Read Microsoft PowerPoint - EPTM 08 Case Studies in Fluidized Bed Processing rev 1.ppt text version

Case Studies in Fluidized Bed Processing

David M. Jones OWI-Consulting Inc

Email: [email protected] Phone: 201-825-1607 Cell: 201-264-5173

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Topics of the Presentation

Equipment description A typical process recipe Case studies

The use of DoE in product development and scale-up Nuisance alarms Part 11 ­ unintended consequences Operating ranges for dependent variables

Summary

Graphics courtesy of Glatt Air Techniques, Inc., Ramsey, NJ

A Typical Fluid Bed Spray Granulator Installation

Inlet and exhaust air handling "GMP" Utility

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The Machine Tower Components

Outlet filter housing

Expansion chamber

Spray nozzle wand

Product container

Inlet duct and lower plenum

Top Spray

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The Machine Tower Components

Outlet filter housing

Expansion chamber

Product container

Spray nozzle wand

Inlet duct and lower plenum

Wurster

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The Outlet Filter Housing - Types

Single chamber

Twin chamber

Cartridge

The Machine Tower Components

The vast majority of fluid bed systems incorporate the use of fabric filters (as shown). The two dominant considerations are: A.Porosity (the size of the openings in the fabric) B.Permeability (the number of openings per unit area

However: there is NO standardized test for determining this behavior, there is vendor variability and periodically, fabrics are discontinued.

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Filter Fabric - Performance

Filter Material Permeability

Various fabrics - duct velocity vs. filter pressure

11 10 9

Duct Velocity (m/sec)

8 7 6 5 4 3 2 1 0 20 40 60

PB 3% (50) PB 2% (20) T 165 P (20) Nadelfilz 3451/01 BR (10) K22 1103 (25) 1893 (10) Nadelfilz TW 452 (7) WECO T15E (48) PES 9373 (3-5)

80

Filter Differential Pressure (mm water)

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Filter Fabric - Performance

Filter Material Permeability

Filter materials which resemble Glatt PES 3-5 micron

12

10

Duct Velocity (m/sec)

8

6

4

Glatt PES 9373 Kavon CN 999 Shaffer PA/C1 Shaffer PA/C2

2

0 0 10 20 30 40 50

Filter Differential Pressure (mm water)

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A Representative Process Recipe

Step AHU Preconditioning M/C tower warm-up Charging Product warm-up Spraying Drying Cooling Discharge 2,000 cfm 2,000 2,000 2,000 1,700 1,700 Process air volume Inlet/ bypass temp. 70 C 70 70 70 70 70 35 1,000 g/min 2.5 bar Spray rate Atm. air press. End step Values at set point - prompt Exhaust temp. Prompt Product temp./ min. time Solution quantity Product temp. Product temp. Run another batch?

Global Process Parameters and Other Considerations

Global Parameters Process air dew point Filter shake interval Filter shake time Total air volume Value 15 C 45 sec 6 sec 2,500 cfm Other considerations PID tuning parameters Operating ranges, alarm limits and tolerances

A simple process should be the goal of product/process development.

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Graphically:

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An alternative: What are some issues?

18" HS Wurster Batch

100 90 80 70 Temperature (C) 60 50 40 30 20 10 0

10: 35: 05 10: 46: 07 11: 02: 56 11: 18: 27 11: 33: 58 11: 49: 30 12: 05: 03 12: 20: 34 12: 36: 05 12: 51: 35 13: 07: 08 13: 22: 39 13: 38: 11

1200

1000 Spray Rate (g/min)

800

600

400

200

0

Time

Process Dewpt (°C) Process Air Temp (°C) Product Temp (°C) Spray Flow Rate (g/min) Exhaust Temp (°C)

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Case Studies

1. 2. 3. 4.

Effectiveness of DOE in lab/pilot scale `Nuisance' alarms (electronic controls) Unintended consequences of Part 11 Identifying ranges for dependent variables

OWI-Consulting Inc

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Case Studies

1. 2. 3. 4.

Effectiveness of DOE in lab/pilot scale `Nuisance' alarms (electronic controls) Unintended consequences of Part 11 Identifying ranges for dependent variables

OWI-Consulting Inc

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"With all of the variables involved in formulation and process development, we don't have the time or the API to do DoE."

