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Process Modelling and Simulation /

Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Rahimpour, Mohammad Reza, 1961- (Editor), Makarem, Mohammad Amin (Editor), Meshksar, Maryam (Editor)
Format: eBook
Language:English
Published: Amsterdam : Cambridge, MA : Elsevier, [2024]
Series:Advances and Technology Development in Greenhouse Gases: emission, capture and conversion
Subjects:
Greenhouse gases > Mathematical models.
Greenhouse gases > Simulation methods.
Gaz à effet de serre > Méthodes de simulation.
Gaz à effet de serre > Modèles mathématiques.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • PROCESS MODELLING AND SIMULATION
  • PROCESS MODELLING AND SIMULATION
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • Reviewer acknowledgments
  • I
  • Greenhouse gases emission
  • One
  • Modeling of methane emission
  • 1 Introduction
  • 2 Methods for measuring methane
  • 3 Modeling of methane emission from landfills
  • 3.1 Simple first-order decay (TNO) model
  • 3.2 Multi-phase model of Afvalzorg
  • 3.3 GasSim model
  • 3.4 EPER11European Pollutants Emission Register. model
  • 3.5 EPER model Germany
  • 3.6 Methane content, recovery model
  • 3.7 Modeling of methane oxidation
  • 3.8 Accuracy of modeled methane emission
  • 4 Modeling of methane emission from wastewater collection and treatment systems
  • 4.1 Empiric models for predicting methane production in sewerages
  • 4.2 Modeling of methane emission in sewers
  • 5 Methane emission modeling in preparation of manure/erobic compost in particle-scale
  • 5.1 Methane emission kinetics
  • 5.2 Model of particle-scale OUR
  • 6 Removal and consumption of emitted methane
  • 7 Conclusion and future outlooks
  • Abbreviations and symbols
  • References
  • Two
  • Modeling of carbon dioxide (CO2) emissions
  • 1 Introduction
  • 2 Principles and procedures
  • 3 Processes
  • 3.1 Traditional methods
  • 3.2 Statistical models
  • 3.2.1 Autoregressive integrated moving average (ARIMA)
  • 3.2.2 Seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX)
  • 3.3 Machine learning models
  • 3.3.1 Back propagation (BP) neural network
  • 3.3.2 SVM (support vector machine) model
  • 3.3.3 Scalable random environmental impact assessment model (STIRPAT)
  • 3.3.4 Long short term memory (LSTM) model
  • 3.3.5 RF (random forest) model
  • 3.3.6 Extreme Learning Machine (ELM) model
  • 3.4 Driving force model
  • 4 Cases studies
  • 5 Conclusion and future outlooks
  • Abbreviation and symbols
  • References
  • II
  • Carbon capture techniques
  • Three
  • Process modeling and simulation of carbon capture using packed-bed and fluidized-bed absorbers
  • 1 Introduction
  • Conventional amine-based CO2 absorption
  • 3 Process improvements in absorber
  • 3.1 Enhancement of CO2 capture
  • 3.2 Reduction in energy consumption for solvent regeneration
  • 4 Economics of amine-based CO2 capture
  • 5 Carbon capture using fluidized bed absorber
  • 6 Conclusion and future directions
  • Abbreviations and symbols
  • References
  • Four
  • Modeling and simulation of carbon capture by adsorption technologies: PSA, VSA, TSA, etc
  • 1 Introduction
  • 2 Process modeling and simulation
  • 3 Solutions for developed models
  • 4 Conclusion and future outlooks
  • Abbreviations and symbols
  • References
  • Five
  • Modeling and simulation of carbon capture using polymeric membranes
  • 1 Introduction
  • 2 Theory of membrane gas separation
  • 2.1 Definitions
  • 2.2 Gas transport in polymeric membranes