Hybrid Modeling in Process Industries
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Hybrid Modeling in Process Industries

Hybrid Modeling in Process Industries

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About the Book

This title introduces the underlying theory and demonstrates practical applications in different process industries using hybrid modeling. The fundamental part covers questions on “How to develop a hybrid model?”, “How to represent and identify unknown parts?”, “How to enhance the model quality by design of experiments?” and “How to compare different hybrid models?” together with tips and good practices for the efficient development of high quality hybrid models. The application part covers the utilization of hybrid modeling for typical process operation and design applications in industries such as chemical, petrochemical, biochemical, food and pharmaceutical process engineering.

Table of Contents:
Chapter 1: Benefits and challenges of hybrid modelling in the process industries: An introduction Moritz von Stosch, Jarka Glassey 1.1 An intuitive introduction to hybrid modelling 1.2 Key-properties and challenges of hybrid modelling 1.3 Benefits and challenges of hybrid modelling in the process industries 1.4 Hybrid modelling, the idea and its history 1.5 Setting the stage Chapter 2: Hybrid Model Structures for Knowledge Integration Moritz von Stosch, Rui M.C. Portela, Rui Oliveira 2.1. Introduction 2.2. Hybrid semi-parametric model structures 2.3. Examples 2.4. Concluding remarks Chapter 3: Hybrid models and Experimental Design Moritz von Stosch 3.1. Introduction 3.2. Design of Experiments (DoE) 3.3. The Validity/Applicability Domain of hybrid models 3.4. Hybrid model based (Optimal) Experimental Design 3.5. Conclusions Chapter 4: Hybrid model identification and discrimination with practical examples from the chemical industry Schuppert and Th. Mrziglod 4.1 Introduction 4.2 Why data based modelling? 4.3 Principles of data based modelling 4.4 Structured hybrid modelling – introduction 4.5. Practical realisation of Hybrid Models 4.6 Applications 4.7. Summary Chapter 5: Hybrid modeling of biochemical processes Vytautas Galvanauskas and Rimvydas Simutis, Andreas Lübbert 5.1 Introduction 5.2 Hybrid modeling for process optimization 5.3 Hybrid modeling for state estimation 5.4 Hybrid modeling for control 5.5 Hybrid modeling for fault analysis 5.6 Concluding remarks Chapter 6: Hybrid modelling of petrochemical processes Vladimir Mahalec 6.1 Introduction 6.2 Computation of mass and energy balances 6.3 Hybrid Models of petrochemical reactors 6.4 Hybrid Models of Simple Distillation Towers 6.5 Hybrid Models of Complex Distillation Towers 6.6 Summary   Chapter 7: Implementation of hybrid neural models to predict the behaviour of food transformation and food waste valorisation processes Stefano Curcio 7.1 Introduction 7.2 Case study 1 – Convective drying of vegetables 7.3. Case study 2 – Enzymatic transesterification of waste olive oil glycerides for biodiesel production 7.4 Conclusions Chapter 8: Hybrid modelling of pharmaceutical processes and PAT Jarka Glassey 8.1 Quality by Design and Process Analytical Technologies 8.2 Case study 8.3 Conclusions


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Product Details
  • ISBN-13: 9781351184373
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Routledge
  • Language: English
  • No of Pages: 231
  • ISBN-10: 1351184377
  • Publisher Date: 21 Feb 2018
  • Binding: Digital (delivered electronically)
  • No of Pages: 219


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Hybrid Modeling in Process Industries
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Hybrid Modeling in Process Industries
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