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Variables in Fluid Bed Processing

Top Spray (TS) and Wurster (W)

Process air volume Process air temperature Process air dew point Atomizing air pressure Atomizing air volume Liquid spray rate Product temperature Exhaust temperature Product differential pressure Exhaust filter differential pressure Product bed depth, batch size In-process moisture content Spray nozzle type Spray nozzle port size Spray nozzle height (TS) Orifice plate configuration (TS) Partition height (W) Up bed orifice plate (W) Down bed orifice plate (W) Product retention media Exhaust filter media Spray liquid viscosity Spray liquid solution or suspension Spray liquid medium (solvent or water) Spray liquid solids concentration

`With all of these variables, there is no time for DoE'

Remove the `Set it and Forget it' Variables

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Variables in Fluid Bed Processing

Top Spray (TS) and Wurster (W)

Process air volume Process air temperature Process air dew point Atomizing air pressure Atomizing air volume Liquid spray rate Product temperature Exhaust temperature Product differential pressure Exhaust filter differential pressure Product bed depth, batch size In-process moisture content Spray nozzle type Spray nozzle port size Spray nozzle height (TS) Orifice plate configuration (TS) Partition height (W) Up bed orifice plate (W) Down bed orifice plate (W) Product retention media Exhaust filter media Spray liquid viscosity Spray liquid solution or suspension Spray liquid medium (solvent or water) Spray liquid solids concentration

A few `feasibility' experiments will allow you to `set' several parameters

Now Eliminate the `Dependent' Variables

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Variables in Fluid Bed Processing

Top Spray (TS) and Wurster (W)

Process air volume Process air temperature Process air dew point Atomizing air pressure Atomizing air volume Liquid spray rate Product temperature Exhaust temperature Product differential pressure Exhaust filter differential pressure Product bed depth, batch size In-process moisture content Spray nozzle type Spray nozzle port size Spray nozzle height (TS) Orifice plate configuration (TS) Partition height (W) Up bed orifice plate (W) Down bed orifice plate (W) Product retention media Exhaust filter media Spray liquid viscosity Spray liquid solution or suspension Spray liquid medium (solvent or water) Spray liquid solids concentration

The list is much smaller and more manageable...

Variables in Fluid Bed Processing

Top Spray (TS) and Wurster (W)

Process air volume Process air temperature Process air dew point Atomizing air pressure Atomizing air volume Liquid spray rate Product temperature Exhaust temperature Product differential pressure Exhaust filter differential pressure Product bed depth, batch size In-process moisture content Spray nozzle type Spray nozzle port size Spray nozzle height (TS) Orifice plate configuration (TS) Partition height (W) Up bed orifice plate (W) Down bed orifice plate (W) Product retention media Exhaust filter media Spray liquid viscosity Spray liquid solution or suspension Spray liquid medium (solvent or water) Spray liquid solids concentration

...and some parameters are `either/or'.

18" HS Wurster Batch

Why DoE is such a powerful tool

100 90 80 70 Temperature (C) 60 50 40 30 20 10 0

1 0: 35: 05 10: 46: 07 11: 0 2: 56 11: 18: 27 1 1: 33: 5 8 11: 49: 30 12: 0 5: 03 12: 20: 34 12: 36: 0 5 1 2: 51: 35 13: 0 7: 08 13: 22: 39 13: 38: 1 1

1200

1000 Spray Rate (g/min)

800

600

400

200

0

Time

Process Dewpt (°C) Process Air Temp (°C) Product Temp (°C) Spray Flow Ra te (g/min) Exhaust Temp (°C)

1. It forces development personnel to keep the process simple ­ avoid ramping where possible! 2. It helps to quantify the magnitude of impact for critical process parameters 3. A broad range of response variables can help teach something unexpected 4. The domain helps to identify operating ranges for critical process parameters

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A case study:

Top spray fluidized bed spray granulation

A preliminary `range' study to identify the domain for a 3 factor, 2 level DOE.

The factors are inlet air temperature (evaporation rate), liquid spray rate (primarily in-process moisture content) and atomizing air pressure/volume (droplet size)

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batches 87-93

a series of batches to define DOE domain

60 Percent retained 50 40 30 20 10 0 16 20 40 93 92 60 91 90 80 89 88 100 87 200 pan

Particle size and distribution respond strongly to the range of process variables selected for study. OWI-Consulting Inc

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batches 87and 88

influence of inlet air temperature

60 Percent retained 50 40 30 20 10 0 16 20 40 60 80 100 200 pan 88 (+), BD 0.43g/cc 87 (-), BD 0.52g/cc

·The lower inlet air temperature results in a coarser particle size and higher bulk density (principally due to higher inOWI-Consulting Inc process moisture content). Onsite With Insight

batches 90 and 91

influence of spray rate

50 Percent retained 40 30 20 10 0 16 20 40 60 80 100 200 pan 91 (+), BD 0.56g/cc 90 (0), BD 0.0.48g/cc

·The increased spray rate increases particle size and bulk density. Note: this batch was the `worst case' and required a revision to the domain.

batches 89 and 92

influence of atomizing air pressure

40 Percent retained 30 20 10 0 16 20 40 60 80 100 200 pan 92 (+), BD 0.40g/cc 89 (0), BD 0.42g/cc

Atomizing air pressure/volume strongly affects droplet size, and ultimately particle size and distribution OWI-Consulting Inc

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Results:

· All batches tableted successfully. Distribution uniformity, hardness, friability and disintegration time all passed the specification. A robust process? There was an interesting impact on a machine component...

