Introduction to Aspen

CPE 270 Process and Product Design (Computing)

Process simulations with Aspen Plus

February 14, 2020

Important information i.

Run Aspen from your personal laptop via VPN connection.

Contact IT Services (formerly CiCS) for further help on that

ii. Additional support from GTAs

Aspen support You are welcome to contact me at any time with questions on Aspen Plus Dr Olumide Olumayegun

Room G57, Sir Robert Hadfield Building o.olumayegun@sheffield.ac.uk

“In the computer model the only side effect was a dry mouth�

Outline 1. Aim and intended learning outcome (ILOs) 2. Modelling and simulation 3. Commercial process simulators 4. Introduction to Aspen Plus 5. Property set-up and analysis in Aspen Plus 6. Simulation environment 7. Lab demonstration – computing 8. Wrapping up

1. Aim and intended learning outcomes 1.1 Aim

This session will develop students’ knowledge of process modelling and simulation and the students will understand the basics of computer simulation of chemical processes using Aspen Plus.

1. Aim and intended learning outcomes 1.2 Intended learning outcomes (ILOs): Explain the basic concepts of process modelling and simulation Configure the property environment in Aspen Plus Generate component properties in Aspen Plus Make use of experimental data to perform regression to determine missing property parameters or replace default parameters in Aspen Plus  Make use of Aspen Plus Simulation environment to build process flowsheet for the separation of acetone/water mixture  Simulate the separation process and examine the result in Aspen Plus    

2. Modelling and simulation In Chemical Engineering, a model is generally a mathematical representation of a physical and/or chemical processes.

Can you mention examples of physical and/or chemical processes?

Simulation is the use of a model to predict or study the behaviour of a process under different conditions. When you solve energy and mass balance problems, you are performing simulation without knowing it. Good news!!!

Process

Model

Simulation

2. Modelling and simulation

2.1 Why modelling and simulation?

The aim of modelling and simulation is to study and understand the behaviour of a given process under different conditions Alternatively you can perform such investigation directly in the real process.

Ever wandered how NASA put astronauts on the Moon in 1969?

Why is modelling and simulation important to you as an Engineer?

2. Modelling and simulation 2.2 Model equations Governing equations: Momentum, mass and energy balance equations. In some cases, you may need just one, two or all three of the balance equations

Supporting equations: Thermodynamic and physical property relations, reaction chemistry, correlations for predicting important parameters such as mass transfer, heat transfer etc.

The governing and supporting equations are often called the MESH equation Have you heard about MESH equations before? What does it stand for?

Material balances, Equilibrium relationships, Summation equations and Heat balances

2. Modelling and simulation 2.3 Degree of freedom (DoF)  Models comprise of a number of equations each with a certain number of variables.  The DoF is the number of variables to be specified to obtain a complete solution of the model.

DoF = Number of Unknown Variables – Number of Independent Model Equations Model equations: x = z; x+2y = 1; x-y+2z= 4 Number of unknown variables = ?

Number of independent model equations = ?

DoF = ?

Model equations: x+5y = 3; 2x-y+z= 7 Number of unknown variables = ?

Number of independent model equations = ?

DoF = ?

2. Modelling and simulation 2.4 DoF example (splitter) Fout2 Fin Fout1

The splitter comprise of three streams, Fin, Fout1 and Fout2, as shown above. Each of the streams is made up of three components, benzene, toluene and diphenyl. Let the component mass fractions in the three streams be a, b and c. There is no energy and momentum change.

2. Modelling and simulation 2.4 DoF example (splitter) Equations needed: only mass balance Global mass balance: Fin = Fout1 + Fout2

Component mass balance: a1*Fin = b1*Fout1 + c1*Fout2 a2*Fin = b2*Fout1 + c2*Fout2 a3*Fin = b3*Fout1 + c3*Fout2

Component fractions: a1+ a2 + a3 = 1

Number of independent equations = ???

b1+ b2 + b3 = 1

Number of variables = ???

c1+ c2 + c3 = 1

DoF = ???

2. Modelling and simulation 2.5 Types of models: Dynamic vs steady state model Dynamic models show the behaviour of a process over time. They are used to simulate load change scenario, shut down, start up, control design and in some cases optimization.

Steady state models do not show process behaviour over time. They are used to simulate normal operation, sensitivity analysis, design, economic analysis, optimization etc. Aspen Plus can only be used for steady state modelling and simulation.

