[How To] Perform & Evaluate DSC(Differential Scanning Calorimetry) study

Hiii all....!!
Good day everyone, hope everything is going good.

After a gap, back with a most waited post i.e., DSC study generation & evaluation.

I think everyone is well aware of this and what importance it holds during detailing of safety aspects prior product manufacturing.

DSC is abbreviated Differential Scanning Calorimetry.




Most of the small scale pharmaceutical companies wont spare time for generating and evaluating these and it will results in large scale disasters. Whenever a manufacturing process is developed and taken into site for execution trials, the process shall undergo DSC analysis by default.

It will be the duty of an engineer who handles the manufacturing process at site level

Before that i'll deliver some basic thing which should be known.




What is DSC used for ?

Considering safety aspect, DSC is used to understand the material thermal behavior at various temperatures and to study the decomposition of material. Also Isothermal DSC measurements are used for following applications:

• Crystallization process which includes polymorphs,
• Sorption, Vaporisation and drying,
• Auto-oxidations, polymerization etc.

What is thermal decomposition ?
Thermal decomposition is breaking of bonds which is attributed by heat intake. It is something like a chemical change(which cannot be reversed).




What is the principle of DSC ?

DSC is a thermo analytical technique. It is used to measure heat variation / enthalphy variation which occur due to the chemical changes in a substances as a function of time and temperature.

What can be evaluated through DSC study ?

DSC helps us to understand the material characteristics with respect to temperature and time. It helps in understanding the decomposition temperature, Heat liberation during decomposition, available exotherms in the process or in the operating range, time impact on decomposition temperature.

What are the alternatives for DSC ?

There are other alternatives available for DSC. They are TMA (Thermo-Mechanical Analysis), DMA (Dynamic Mechanical Analysis), ARC (Accelerated Rate Calorimetry).




Is the DSC method accurate ? 

DSC is just a screening method and not much accurate. But can be trusted if the process is not much critical.

What can be preferred against DSC in critical case ?

As per me, if the process is critical, its better move ahead with ARC(Accelerated Rate Calorimetry). 

What makes ARC more accurate than DSC ?

DSC shall be performed with a differential temperature of 5 °C/min and the temperature is a variable, whereas ARC requires more time, the differential temperature would be 0.001°C/min.




What is the parameter used in DSC ?

DSC measures the change in enthalphy, i.e., dH/dt and the graph is plotted between 
dH/dt VS temperature.


Also Read:

Checking batch size feasibility for scale-up 

Guidelines for process development

What are the cases which requires ARC ?

Usually after performing DSC, the tentative decomposition temperature would be revealed and if the decomposition temperature is nearby the operating temperature, it would be advisable to understand the exact temperature of decomposition. Based on the evaluation, appropriate mitigation plan can be designed.




What is the type of system used for DSC study ?

DSC study can be done two types of systems i.e., Heat Flux DSC & Power Compensated DSC. Usually we'll prefer Heat flux DSC for pharmaceutical intermediates & API's.

Heat flux DSC heats the two pans (Sample pan & Reference pan) on a single heater with single heat flux and the temperature difference shall be converted to power difference. Power difference(watts) can be represents the variation in heat flow in both cases.

In Power compensated DSC, reference pan and sample pan shall be heated separately and the pan temperatures shall be monitored using thermo-couples. The connected thermocouples measures the differential heat flow.




What are the possible thermal events during DSC study ?

A (S1)                     ------- >   A (S2)  - Phase transition,
A (S1)                     ------- >   A (L)  - Melting,
A (S1)                     ------- >   A (G) - Sublimation,
A (S1)                     ------- >   B (S) + Gasses (or) Gasses - Decomposition

Here, S1 refers to Solid-1, S2 refers to Solid-2, L refers to Liquid, G refers to gas.

And finally, what is TMR ?

TMR refers to time to maximum rate. Simply it is the time taken by reaction mass to attain/reach maximum heat liberation rate.

Delivered the basic stuff above. Let's jump into the topic.

How To Perform DSC analysis:

DSC is a measure thermal enthalphy, for measuring the enthalphy we need a reference.

Two pans will be considered for the study, out of which one will be a reference pan(empty pan P - 1 ), second one will be filled with material (P-2) which need to be studied.

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A sample of ~5 to 10 grams shall be filled in the pan and care shall be taken to avoid any gaps in the pan i.e., material shall cover the heat transfer surface of pan.

These two pans shall be placed on the heater and shall be heated slowly. The whole system shall be connected to the computer and the system shall be operated.

The process of heating pans will come under dynamic run with an pre-defined temperature gradient i.e., like 2℃/min or 5℃/min. It is based on our convenience. Usually the dynamic run shall be  started from RT temperature. (During this dynamic run, system will stabilize initially).




As the Pan - 2 contains sample, the intake heat will be somewhat higher than that of the reference pan - 1.

The dH/dt will be plotted against the temperature and the graph will look like this:

As pert the above graph, there is an exotherm (the peak above reference line) observed at 137.16℃ with a liberation of 36.36 J/g and an endotherm (peak below reference line) is observed at 232.32℃ with an intake of 40.13 J/g.

