rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial intelligence and machine learning in heat transfer optimization for sustainable energy systems
  name:
    name part: Heeraman, Jatoth
    role:
      role term: editor.
  name:
    name part: Barmavatu, Praveen
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (370 pages) : illustrations (black and white)
  abstract: Artificial Intelligence and Machine Learning in Heat Transfer Optimization for Sustainable Energy Systems examines how to use AI/ML-driven methodologies to enhance heat transfer processes in energy systems, such as industrial heat recovery, HVAC systems, and renewable energy generation, with a focus on sustainability.Exploring applications in sustainable energy systems, renewable resources, and smart grids, the book presents intelligent control methodologies, predictive modeling, real-time data analysis, and thermal management with deep learning. It covers AI-driven heat transfer monitoring, which is critical for a variety of applications beyond sustainability, including industrial production, aerospace, automotive, and electronics cooling. The chapters feature numerous case studies of AI/ML implementation in heat exchangers, power plants, and renewable energy systems.This book will interest researchers and graduate students studying the intersection of AI, ML, and heat transfer optimization as applied to energy systems.
  note: edited by Jatoth Heeraman, Praveen Barmavatu.
  subject:
    topic: Artificial intelligence
    topic: Industrial applications
  subject:
    topic: Heat
    topic: Transmission
  subject:
    topic: Machine learning
  subject:
    topic: Renewable energy sources
  subject:
    topic: Intelligence artificielle
    topic: Applications industrielles
  subject:
    topic: Chaleur
    topic: Transmission
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Énergies renouvelables
  subject:
    topic: heat transmission
  subject:
    topic: Artificial intelligence
    topic: Industrial applications
  subject:
    topic: Heat
    topic: Transmission
  subject:
    topic: Machine learning
  subject:
    topic: Renewable energy sources
  classification: TJ255
  classification: 621.042
  identifier: 9781040622544
  identifier: 1040622542
  identifier: 9781003647782
  identifier: 1003647782
  identifier: 104056464X
  identifier: 9781040564646
  identifier: 9781003647782 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UKMGB
    record creation date: 260119
    record change date: 20260430175737.7
    record identifier: in00005780483
    language of cataloging:
      language term: eng