Rate-Based Modelling of Plate-Packed Heat Integrated Distillation Columns

Rate-Based Modelling of Plate-Packed Heat Integrated Distillation Columns

Biesheuvel, Bas (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Process and Energy)

Infante Ferreira, Carlos (mentor)
van Goethem, Marco (graduation committee)

Delft University of Technology

Mechanical Engineering | Process and Energy Technology

2019-06-25

TechnipFMC is pushing innovations to reduce the emissions of ethylene plants separations. This development is also driven by customer demands. They face increasingly strict legislation on \ce{CO2} emissions. Separation in distillation columns is one of the major energy intensive unit operations in an ethylene plant, and increasing their efficiency directly improves the overall plant efficiency. This work is commissioned by TechnipFMC, which is part of the HEADLINES consortium for heat integrated distillation column development. The plate-packed heat-integrated distillation column (pp-HIDiC) is a promising new distillation column design that could replace current ethane/ethylene fractionators (\splitter{}). A numeric model and calculation tool is needed to aid the design of a pp-HIDiC for the \splitter{} as proposed in the work by \textcite{bruinsma2012}. A numeric modelling procedure for this type of column was developed in this work. This modelling procedure was then implemented in Python. A rate-based modelling methodology was applied to describe the continuous heat- and mass transfer on the internal column structures. The aim of the model is to find column dimensioning and duties, which followed from the model's balance equations. A designer should be able to input the column dimensions and flow rates, and run the model to retrieve mole fractions, heat transfer and temperatures along the column. Heat- and mass transfer correlations were needed, despite there being no special correlations for the proposed setup. Generic structured packing correlations were used from literature as a modelling approximation. The performance of different available thermodynamic packages for Python was compared. Diffusivity and viscosity correlations were implemented to aid in mass transfer calculations. A Python Newton solver was chosen to solve the model equations, using CoolProp to perform thermodynamic calculations. Due to the choice of model variables, convergence of the equations remained an issue. In the system of equations, column temperatures cannot follow from the equilibrium relation on the vapour-liquid interface, as they do not depend on each other strongly enough. It is not possible to find condenser and reboiler duties as an output of this model while specifying boil-up and reflux ratios. The model needs to be changed so that these variables are fixed by the user, and the reflux ratios are a model result instead. A model's thermodynamic calculation packages should be robust. Additionally, a well-documented interface with the used programming language and validated data is needed. This could require the use of commercial thermodynamic software, or validated process simulators. The model designer is then less hindered by programming environment related issues. A model that is purely based on theory and inapplicable correlations cannot be the only basis of design decisions. The used heat and mass transfer correlations are not made for the studied geometry, so their usage introduces inaccuracies in the model results. Applicable correlations from experiment are recommended to characterise pressure drop, heat transfer between HIDiC compartments, heat transfer coefficients, and vapour and liquid mass transfer coefficients on the packing surface. Further experiments can also aid in better understanding of the wetting, flooding and liquid distribution of various pp-HIDiC geometries. Beside characterising one packing geometry, it will be beneficial to experiment on different viable geometries that are specifically suitable for a pp-HIDiC. Too few alternative designs have been tested so far. Investigating more geometries can help to find the most promising designs more quickly. After that, models and experiments can then complement each other to develop the most promising geometries and accelerate the column development.

Distillation
HIDiC
Mass transfer
Heat Transfer
Modelling
Python
Structured packing
Design Tool
Heat integration
Energy efficiency
Cost saving
Innovation

http://resolver.tudelft.nl/uuid:81322f5e-147e-47cf-8e73-b2e7eda74db9

2020-06-25

Student theses

master thesis

© 2019 Bas Biesheuvel