Through the dynamic run, we'll be able to identify the product decomposition temperature and the available exotherms and endotherms in the path.


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The energy liberation / requirement during the particular duration is given by 

q = qo x e^(-Ea/RT).

q    - Heat liberated / consumed at the temperature T,
qo  - Threshold energy of the material (Constant),
Ea  - Activation energy,
R    - Universal gas constant,
T    - Temperature.

In order to calculate the TMR (Time to maximum rate), we need to calculate the threshold energy and activation energy of the material. For that we have to perform a minimum of two iso-thermal runs.




1st iso-thermal run shall be performed at the onset of decomposition i.e., 137.16 °C with an energy liberation of 36.36 J/g. So the first equation will be like

36.36 x 5 = qo x e^(-Ea/(8.314 x (137.16 + 273.15)))

181.8 = qo x e^(-Ea/3411.32) ------ ( 1 )

2nd iso-thermal run shall be performed at the temperature approximately 15-20°C less than that of the onset temperature. Lets say, the temperature is 120°C. So the second equation will be like

27 x 5 = qo x e^(-Ea/(8.314 x (120 + 273.15)))  [** 27 j/g is the considered value, not derived one]

135 = qo x e^(-Ea/3268.65) -------- ( 2 )

These two equations need to be resolved to get qo, Ea values.

Eq. ( 2 ) - Eq. (1) => log(135/181.8) = (-Ea/R) x [(1/120) - (1/137.16)]

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Upon resolving,

Ea = 10102.36 J/mole
qo = 3481.77 Joules = 3481.77/5 J/g = 696.35 J/g [** 5 is the sample Qty.]
Now, TMR can be calculated from the below formula,

TMR = (Cp x R x T^2)/(q x Eo), in hours




Lets calculate the time to maximum rate at 140 °C,

q = 696.35 x e^(-10102.36/(8.314 x (140+273.15))) = 37.105 J/g.

TMR = (2 x 8.314 x (273.15+140)^2) / (37.105 x 10102.36) = 7.57 hours.
[Cp = 2 J/g. K considered].

i.e., if we maintain the reaction mass at 140°C for a span of 7.57 hours, maximum heat liberation will be observed.

That's it....!!!

Hope it is clear for all.........!!!

If any queries, feel free to comment.......!!!

Comments are most appreciated.........!!!

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About The Author

---

Hi! I am Ajay Kumar Kalva, Currently serving as the CEO of this site, a tech geek by passion, and a chemical process engineer by profession, i'm interested in writing articles regarding technology, hacking and pharma technology.
Follow Me on Twitter AjaySpectator & Computer Innovations

[How To] Perform Design Of Experiments (DOE) using Minitab

Hiii all....!!!

Back with a typical post which is related to process optimization using software i.e., Minitab, simply performing Design of Experiments(DOE).

I've done final year project during BTech on 'Treatment of Industrial waste using Coaggulation - Flocculation with Minitab using DOE & Response Surface Methadology'. A special thanks to Mrs. Kalyani Gaddam & Mr. Shishir Kumar Behera for their guidance.

Most of you are having good knowledge on this topic, but recently i've received a request asking to demonstrate, as on we currently there is no related content which can be easily understandable by process engineers.

So i've taken initiation to explain it here.

To be a perfect engineer, one should be able to perform calculations manually as well as should be in a position to workout through software's. But most of the companies are not able to provide those to their engineers. But believe me, working with those will have an awesome feeling.

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Getting into the topic, Minitab is a package of many purposeful applications. Can draw graphs to evaluate the trends, can evaluate the moving ranges of the variables, can define some random equations based on the available data(regression), Can be used during process optimization stage of product manufacturing.

So, here i'm gonna provide you a small demo about optimization using minitab step-wise manner.

Let the case be particle size distribution, we have to get a desired PSD from a customized isolation step. In this case the variables / factors would be Cooling temperature, Agitator Speed, Rate of cooling. And the output will be d(0.1), d(0.5), d(0.9) with some desired specification.

Let the raw limits of the variables / factors would be 

Cooling temperature : 0 - 20 °C,
Agitator RPM             : 20 - 60,
Rate of cooling           : 10 °C / hour.

Let the expected response be d(0.1) = 10 - 50 µ, d(0.5) = 50 - 100 µ, d(0.9) = 100 - 200 µ 
& yield % = 80 - 90%.

Lets start the show,

Step - 1: Open Minitab, [I'm using Minitab 18].

Step - 2: Click on STAT in main menu bar and then enter the DOE from the drop down,

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Select Screening > Create Screening design.
Below screen will appear.

Select Definitive screening.

Step - 3: Set number of factors to 3 (As our output PSD will depend on cooling rate, agitation rate & Cooling temperature, factors shall be 3).
For proceeding further click on designs and close that then the factors option will be highlighted.

Now click on Factors, it will look like below:

Enter the Low & High values as shown above in the factors dialogue box and click OK.
Again Click OK. random runs will be generated like shown below:

Step - 4: Random runs & experiments.





