[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","2026-02-10T09:04:39.098Z","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","system","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzUzODM","title","Building AI-Driven Digital Twins for the Process Industry","state","published","contactAuthorMethod","email","aboutText","

Why settle for a model that works only on paper?

In 2026, the gap between \"Simulation\" and \"Reality\" is the biggest hurdle in process optimization. Most engineers are trained in \"Clean\" simulations (Aspen Plus, HYSYS), but real plants are messy.

This workshop report, designed by Dr. Kamal Al-Malah, bridges that gap. You won't just learn AI; you will learn Physics-Informed AI. By the end of this course, you will have the tools to build a virtual instrument that \"sees\" through sensor noise to predict equipment failure before it happens.

What’s Included:

Theory-to-Code Audit: Mathematical mapping of FUG and Kern-Seaton models.
GPR Implementation: Using Gaussian Process Regression as a physics-informed filter.
Interactive MATLAB Tools: Source code for the C3 Splitter Mini-Simulator.

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