{"id":9192,"date":"2023-10-25T06:48:00","date_gmt":"2023-10-25T06:48:00","guid":{"rendered":"https:\/\/www.gradiant.com\/?post_type=success-stories&p=9192"},"modified":"2026-04-03T16:11:44","modified_gmt":"2026-04-03T20:11:44","slug":"smartops-ai-desal-optimization","status":"publish","type":"success-stories","link":"https:\/\/www.gradiant.com\/de\/success-stories\/smartops-ai-desal-optimization\/","title":{"rendered":"SmartOps AI - Optimierung der Entsalzung"},"content":{"rendered":"
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SmartOps AI<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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MASCHINELLES LERNEN SPART ENERGIE\nF\u00dcR GROSSE MEERWASSER-UMKEHROSMOSE (SWRO) IM NAHEN OSTEN<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Turing, ein Unternehmen von Gradiant, konnte mithilfe von maschinellem Lernen seine patentierte SmartOps-Technologie anwenden, um in einer gro\u00dfen Meerwasser-Umkehrosmoseanlage im Nahen Osten unter Einhaltung der strengen Auslegungsgrenzen bis zu 5 % Energie einzusparen, was Hunderttausenden von Dollar entspricht. Zur Best\u00e4tigung der Testergebnisse wurde ein internationales Protokoll zur Leistungsmessung und -\u00fcberpr\u00fcfung angewendet.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Kurz & Knapp<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Standort<\/strong>: Naher Osten<\/p>

Endverbraucher<\/strong>: ENGIE<\/p>

L\u00f6sung<\/strong>: Meerwasserentsalzung<\/p>

Industrie<\/strong>: Kommunales & Infrastruktur<\/p>

Speisewasserquelle<\/strong>: Meerwasser<\/p>

Technologie<\/strong>: SmartOps AI<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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System-Konfiguration:<\/strong>25 + 1 Z\u00fcge, Peltonrad<\/p>

Kapazit\u00e4t des Systems<\/strong>: > 200.000 m\u00b3\/Tag<\/p>

Online Datum<\/strong>: 2021<\/p>

Liefermodell<\/strong>: Software-as-a-Service (SaaS)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Die Herausforderung<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Unser Kunde ENGIE, ein franz\u00f6sischer multinationaler Stromversorger, stand vor der Herausforderung eines hohen Energieverbrauchs in einer gro\u00df angelegten Meerwasser-Umkehrosmose-Anlage (SWRO) im Nahen Osten. Die Entsalzung von Meerwasser ist aus einigen wichtigen Gr\u00fcnden energieintensiv. Die Entsalzung erfordert in der Regel eine betr\u00e4chtliche Menge an Energie, um das Meerwasser zu pumpen, unter Druck zu setzen und durch Filtrations- oder Membransysteme zu leiten, um Salz zu entfernen. Die Energiekosten tragen wesentlich zu den Gesamtkosten der Entsalzung bei. Die Optimierung der Leistung mehrerer Umkehrosmoseanlagen stellt f\u00fcr die Betreiber eine gro\u00dfe Herausforderung dar, da es praktisch unm\u00f6glich ist, die besten Betriebsparameter und die H\u00e4ufigkeit des Membranwechsels zu bestimmen, um die Energieeinsparungen zu maximieren.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Die L\u00f6sung<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Die SmartOps-Technologie von Gradiant ist eine leistungsstarke Softwareplattform, die maschinelles Lernen zur Optimierung der Anlagenleistung und zur effizienten Verwaltung von Anlagen einsetzt. Die Technologie ber\u00fccksichtigt verschiedene Input- und Output-Parameter und stellt sicher, dass die Anlagen die Betriebsnormen und mechanischen Einschr\u00e4nkungen einhalten, w\u00e4hrend der Energieverbrauch gesenkt und die Wasserqualit\u00e4t und die Produktionsziele eingehalten werden. Sie ist besonders effektiv bei der Optimierung des Wasserflusses in Umkehrosmoseanlagen mit unterschiedlichen Wirkungsgraden.<\/p>

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Durch die Nutzung der Leistungsf\u00e4higkeit von KI-Algorithmen f\u00fcr maschinelles Lernen kann SmartOps den Anlagenbetrieb verbessern und vorhersagen, was zu unmittelbaren Produktivit\u00e4tssteigerungen, Kosteneinsparungen und einer Verringerung des Kohlenstoff- und Wasserverbrauchs f\u00fchrt.<\/p>

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Ein Audit historischer Daten zeigte, dass die SmartOps-Digitaltechnologie Energieeinsparungen von bis zu 5 % erzielen kann, mit einer maximalen R\u00fcckgewinnungsrate von 48 % f\u00fcr eine Anlage, die ein Peltonrad und ein manuelles Einspeiseregelventil verwendet.<\/p>

