System Modelling & Simulation of Energy

Systems

References:

Hodge, B.K., and Taylor, R.P., 1999, Analysis and Design of Energy Systems, 3rd edition, Prentice-Hall, Inc.

Stoecker, W.F., 1989, Design of Thermal Systems, 3rd edition, McGraw-Hill.

Process Modeling and Simulation

Process modeling today is an integral part of

process development and design, operation

optimization, process control, business

evaluation and decision-making.

Modeling and simulation including computer

simulation / calculation software or program

and mathematical representations of physics

and chemistry of complex process system

have been increasingly used to assist

process development and design.

Three general approaches to modeling:

First principle approach

Functional block approach

Gray box approach

In the first principle approach, the models are

derived based on physical and chemical

laws, e.g. mass and energy balances,

thermodynamic equilibrium, chemical

reaction kinetics and mass and heat transport

phenomena.

The functional block / black box approach

The functional block is often referred as black

box approach. In this approach, the models

are derived strictly based on empirical

descriptions. In most cases a complete

process model building is not entirely

based on a single approach. A process model

built by the first principle approach usually

leaves a few parameters to be validated by

experimental or the real plant operation data.

gray box approach

A process model built by the first principle

approach usually leaves a few parameters to

be validated by experimental or the real plant

operation data.

On the other hand, the mathematical

structure of an empirical process model

should be assumed based on an

understanding of the physicochemical nature

of the process. This combined approach is

often called gray box approach.

Two types of models are:

Steady state model

Dynamic model

A steady state model reflects the process during steady state operation. Neither energy nor material accumulations with respect to time are considered in the model.

A dynamic model reflects the time transient

response of the process from one steady

state to another. Energy and / or material

accumulations with respect to time are

considered in the model.

Modeling vs. Simulation

Modeling deals with establishing physically

correct quantitative relationships between

real systems and models of those real

systems

Simulation deals with implementing the

models, usually using the computer, in such a

way that the results match those of the real

system to a high degree

Degree of validity: the extent to which

the model matches data from the real

system

Replicatively valid: it matches existing data from the

real system

Predictably valid: it can predict data outside the

range of parameters of the original database

Structurally valid: it truly reflects how the real system

operates

All simulations should be validated using

experimental data.

Purpose of Simulation

Simulations can have a wide range of purposes. They

may include

Predicting off-design performance of existing systems

to identify and mitigate possible problems

Optimizing the efficiency of a system during the design

process to decrease energy costs

Determining how making a modification in one part of

an existing system will affect the rest of the system.

Once validated with experimental data, simulations

can save a lot of time and money – they’re a lot

cheaper and faster than running experiments!

Classes of Simulations

Continuous vs. Discrete

Continuous: flow through a system is continuous, like fluid flow

Discrete: flow is treated as a certain number of discrete integers, such as number of people

Deterministic vs. Stochastic

Deterministic: input parameters are known and precisely specified

Stochastic: input parameters are uncertain. They may be determined randomly or using a probability distribution, for instance.

Steady State vs. Transient (Dynamic)

Most of our problems will be continuous, deterministic, and steady state

Developing accurate models and

simulations

Several areas must be looked at closely to

develop an accurate simulation

Physical bases

Levels of the component models

Accuracy

Validation procedure

Physical bases

If the component models don’t represent the correct

physics, the model will not give accurate results or you

will not be able to use the model beyond a very limited

range

How do the individual components act?

Do your mathematical equations accurately predict

performance?

Do you understand how the different components

interact?

Make sure that you include the effects that the system

may have on the component performance.

Levels of Component Model

The higher the level of the model, the more

details are captured. For example, think of a

compressor model

Level 1 might be doing a simple analysis like

done as homework problems for an

undergraduate thermodynamics class.

Level 2 might be the model that one

developes for more parameters.

Levels of Component Model

Level 3 might be a detailed transient finite

difference computer analysis of the fluid

dynamics inside the compressor.

Higher level models, if done correctly, are

more accurate and model the true situation

better.

However, you pay a price with increased

computation time and increased personal

time to develop them.

Accuracy

Make sure that you clearly understand the assumptions being made and how they affect the accuracy of the results.

Choose a simulation level consistent with your desired accuracy.

You may have no need for a sophisticated finite difference model.

Use a similar simulation level for each component in your system unless you have a good reason not to.

Accuracy

Your system accuracy might be dominated by the component modeled with the lowest-level model. In that case, there’s no reason to use a higher-level model for other components.

Performing a sensitivity analysis may help us determine how good the model of a certain component should be.

If “y” is the desired output and “x” the result of an individual component model, vary “x” and see how much ”y” changes. If changing “x” has little effect on “y”, then that component doesn’t

need a very sophisticated model.

Validation

This includes two steps: validating the individual

component models and the entire system

simulation.

Make sure that your simulation can reproduce

existing experimental data.

Be careful about running your simulation for

parameters outside the range of validation. At times

you may need to, but realize that you’re increasing

the uncertainty of your results.

Who Should do Simulations

Computer scientists and computer engineers may be

able to assist in transforming your model into a

computer simulation

However, they will not understand the physics that

you’re trying to model, how components interact, the

required accuracy, what assumptions are OK, etc.

Therefore, it is important that system simulations be

developed by the people who are experts in that

particular area of engineering.

Systems of Simultaneous Equations

Many simulations require solving systems of

simultaneous equations.

Finite difference methods

Using EES for a refrigerator model, the compressor

performance depends on the evaporator, which

depends on the expansion valve, which depends on

the condenser, which depends on the compressor –

they’re all linked together. Two methods of

simulation include Successive Substitution and

Newton -Raphson

ENERGY CONVERSION PLANT

ENERGY CONVERSION PLANT

Dynamic Life Cycle Assessment of biogas

production from micro-algae

Because of their high production yield, micro-algae

have been pointed as an interesting biofuel.

A relevant mean to upgrade the energy value of

micro-algae with optimal performances is the

anaerobic digestion of the algae. It enables

achievement of environmental benefits and

production of energy from renewable resources.

However such processes only exist at lab-scale.

Continued...

In order to assess and optimize its performances

and environmental impacts, one has to simulate its

behaviour through dynamical models.

In broad outline the two major compartments of the

system (micro-algae culture and anaerobic digestion

process) are linked by internal flows (micro-algae,

digestates…) and receive external flows (light,

cosubstrates…). As a consequence, overall

behaviour is determined by the interaction of several

time-dependent processes.

Biogas production from micro-algae

We integrate dynamic

system modeling of

micro-algae growth and

anaerobic digestion of

biomass in the LCA in

order to obtain dynamic

flows.

Continued...

A pertinent Life Cycle Inventory can not be achieved

without taking into account the dynamic of several

processes; some economic flows are determined

according to the temporal evolution of processes.

Consequently, we integrate dynamic system

modeling of micro-algae growth and anaerobic

digestion of biomass in the LCA in order to obtain

dynamic flows.

This approach allows us to obtain dynamic data for

the Life Cycle Inventory. This is a preliminary step to

more accurate impact assessment.

Wind Energy Conversion System

The Modelling Process

Real world

problem

Mathematical

problem

Mathematical

solution Interpretation

1

2

3

4

Model Application Areas

Process design

Process control and diagnosis

Troubleshooting

Process safety

Operator training

Environmental impact assessment