{"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\/da\/2024\/01\/researchers-use-ai-to-reduce-carbon-capture-energy-consumption-by-a-third\/","title":{"rendered":"Forskere bruger AI til at reducere energiforbruget til kulstoffangst med en tredjedel"},"content":{"rendered":"

En unders\u00f8gelse offentliggjort i Reaction Chemistry and Engineering af forskere ved University of Surrey brugte AI til at forbedre kulstofopsamling p\u00e5 kraftv\u00e6rker, hvilket reducerede energiforbruget med over en tredjedel.<\/b><\/p>\n

Den forskning<\/a>Det blev ledet af professor Jin Xuan, der er professor i b\u00e6redygtige processer ved University of Surreys School of Chemistry and Chemical Engineering, og fokuserede p\u00e5 at optimere systemer til opsamling af kulstof.\u00a0<\/span><\/p>\n

Den viser et modelsystem baseret p\u00e5 et rigtigt kulfyret kraftv\u00e6rk, der bruger AI til at opn\u00e5 en stigning p\u00e5 16,7% i opsamling af kuldioxid (CO2), samtidig med at det ikke-energim\u00e6ssige forbrug fra Storbritanniens nationale net reduceres med 36,3%.<\/span><\/p>\n

Prof. Xuan understregede det nye i denne tilgang, <\/span>Det fremg\u00e5r af en artikel fra University of Surrey<\/span><\/a>\"Normalt k\u00f8rer kulstofopsamlingssystemer konstant med samme hastighed - uanset de eksterne \u00e6ndringer i milj\u00f8et. Men vi viste, at hvis man l\u00e6rer systemet at blive ved med at foretage sm\u00e5 tilpasninger, kan det give store energibesparelser - og samtidig opsamle mere kulstof.\"<\/span><\/p>\n

Kulstofopsamling er en kritisk proces til at mindske milj\u00f8p\u00e5virkningen fra CO2, den st\u00f8rste drivhusgas, der produceres af de fleste kraftv\u00e6rker.\u00a0<\/span><\/p>\n

Det sker typisk ved hj\u00e6lp af forst\u00e6rket forvitring, en metode, hvor r\u00f8ggas bobles gennem vand, der indeholder kalksten, som reagerer med kalciumkarbonat og danner harml\u00f8st bikarbonat.\u00a0<\/span><\/p>\n

Denne proces er dog energikr\u00e6vende, da der skal bruges str\u00f8m til at pumpe vand og CO2. CO2-fangstanl\u00e6gget i unders\u00f8gelsen havde sin egen vindm\u00f8lle til vedvarende energi, men i roligere vejr, hvor det ikke var tilstr\u00e6kkeligt, brugte det energi fra elnettet.\u00a0\u00a0<\/span><\/p>\n

Forskerteamets resultater ligger i at bruge kunstig intelligens til at g\u00f8re modelsystemet i stand til at forudsige udsving i CO2-produktionen og tilg\u00e6ngeligheden af vedvarende energi. Det gjorde det muligt for systemet at justere vandpumpningen i overensstemmelse hermed og optimere energiforbruget over tid.<\/span><\/p>\n

\"Maskinl\u00e6ring
Optimering af teknologier til kulstofopsamling med maskinl\u00e6ring. Kilde: Reaktionskemi og -teknik<\/a>.<\/figcaption><\/figure>\n

Dr. Lei Xing, der er lektor i kemi og kemiteknik ved University of Surrey, fremh\u00e6vede konsekvenserne af deres resultater og sagde: \"Selvom vi testede vores model p\u00e5 forbedret forvitring, g\u00e6lder principperne mere bredt. Vores model kan hj\u00e6lpe alle, der fors\u00f8ger at indfange og lagre mere CO2 med mindre energi - uanset hvilken proces de bruger.\"<\/span><\/p>\n

Forskerne h\u00e5ber, at disse resultater kan bidrage til FN's m\u00e5l for b\u00e6redygtig udvikling, som sigter mod at reducere presset p\u00e5 ikke-vedvarende energikilder og investere i innovative teknologier til kulstofindfangning.\u00a0\u00a0<\/span><\/p>\n