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Um diese Energieeinsparungen zu \u00fcberpr\u00fcfen, setzte Gradiant Turing ein Simulationsmodell auf der Grundlage des ISO International Performance Measurement and Verification Protocol (IPMVP\u00ae) ein, um vorherzusagen, wie hoch der Energieverbrauch unter Standardbedingungen gewesen w\u00e4re. Damit wurden die Energieeinsparungen gegen\u00fcber dem Kunden best\u00e4tigt.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Die Vorteile<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Das maschinelle Lernen ist ein entscheidender Faktor f\u00fcr Anlagenbesitzer und -betreiber, denn es hilft ihnen, Systeme effizient zu steuern und Entscheidungen auf der Grundlage von Daten zu treffen. Einfache t\u00e4gliche Einstellungsempfehlungen erm\u00f6glichen es den Betreibern, die besten Werte einzugeben, sodass sie sich darauf konzentrieren k\u00f6nnen, den 600.000 Endverbrauchern der ENGIE-Anlage qualitativ hochwertiges Wasser zu liefern. Um diese Einsparungen zu erzielen, sind keine zus\u00e4tzlichen Investitionen in Ger\u00e4te oder Hardware erforderlich.<\/p>

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Die Gro\u00dfanlage (SWRO) von ENGIE ist nach wie vor f\u00fchrend bei der Einf\u00fchrung der Technologie des maschinellen Lernens. Ihr Erfolg ist zum Modell f\u00fcr viele Anlagen weltweit geworden, so dass sie diese Vorteile nachahmen k\u00f6nnen. Durch die Integration des maschinellen Lernens wurde nicht nur viel Energie eingespart, sondern auch die Systemsteuerung und die datengest\u00fctzte Entscheidungsfindung verbessert, wovon sowohl die Anlagenbetreiber als auch die Manager profitieren.<\/p>

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Die SmartOps Digital-Technologie von Gradiant revolutioniert weiterhin die Meerwasserentsalzungsbranche und weist anderen Anlagen auf der ganzen Welt den Weg, um Energie zu sparen und \u00e4hnliche Vorteile zu erzielen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Auswirkungsstatistiken<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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5%<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Energieeinsparung<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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48%<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Maximale<\/p>

R\u00fcckgewinnungsrate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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> 200.000 m<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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,<\/sup><\/p>\t\t\t\t<\/div>\n\t\t\t\t

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T\u00e4gliche Systemkapazit\u00e4t<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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< 5 %<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Differenzdruck-<\/p>

anstieg<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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99,95 %<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Betriebszeit<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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12 M<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Tonnen Co<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t2<\/sub>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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  j\u00e4hrlich eingespart<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Rechtlicher Hinweis<\/strong><\/p>

Dieses Dokument dient nur der allgemeinen Information. Es wird keinerlei Garantie oder Gew\u00e4hrleistung gegeben oder impliziert, und Gradiant ist nicht an die hierin enthaltenen Informationen gebunden und haftet nicht daf\u00fcr. Es liegt in der alleinigen Verantwortung des Kunden, festzustellen, ob die in diesem Dokument enthaltenen Informationen f\u00fcr die Verwendung durch den Kunden geeignet sind, einschlie\u00dflich und ohne Einschr\u00e4nkung der tats\u00e4chlichen Standort-, geografischen und Werksbedingungen, Spezifikationen, Anforderungen, Entsorgung, geltenden Gesetze und Vorschriften. Dieses Dokument ist geistiges Eigentum von Gradiant, einschlie\u00dflich, aber nicht beschr\u00e4nkt auf die in diesem Dokument enthaltenen Patente und Marken. Die Verteilung dieses Dokuments bedeutet nicht, dass das geistige Eigentum von Gradiant \u00fcbertragen wird.<\/p>

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Gradiant, das Gradiant Logo und alle mit \u2122 oder \u00ae gekennzeichneten Handels- und Dienstleistungsmarken sind, sofern nicht anders angegeben, Eigentum von Tochtergesellschaften der Gradiant Corporation. \u00a9 2023 Gradiant.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

SmartOps AI download pdf MACHINE LEARNING SAVES ENERGY FOR LARGE SWRO IN THE MIDDLE EAST Turing, a Gradiant company, was able to use machine learning to apply its patented SmartOps technology to save up to 5% energy, the equivalent of hundreds of thousands of dollars, at a large seawater reverse osmosis plant in the Middle […]<\/p>\n","protected":false},"featured_media":9464,"template":"","class_list":["post-9192","success-stories","type-success-stories","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nSmartOps AI \u2013 Desal Optimization - Gradiant<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.gradiant.com\/de\/erfolgsgeschichten\/smartops-ai-desal-optimization\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SmartOps AI \u2013 Desal Optimization - 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