As like shown above, total of 13 random runs are generated and now experiments need to be performed in lab scale (preferably in laboratory Auto reactors replicating plant agitators). 

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Step - 5: Responses 

After getting the results, we have to fill the results in C8, C9, C10 & C11 columns in the work sheet. Below are some of the tentative results.

Step - 6: Analyzing the response.
Click on Stat > DOE > Screening > Analyzing Screening Design.

By clicking the 'Analyse Screening Design', a window will appear like below shown:

Now we have to select the responses that we need to analyse, now i'll be selecting all the available four responses,

Then Click Ok.

Step - 7: Analyzing the Graphs (Pareto's).

As we have selected a total of 4 responses, 4 pareto graphs will appear on screen, below screenshot fyr:

Now you may get a doubt, 'what does these graphs represent and what we need to understand ?',
Actually in the before clicking Ok, we have to select % of confidence in 'Graphs' option.
Which means the graphs will show that what factors will impact the responses with 95% significance level.

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Let me explain you clearly,
First graph is Yield Vs Factors(Cooling time, Rate, RPM):

There will be a red coloured line over the graph, which represents the standard, the bars which are above the line are said to be having impact over yield. So in the first graph, Factor B, C are having impact on yield, Factor B, C are RPM & temperature, and the factor A is below the standard line, which indicates that Cooling rate(A) is not having considerable impact.

Similarly,
Second Graph ( d(0.1) Vs Factors ):

Factor C(Temperature) bar is below the standard line, hence it is not having impact on d(0.1), whereas factors A, B are above the reference line, hence both of then are having impact on d(0.1).

Third Graph ( d(0.5) Vs Factors ):

Factor B(RPM), C(Temperature) bars are below the standard line, hence it can be said that those two factors are not having significant impact on d(0.5) response, whereas factor A is having significant impact on d(0.5).

Fourth  Graph ( d(0.9) Vs Factors ):

Factor B(RPM), C(Temperature) bars are below the standard line, hence it can be said that those two factors are not having significant impact on d(0.9) response, whereas A is having significant impact on d(0.9)..

That's it, now half of the job is done. You understood the pareto's.

Step - 8: Finding the Regression equations.

If you close all the pareto's, there will be a Session / Activity sheet.
This sheet will record all the process that we have done.





Below is the screenshot fyr:

Now, lets expand each of them.

Yield % Vs Factors:

In the model summary, there will be R-Sq, here it is shown as 66.10%, which indicates that the model is not stable. For a stable model the R-Sq value should be greater than 90% [Some times the design experts will consider even 80% also as stable, but i'll consider 90% as stable].

Regression equation is 87.77 - 0.053 x Cooling rate - 0.2050 x RPM - 0.36 x Temperature.

Using this equation we can predict the yield %, once if the factors are known.

Similarly for other responses also, there will be the regression equations.

Step - 9: Optimizing the response.

This is what we need actually, finding out the optimum response based on our requirement. 
As we need the Yield % in 80 - 90%, d(0.1) : 10-50 µ, d(0.5): 50-100 µ, d(0.9): 100-200 µ.

Navigation to Response Optimizer:

Stat > DOE > Screening > Response Optimizer.

By clicking that, a window will appear as below:

As we have total of four responses, they will appear and we have to select the range for them.

Available options for them are Do Not Optimize, Minimize, Target, Maximize.

From these 4 we have to select any one for the responses.

As we need Yield % in range of 80-90%, i'll prefer it as 88% by selecting target option,
For d(0.1), i'll select 30 as target, 
For d(0.5), i'll select 80 as target,
For d(0.9), i'll select 150 as target.

Below screenshot fyr:

Then Click Ok.

The optimum value will appear for you. Below Screenshot FYR:

The optimum values are Cooling rate : 5 ℃/hr, RPM : 37.77, Temperature : 0 ℃.

Apart from this we have to check one more thing here, that is desirability which is denoted by d.





The desirability d represents the total probability. If its above 95% the optimized response is reproducible, or else not.

In our case the average desirability is 61.28%
Individual desirability are

Yield % : 58.81 %,
d(0.1)    : 67.79 %,
d(0.5)    : 93.95 %,
d(0.9)    : 37.65 %.

So from the above, we can say that the probability of reproducing the desired response is very less.

So if we change the targets to different values, the optimum values might vary and desirability might increase / decrease.





That's it.......!!!!!

Hope you understand, this is the basic explanation and i'll generate a video in future explaining this topic in detail.

Any queries feel free to comment. or reach me at pharmacalc823@gmail.com.

Comments are most appreciated......!!!!




Related Articles:

[How To] Perform Energy Balance 

Calculating Raw Material cost contributions

Enhancing plant capacity, how to do that?

Analyse TLC method, how to perform TLC ?

About The Author

---

Hi! I am Ajay Kumar Kalva, Currently serving as the CEO of this site, a tech geek by passion, and a chemical process engineer by profession, i'm interested in writing articles regarding technology, hacking and pharma technology.
Follow Me on Twitter AjaySpectator & Computer Innovations