For dynamic modelling and simulation, you should use Aspen plus dynamics (or Aspen HYSYS) which are separate applications within the Aspen toolbox

2. Modelling and simulation 2.5 Types of models: Dynamic vs steady state model

Dynamic model Fin

L

dm = Fin − Fout dt Steady state model Fout

Fin = Fout = 0

dL ρA = Fin − Fout dt

2. Modelling and simulation 2.6 Types of models: Sequential-modular vs equation-oriented

In sequential-modular models, the process units are solved in sequence starting from the feed streams. They are easy to use but recycles are tricky to solve. Aspen Plus is a typical example

In equation-oriented models, the flowsheet is solved simultaneously.

It is more complex but recycles are easier to solve. gPROMS is a typical example

2. Modelling and simulation 2.6 Types of models: First principle vs data-based First principle (or mechanistic) models are derived from the MESH equations. They are tedious to develop but could be applied to study a plant behaviour beyond the standard operating limit. Aspen Plus models falls under this category.

Data-based (or statistical) model are derived from a set of input-output data. Easy to develop but limited to the input-output data range (cannot extrapolate). Example include neural network, system identification etc.

Toolbox available in MATLAB

3. Commercial process simulators i. AspenTech Inc.:  Aspen HYSYS  Aspen PLUS iii. PSE Ltd:  gPROMS ModelBuilder v. AmsterCHEM:  COCO (Free. Download here: https://www.cocosimulator.org/index_downl oad.html)

ii. Simulation Sciences, Inc.:  PRO/II iv. ChemStations Inc.:  CHEMCAD

4. Introduction to Aspen Plus 4.1 Applications Aspen Plus can be used for:

i.

Simulation of physical and chemical processes from feed to product

ii. Thermo-physical analysis (e.g. to predict physical properties) iii. Estimate the energy requirements of a plant (steam, electricity) iv.

Size equipment (e.g. heat exchanger)

v.

Perform economic analysis

vi. Perform optimization and data fitting

4. Introduction to Aspen Plus 4.2 Lunching Aspen Plus  Locate Aspen Plus folder from Start Menu. This can either be a standalone folder or found inside AspenTech depending on your Window version  Follow as shown and Click Aspen Plus to lunch

4. Introduction to Aspen Plus 4.3 User interface

Open example models from database

Open existing Files or create a new one

4. Introduction to Aspen Plus 4.3 User interface

4. Introduction to Aspen Plus 4.3 User interface

Environments: Properties Simulation Energy analysis

4. Introduction to Aspen Plus 4.3 User interface

Properties environment: 

Select the chemical components in your process



Select calculation methods for thermodynamics and physical properties prediction



Set desired units

Simulation environment:  Select unit operations/input conditions  Stream connections to build flowsheet  Run the model  See results and perform other analysis

5. Property set-up and analysis in Aspen Plus  Properties of pure component and mixtures (Enthalpy, density, viscosity, heat capacity, thermal conductivity etc.)  PVT analysis to obtain PT, T-xy, x-y plots etc.  Phase equilibrium calculations (VLE, VLLE, LLE etc.)  Regression to obtain missing parameters for property models (e.g. binary interaction parameters) using experimental data

5. Property set-up and analysis in Aspen Plus 5.1 Property methods Property methods (also called Base methods) in Aspen Plus comprise of an Equations of State (or an Activity Coefficient model) and a collection of correlations for different properties. The property methods are usually designated by the name of the EOS or Activity Coefficient model:  Equation of state (EOS): Peng-Robinson (PR), Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK)  Activity coefficient model: UNIQUAC, UNIFAC, Non-Randon Two-Liquid (NRTL)

5. Property set-up and analysis in Aspen Plus 5.1 Property methods Ideal gas equation: pV = nRT

pv =RT

N/B: v is molar volume (m3/mol)  At fixed T (for instance), v can be calculated for different p values etc.  Ideal condition not achievable in practice, ideal gas equation modified to obtain different EOS

5. Property set-up and analysis in Aspen Plus 5.3 How to select appropriate base method: Option 1 Polar?

No

EOS

Yes Conditions near critical condition?

Yes No

Light gas or supercritical component?

No Activity coeff. model

Yes Activity coeff. model with Henry’s law

Can you mention an example of a polar and a non-polar molecule?

5. Property set-up and analysis in Aspen Plus

5.4 How to select appropriate base method: Option 2 (Method Assistant)r

6. Simulation environment

7. Lab demonstration – Computing  Case study 1 – Configure property environment, generate physical properties for components, experimental data regression for missing parameters  Exercise – Build thermophysical property environment fro nitric acid process  Case study 2 – Build and simulate process flowsheet for acetone/water mixture separation process

8. Wrapping up Today:

 Basic concepts in process modelling and simulation was introduced and defined  Aspen Plus software was introduced