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{ "item_title" : "Methods of Model Based Process Control", "item_author" : [" R. Berber "], "item_description" : "Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their explorationof the latest trends in the area. ", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/79/233/524/0792335244_b.jpg", "price_data" : { "retail_price" : "549.99", "online_price" : "549.99", "our_price" : "549.99", "club_price" : "549.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Methods of Model Based Process Control|R. Berber

Methods of Model Based Process Control

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Overview

Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges.
Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their explorationof the latest trends in the area.

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Details

  • ISBN-13: 9780792335245
  • ISBN-10: 0792335244
  • Publisher: Springer
  • Publish Date: May 1995
  • Dimensions: 9.21 x 6.14 x 1.75 inches
  • Shipping Weight: 2.97 pounds
  • Page Count: 826

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