And thank you for joining the chemical and

process industry

booth

Now our booth actually because it's so generic

about chemical

and process industry in general we actually

have compiled four

different presentations

that are on different topics

so the first one will be focused on mixing

equipment

the second one will be focused on heat

exchanger design

and development the third presentation will

be focused on environmental

pollution control and then the 4fourth and last

presentation but

certainly not least will be focused on gas

liquid equipment

and what I'm trying to do within the next

let's

say 25 minutes or so is give you a high

level overview of the wealth of information

that we have

compiled in those four presentations but I

definitely encourage you

to both download the PDF file as well as

watch the other videos in details

for

all these four different presentations and to

be able to

find more information and so if I go back to

the mixing

equipment presentation best practices I will

try to go to

highlight again the key aspects of this

presentation and keep

in mind that the other three presentations on

heat exchangers

on environmental control and on gas liquid

systems do follow

pretty much the same layout and I'm again

going to

highlight the key applications over there so

with that set

the let's focus on our first presentation

here which is

targeting mixing equipment

Now in terms of challenges that you know face

people

in this industry when we're dealing with how

to operate

mixing equipment in general

Things like scale up and actually scaled down

are very

common issues and in in simple words scale up

means

once you have optimized a lap scale or a bench

scale

mixing process how can you take this

production level how

you can take it from the liters length scale

to

thousands of liters length scale and still

maintain the proper

mixing behavior that you have tested in the

lab the

opposite is also sometimes with interest

which is the scale

down process and potentially you may have a

large mixing

vessel or a large mixing setup that is working

properly and

potentially you may have other scenarios

where you want to

be able to operate smaller versions of the

equipment maybe

that will allow you to customize and come up

with

newer products maybe it provides you with

additional control that

you don't have on the larger scale and things

like

that so both scale up and scale down are

actually

quite common scenarios that people are

interested in and it's

always about yield how to

increase the productivity

of existing equipment how can you actually

reduce the time

to market and potentially even introduce new

products that's very

common challenges and other things that are

also worth mentioning

here is While doing experimentation trying to

come up with

the optimized mixing process regardless of

the scale

people always keep a close eye on for example

material

costs depending on the material that you are

working with

you know again while you are doing

experiments this is

lost material this is lost material so you

want to

reduce that for example in the extreme case

of in

the pharma industry the APIs active

pharmaceutical ingredient can actually

in many cases be more expensive than gold

itself so

you can imagine any loss batch me may be

quite

painful actually to lose the other thing also

is when

doing experimentation in many cases you may

actually have to

wait for the availability of lab space to

actually do

your own experiments so and that can in some

cases

actually cause weeks of delay Another very

common challenge actually

is when running these equipment mixing

equipment

obviously you may end up running

different materials

in different times and while doing this

change over of

materials you want to make sure that the

equipment is

cleansed properly or clean properly before

the new batch is

introduced so then the question is how long

is this

change overtime is right and also how

efficient is the

cleaning process because again change overtime

is time that

basically is lost right but you still have

to go

through it when you are changing from

material A to

material B other challenges that again people

are always looking

at are

power consumption again these depending on

the size of the

mixing tank power consumption may actually be

a substantial part

of your operating costs and then again

You always are dealing with an entire system

it's not

just the mixing tank on its own so how are

the different components of different

ingredients of the system actually

are interacting with each other and are these

individual components

properly sized with respect to each other or

not and

then last but not least potentially looking

into designing and

testing a control system

for

the operating for the actual operation and

potentially how you

can commission the system before you actually

move to full

scale production so these are just general

challenges now the fact that we're all here

and talking

obviously acknowledges that we all appreciate

what simulation is able

to provide for mixing analysis in this

particular case and

these are just some general numbers that we

have compiled

to quantify the benefit and the

impact of simulation

on mixing equipment utilization so for

example here we did

an internal study that actually proved that

just a 1%

just the 1% increase in concentration of a

particular mixing

process may yield to millions of dollars

revenue per

batch

Another study actually which was published

which is published right

