Los investigadores desarrollaron modelos de aprendizaje autom\u00e1tico para predecir la eficacia con la que un reactor podr\u00eda capturar CO2 y la cantidad de energ\u00eda que consumir\u00eda. Utilizaron dos tipos de modelos: LSTM (Long Short-Term Memory) y MLP (Multilayer Perceptron).<\/span><\/li>\n
  • Previsi\u00f3n de factores clave con IA<\/b>: Utilizaron la IA para predecir dos factores cruciales: cu\u00e1nto CO2 habr\u00eda en el gas de una central de carb\u00f3n y cu\u00e1nta energ\u00eda e\u00f3lica estar\u00eda disponible para alimentar el proceso de captura. Estas previsiones son importantes para planificar el proceso de captura.<\/span><\/li>\n
  • Probar y mejorar los modelos<\/b>: El equipo comprob\u00f3 rigurosamente la precisi\u00f3n de sus modelos de inteligencia artificial mediante m\u00e9todos estad\u00edsticos. Tambi\u00e9n utilizaron una t\u00e9cnica llamada predicci\u00f3n conforme para determinar hasta qu\u00e9 punto pod\u00edan confiar en las predicciones del modelo.<\/span><\/li>\n
  • Optimizaci\u00f3n del proceso de captura<\/b>: Utilizando los datos de sus modelos de IA, los investigadores emplearon un sofisticado algoritmo para encontrar el mejor equilibrio entre capturar la mayor cantidad de CO2 y utilizar la menor cantidad de energ\u00eda no renovable.<\/span><\/li>\n
  • An\u00e1lisis de los resultados<\/b>: Los resultados fueron prometedores. Demostraron que, por t\u00e9rmino medio, el reactor capturaba 16,7% m\u00e1s de CO2 y utilizaba 36,3% menos de energ\u00eda procedente de fuentes no renovables a lo largo de un mes.<\/span><\/li>\n<\/ul>\n

    Esto demuestra la utilidad de la IA en la tecnolog\u00eda de reducci\u00f3n de energ\u00eda.\u00a0<\/span><\/p>\n

    Los sistemas ligeros de IA instalados en procesos y aparatos que consumen energ\u00eda pueden optimizar su funcionalidad para reducir eficazmente el consumo de recursos. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Un estudio publicado en Reaction Chemistry and Engineering por investigadores de la Universidad de Surrey utiliz\u00f3 la IA para mejorar la captura de carbono en centrales el\u00e9ctricas, reduciendo el consumo de energ\u00eda en m\u00e1s de un tercio. La investigaci\u00f3n, dirigida por el profesor Jin Xuan, catedr\u00e1tico de Procesos Sostenibles de la Facultad de Qu\u00edmica e Ingenier\u00eda Qu\u00edmica de la Universidad de Surrey, se centr\u00f3 en la optimizaci\u00f3n de los sistemas de captura de carbono. Presenta un sistema modelo basado en una central el\u00e9ctrica de carb\u00f3n real que utiliza la IA para lograr un aumento de 16,7% en la captura de di\u00f3xido de carbono (CO2) y reducir al mismo tiempo el consumo no energ\u00e9tico de la red nacional del Reino Unido en 36,3%. El profesor Xuan destac\u00f3 la<\/p>","protected":false},"author":2,"featured_media":9301,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[522,262,105],"class_list":["post-9300","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-energy","tag-energy-consumption","tag-machine-learning"],"yoast_head":"\nResearchers use AI to reduce carbon capture energy consumption by a third | DailyAI<\/title>\n<meta name=\"description\" content=\"A study published in Reaction Chemistry and Engineering by researchers at the University of Surrey used AI to enhance carbon capture at power plants, reducing energy consumption by over a third.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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