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Applications of Molecular Simulation in the Oil and Gas Industry - P. Ungerer

Book "Applications of Molecular Simulation in the Oil and Gas Industry"

Applications of Molecular Simulation in the Oil and Gas Industry - Monte-Carlo Methods

Philippe UNGERER, Bernard TAVITIAN, Anne BOUTIN (CNRS)

Molecular simulation is an emerging technology for determining the properties of many systems that are of interest to the oil and gas industry, and more generally to the chemical industry. Based on a universally accepted theoretical background, molecular simulation accounts for the precise structure of molecules in evaluating their interactions. Taking advantage of the availability of powerful computers at moderate cost, molecular simulation is now providing reliable predictions in many cases where classical methods (such as equations of state or group contribution methods) have limited prediction capabilities. This is particularly useful for designing processes involving toxic components, extreme pressure conditions, or adsorption selectivity in microporous adsorbents. Molecular simulation moreover provides a detailed understanding of system behaviour. As illustrated by their award from the American Institute of Chemical Engineers for the best overall performance at the Fluid Simulation Challenge 2004, the authors are recognized experts in Monte Carlo simulation techniques, which they use to address equilibrium properties. This book presents these techniques in sufficient detail for readers to understand how simulation works, and describes many applications for industrially relevant problems. The book is primarily dedicated to chemical engineers who are not yet conversant with molecular simulation techniques. In addition, specialists in molecular simulation will be interested in the large scope of applications presented (including fluid properties, fluid phase equilibria, adsorption in zeolites, etc.).

TABLE OF CONTENTS

 

Foreword by François Montel

V

Acknowledgements

XI

 

Chapter 1

INTRODUCTION

1

   

Chapter 2

BASICS OF MOLECULAR SIMULATION

7

2.1

Statistical Thermodynamics

7

2.1.1 Statistical Ensembles and Partition Functions
2.1.2 Determination of Average Properties
2.1.3 Determination of Derivative Properties from Fluctuations
2.1.4 Possible Ways of Simulating Ensembles

2.2

Potential Energy of Molecular Systems

19

2.2.1 Standard Decomposition of the Potential Energy
2.2.2 Electrostatic Energy
2.2.3 Polarisation Energy
2.2.4 Dispersion and Repulsive Energy
2.2.5 Internal Energy
2.2.6 All Atoms vs United Atoms

2.3

Monte Carlo Simulation Principles

34

2.3.1 Basic Principle
2.3.2 Standard Monte Carlo Moves Involving a Single Box
2.3.3 Insertion and Destruction Moves in the Grand Canonical ensemble
2.3.4 Moves Specific to the Gibbs ensemble
2.3.5 Evaluation of the Chemical Potential
2.3.6 Statistical Bias Monte Carlo Moves
2.3.7 Determination of Bubble Points and Dew Points
2.3.8 Thermodynamic Integration
2.3.9 Parallel Tempering

2.4

Practical Implementation

54

2.4.1 What is Exactly a Simulation Box?
2.4.2 Modelling Microporous Adsorbents
2.4.3 What Type of Potential to Use?
2.4.4 Optimisation of the Intermolecular Potential
2.4.5 Selection of Numerical Parameters
2.4.6 Selection of System Size and Initial Conditions
2.4.7 Convergence and Statistical Uncertainties
2.4.8 Calculation of Thermodynamic Properties
2.4.9 Computer Hardware and Software Considerations

   

Chapter 3

FLUID PHASE EQUILIBRIA AND FLUID PROPERTIES

87

3.1

Predicting the Properties of Pure Hydrocarbons

88

3.1.1 General Strategy
3.1.2 Predicting the Properties of Linear Alkanes
3.1.3 Branched Alkanes
3.1.4 Cyclic Alkanes
3.1.5 Olefins
3.1.6 Aromatics
3.1.7 Perspectives

 

3.2

Thermodynamic Derivative Properties of Light Hydrocarbons

115

3.2.1 Predictions at High Pressure
3.2.2 Prediction of Derivative Properties in Near-critical Conditions

 

3.3

Properties of Polar Organic Compounds

129

3.3.1 Organic Sulphides and Thiols
3.3.2 Organo-Mercuric Compounds
3.3.3 Ketones and Aldehydes
3.3.4 Alcohols

 

3.4

 

Picto PDF
Phase Behaviour of Mixtures (

PDF - 880 Ko

..)

144

3.4.1 Binary and Ternary Alkane Mixtures
3.4.2 Binary Mixtures of H2S with Liquid Hydrocarbons
3.4.3 Phase Equilibria of CO2 with Alkanes and Polyethylene
3.4.4 Phase Equilibria Involving Methanol

 

3.5

Picto PDF

162

3.5.1 Possible Contribution of Molecular Simulation to Industrial Needs
3.5.2 Representation of Natural Gas Composition in Monte Carlo Simulation
3.5.3 Volumetric Properties
3.5.4 Joule-Thomson Coefficient and Derivative Properties

 

3.6

Thermodynamic Properties of Acid Gases at High Pressure

175

3.6.1 Intermolecular Potential for CH4, Water, CO2 and H2S
3.6.2 Phase Behaviour of the H2S–CH4–H2O System
3.6.3 Volumetric Properties
3.6.4 Prediction of Excess Enthalpies
3.6.5 Prediction of Derivative Properties

Chapter 4

ADSORPTION

195

4.1

A Practical Example of Grand Canonical Monte Carlo Simulation of Adsorption

196

4.1.1 Construction of the System: the Solid
4.1.2 Calculation of the Energy Grids
4.1.3 Running a Grand Canonical Simulation
4.1.4 Computation of Heats of Adsorption

4.2

Adsorption of C8 Aromatics and Water in Faujasite Type Zeolites

203

4.2.1 Cation Distribution vs Si/Al Ratio
4.2.2 Adsorption Selectivity of Metaxylene vs Orthoxylene
4.2.3 Adsorption of Water in Faujasites
4.2.4 Co-adsorption of Water and Xylenes in NaY Faujasite

4.3

Optimisation of Interaction Parameters Specific to Zeolites

223

4.4

Adsorption Isotherms and Selectivities of Hydrocarbons on Silicalite

226

4.4.1 Linear Alkanes
4.4.2 Branched Alkanes
4.4.3 Isotherm Fit Using the Langmuir Formalism
4.4.4 Heats of Adsorption
4.4.5 Adsorption of Alkenes in Silicalite
4.4.6 Binary Mixture Coadsorption Isotherms
4.4.7 Separation of Branched Alkanes on Faujasite Type Zeolites

4.5

Separation of Thiols from Natural Gas on Faujasites

258

4.5.1 Adsorption Isotherms of Alkanethiols
4.5.2 Coadsorption of Alkanethiols with Other Components of Natural Gases

Chapter 5

CONCLUSION AND PERSPECTIVES

263

   

APPENDIX

A.1

Parameters of the Anisotropic United Atoms Potential

267

A.2

Implementation of Monte Carlo Moves with the Anisotropic United Atoms Model

270

A.2.1 Translation, Rotation, Volume Changes
A.2.2 Flip, Pivot
A.2.3 CBMC Moves
A.2.4 Reservoir Bias

References

277

Index

291

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