here you can actually click on that link and

actually

go to the details that there was actually

40%

reduction in culture development time that's

actually possible And last

but not least here is through reduced order

modeling technology

we are able to cut down the time for your

general simulation by orders of magnitude

again using reduced order

modeling technology which again we are going

to revisit throughout

this conference

Now going deeper into the mixing side of

things so

we have here multiple or several simulation

scenarios or representing

different problems that simulation has been

used into so first

we're going to very quickly talk about single

phase mixing

then

gas liquid systems and bio reactors

solid liquid systems and solid for example

which are relevant

in solid dissolution systems we'll also

quickly look into intermeshing

mixers where you may potentially have mixers

that are actually

intermeshing as they are mixing the material

quickly highlight static

mixers and where they can be used

I did mention the clean in place process and

then towards

the end we will again highlight the concept

of simulation

democratization and system level modeling

So very quickly here when it comes to single

phase

mixing usually this is the first place where

everybody starts

because it is very well documented it is the

easiest

to deploy and actually it provides a wealth

of information

already that is very useful for people so we

did

mention about the engineering challenges

whether it scale up or

scale down but specifically people are

interested in looking into

residence time and blend time estimates at

different scales for

sure looking at shear exposure especially if

you are dealing

with biological cells that are highly

sensitive to shear

That's again of interest and then potentially

if you are

in the consumer products food or chemicals

industry in general

you may be dealing with complex rheology

complex rheological material

for example non newtonian materials and how

you are able

to handle those so in terms of the Ansys

capabilities

again everybody is fully aware of both steady

state and

transient flow modeling but in addition to

that being also

able to capture multi species or scalars as

well as

particular flows and represent complex

rheology is a must when

you are interested in looking into these kind

of systems

and as an example

You will be able to get several outputs like

in

addition to the typical flow field

visualization which we can

see here on this slide but we can also look

into quantifiably quantifying things like

residence time and blend time

reports quantifying again shear rate exposure

for these biological cells

again in a quantitative as well as

qualitative sense and

we can also provide detailed mixing reports

The other thing is as another step people are

also

interested in looking at bio reactors and gas

liquid systems

where in addition to your typical challenges

for single phase

systems there we can be looking at gas holdup

problems

and making sure that there is no gas there is

no gas short circuiting and because that will

certainly enhance

the mass transfer and how or better or in

other words how can we predict the mass

transfer between

the gas and the liquid how can we predict if

there are excessive vortexing that is

occurring in the mixing

vessel which is not desirable because that

will create a

gas entrapment

And again as usual we always are interested

in looking

at shear rate and again as I mentioned

earlier in

terms of capabilities in the additional part

here is being

able to model multiphase flow including

potentially bubble size distribution

because that will directly impact your mass

transfer projection and

this would be for example a key here output

of

a similar analysis both bubble size

distribution bubble residence time

mass transfer between the gas and the liquid

and again

as we mentioned earlier shear rate prediction

The same can be said for solid liquid systems

and

imagine you have solid particles not gas now

so that

particles in a liquid matrix and you're

interested in again

scaling up or down the system but being able

to

properly predict the solid suspension and

cloud height for these

systems and these systems are very common in

dissolution systems

as well as crystal growth systems and there

the capabilities that

Ansys can provide there is in addition to

everything that

was mentioned earlier but also being able to

contain to

include multi species and particulate flow

in details potentially with

chemical reaction and with complex rheology

at the same time

and the main outputs that people are

interested in here

is being able to predict the dissolution mass

transfer as

well as dissolution rate prediction and also

potentially if you

have crystals the crystal growth rate

Mixing things can be quite complex

potentially with intermeshing blades

and potentially also with rather complex

motion and the objectives

there are very similar but usually there the

rheology is

typically more complex more viscous

potentially with more

complex impeller motion or blade motion

and the

Ansys capabilities required here would be

to be able to

actually model these Rather complex

intermeshing regions and there

are several approaches that Ansys provides

there be it overset

mesh method or be it sliding mesh or be it mesh

superposition

technique which again we will actually

mention in our presentation

in more details and here in terms of output

there are several things that are of interest