Tidligere i januar i \u00e5r brugte Microsoft AI til at skabe en ny elektrolyt til litiumbatterier, der kunne reducere <\/span>litiumindhold i batterier med nogle 70%<\/span><\/a>.<\/span><\/p>\n

Mere om unders\u00f8gelsen<\/span><\/h2>\n

Unders\u00f8gelsen havde til form\u00e5l at optimere kulstofindfangningen for at reducere brugen af ikke-vedvarende energikilder og samtidig bevare eller forbedre CO2-indfangningen.\u00a0<\/span><\/p>\n

Den brugte AI til at justere systemet baseret p\u00e5 sm\u00e5 \u00e6ndringer i stedet for at k\u00f8re kontinuerligt med samme hastighed.\u00a0<\/span><\/p>\n

S\u00e5dan her fungerer det:<\/span><\/p>\n

    \n
  • Oprettelse af modeller til CO2-fangst<\/b>: Forskere udviklede maskinl\u00e6ringsmodeller til at forudsige, hvor effektivt en reaktor kunne opfange CO2, og hvor meget str\u00f8m den ville bruge. De brugte to typer modeller: LSTM (Long Short-Term Memory) og MLP (Multilayer Perceptron).<\/span><\/li>\n
  • Forudsigelse af n\u00f8glefaktorer med AI<\/b>: De brugte kunstig intelligens til at forudsige to afg\u00f8rende faktorer: hvor meget CO2 der ville v\u00e6re i gassen fra et kulkraftv\u00e6rk, og hvor meget vindenergi der ville v\u00e6re til r\u00e5dighed til at drive opsamlingsprocessen. Disse prognoser er vigtige for planl\u00e6gningen af opsamlingsprocessen.<\/span><\/li>\n
  • Test og forbedring af modellerne<\/b>: Teamet testede grundigt deres AI-modeller for n\u00f8jagtighed ved hj\u00e6lp af statistiske metoder. De brugte ogs\u00e5 en teknik kaldet konform forudsigelse til at afg\u00f8re, hvor sikre de kunne v\u00e6re p\u00e5 modellens forudsigelser.<\/span><\/li>\n
  • Optimering af indfangningsprocessen<\/b>: Ved hj\u00e6lp af data fra deres AI-modeller anvendte forskerne en sofistikeret algoritme til at finde den bedste balance mellem at indfange mest mulig CO2 og bruge mindst mulig ikke-vedvarende energi.<\/span><\/li>\n
  • Analyse af resultaterne<\/b>: Resultaterne var lovende. De viste, at reaktoren i gennemsnit opfangede 16,7% mere CO2 og brugte 36,3% mindre energi fra ikke-vedvarende kilder i l\u00f8bet af en m\u00e5ned.<\/span><\/li>\n<\/ul>\n

    Dette viser AI's anvendelighed inden for energireduktionsteknologi.\u00a0<\/span><\/p>\n

    Letv\u00e6gts AI-systemer, der monteres p\u00e5 energiforbrugende processer og apparater, kan optimere deres funktionalitet for effektivt at s\u00e6nke ressourceforbruget. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

    En unders\u00f8gelse offentliggjort i Reaction Chemistry and Engineering af forskere ved University of Surrey brugte AI til at forbedre kulstofopsamling p\u00e5 kraftv\u00e6rker og reducere energiforbruget med over en tredjedel. Forskningen, som blev ledet af professor Jin Xuan, der er professor i b\u00e6redygtige processer ved University of Surreys School of Chemistry and Chemical Engineering, fokuserede p\u00e5 at optimere kulstofindfangningssystemer. Den viser et modelsystem baseret p\u00e5 et rigtigt kulfyret kraftv\u00e6rk, der bruger AI til at opn\u00e5 en stigning p\u00e5 16,7% i opsamling af kuldioxid (CO2), samtidig med at det ikke-energim\u00e6ssige forbrug fra Storbritanniens nationale net reduceres med 36,3%. Prof. Xuan understregede, at<\/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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