making sure

that the impeller motion is proper ensuring

or looking overall

visualizing the flow field and

identifying dead spots as

well as reporting distributive mixing being

able to properly calculate

the residence time distribution and blend

time shear rate is

always of importance because that will

directly impact dispersive mixing

which is essentially the process of breaking

up either droplets

or even solid particles into smaller either

droplets or particles

and again being able to do all that both in

a qualitative sense visually as well as

quantitative sense being

able to report exact for example share rate

histogram or

residence time histogram and things like that

And static mixers are also of interest so

here we

don't have any impellers per se but there

the challenges

are ensuring that the mixer is properly sized

to just

do enough mixing while not excessively

increasing for example the

pressure drop or increasing the amount of

viscous heating or

friction that the fluid or the liquid

experiences potentially causing

material degradation And usually in these

systems we do need

to have a complex rheology capability which

Ansys capabilities include

we definitely need to be able to handle very

complex

geometries for these mixers including

potentially parameterizing these geometries to

run what-if scenarios for different mixer

shapes or sizes

and our typical outputs is potentially the

concentration variation or

basically visualizing the mixing process

looking again things like residence

time and potentially even local temperature

distribution due to viscous

heating for example because of these systems

caused quite a

bit of shear rate through them

and as

we mentioned earlier pressure drop really

reflects the operating

cost that you actually have to incur while

pumping the

material through these mixing equipment

The other thing that also is of

interest

that it will be covered in this presentation

in more

details is the clean in place process and the

challenges are

being able to ensure that the cleaning is

sufficient and

consistent minimize the cleaning time

minimize the cleaning agent use

and also minimize the power consumption

during the cleaning process

and to be able to do that we need to

have Ansys provides actually multiple

capabilities be the

volume of fluid full multi phase model or

discrete phase

model as well as being able to model

potentially very

complex nozzle motion and rotation

And at the end being able to report the

how the jet is actually how the cleaning

jet

is actually forming what is the impact of the

jet

on the cleaning surface how long does it take

to

complete the entire cleaning process how much

liquid you are

consuming and potentially do what if studies

for different nozzle

designs

And then the last thing actually that the

mixing presentation

will actually talk about into more details is

being able

to democratize the mixing analysis through

the use of template

which is very powerful way to enable non

experts for

utilizing the full benefit of 3D

computational fluid dynamics analysis

so that's actually key and then the other

thing is

being able to convert 3D CFD results into

reduced order

models that can be

used in real time potentially for operator

training as well

as digital commissioning and again this is

actually something that

would be very nicely highlighted during this

presentation OK now

this is actually just a quick overview on the

mixing

side so let me quickly switch gears to the

heat

exchangers very quickly and what I'm going to

do here

is the heat exchanger presentation discusses

some very interesting key

studies about optimization in general and how

to ensure flow

uniformity through feeding manifold over heat

exchanger

optimization as well for specific geometrical

details inside the cubes

for example or to really enhance the heat

transfer while

reducing the pressure drop and then the

concept of conjugate

heat transfer in general this is actually key

requirements to

be able to model conduct transfer in

accurately for

the design and troubleshooting and

optimization of heat exchangers

Potentially some of these heat exchangers may

experience fatigue thermal

fatigue to be specific if they are

continuously subjected to

both cold and hot cycles so that there's

actually some

nice key studies over there many heat

exchangers are experiencing

multi phase flow or phase change and being

able to

do that be it boiling or condensation may be

of

interest depending on the application and

then just as we

did for the mixing analysis always looking at

bridging the

gap between 3D component level simulation and

0D system level

simulation is always of interest how can you

convert detailed

3D CFD simulation into a reduced order model

that can

be easily consumed either on its own or as an

integral part of a system so for example for

example

just again I'm not going to go through all

the

breakout topics here for the interest of time

but just

to give you an idea So here this is a

nice case study which will be highlighted

later on in

terms of optimal shape optimization in

general and actually there

are several ways to optimize the shape so for

example

here this is a manifold and optimization if

you think

about it can either be CAD based or

parametric based

or freeform where you can actually morph the

geometry and

this is actually what was implemented here so

the

fluent adjoint solver in this particular case

was actually used

to morph the geometry to ensure flow

uniformity that is