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Initial behavior (6.5 minutes) is interesting:

Batches A, B, and C

Comparison of exhaust filter differential pressures vs. spray rate

Differential Pressure (mmWC)

500 400 300 200 100 0

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5

1000 800 600 400 200 0 Elapsed Time (minutes)

A dP (mmWC) B dP (mmWC) C dP (mmWC) A (g/min) B (g/min) C (g/min)

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Spray Rate (g/min)

The trend for a whole batch is also of interest:

Here, the filter pressure remains low for the entire batch, and there is no appreciable variability in process air volume.

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Here, the filter pressure is trending upward, but not to an alarming level. Variability in process air volume is nominal. OWI-Consulting Inc

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Here, the filter pressure reaches the display limit, and variability in process air volume is increasing. OWI-Consulting Inc

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Here, the filter pressure is extreme ­ the batch was interrupted in an attempt to manually clean the filter. After restart, it was evident that this failed (air flow control not possible). OWI-Consulting Inc The batch was aborted.

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It was found that the filter pressure was related to in-process moisture content. Wetter batches did not tend to foul the filter. A later batch, at high spray rate, actually seemed to `clean' the same filter. OWI-Consulting Inc

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Case Studies

1. 2. 3. 4.

Effectiveness of DOE in lab/pilot scale `Nuisance' alarms (electronic controls) Unintended consequences of Part 11 Identifying ranges for dependent variables

OWI-Consulting Inc

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Nuisance Alarms

18" HS Wurster Batch

100 90 80 70 Temperature (C) 60 50 40 30 20 10 0

10: 35: 05 10: 46: 07 11: 02: 56 11: 18: 27 11: 33: 58 11: 49: 30 12: 05: 03 12: 20: 34 12: 36: 05 12: 51: 35 13: 07: 08 13: 22: 39 13: 38: 11

1200

1000 Spray Rate (g/min)

800

600

400

200

0

Time

Process Dewpt (°C) Process Air Temp (°C) Product Temp (°C) Spray Flow Rate (g/min) Exhaust Temp (°C)

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Nuisance Alarms

Fluidized Bed Spray Granulation

Process data - temperatures, spray rate, air volume

110 100 90 80 70 60 50 40 30 20 10 0

17:12:23 17:22:51 17:33:10 17:43:26 17:53:50 18:04:06 18:14:27 18:24:35 18:34:36 18:44:37 18:55:00 19:05:01 19:15:02 19:25:03

Clock Time

Inlet Dew Point C Inlet Air Temp C Process Air Vol cfm Product Temp C Spray Rate g/min Atomizing Air Press bar Exhaust Temp C

Process Air Volume (cfm) Spray Rate (g/min)

Temperature (C)

Case Studies

1. 2. 3. 4.

Effectiveness of DOE in lab/pilot scale `Nuisance' alarms (electronic controls) Unintended consequences of Part 11 Identifying ranges for dependent variables

OWI-Consulting Inc

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Unintended Consequences...

1. Hand written in-process data sheets proliferate, even for machines fitted with electronic data acquisition systems. 2. A widely used, very user friendly historical trend utility, with 1-second resolution has been deemed noncompliant and its use is being curtailed. 3. Data acquisition systems write data in 30 - 60 second intervals. 4. When QA/QC departments `see' the electronic data, a significant number of excursions outside of the identified operating ranges for process variables are seen. Are they major? Minor? Deviations or not?

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Case Studies

1. 2. 3. 4.

Effectiveness of DOE in lab/pilot scale `Nuisance' alarms (electronic controls) Unintended consequences of Part 11 Identifying ranges for dependent variables

OWI-Consulting Inc

Onsite With Insight

In the first example, filter pressure is very low for the duration of the batch. Process air volume is not impacted during shaking.

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In the second example, filter pressure trends upwards during the batch. Process air volume fluctuates during shaking, but not to a great extent.

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18" HS Wurster Batch

Product differential pressure and process air volume

45 0 12 00

Product Differential Pressure (mmWC)

40 0

10 00

35 0

80 0

30 0

60 0

25 0

40 0

20 0

20 0

15 0

10:35:05 10:50:07 11:14:15 11:35:24 11:56:34 12:17:44 12:38:55 13:00:05 13:21:14 13:42:24 14:03:34

0

Time

Proce ss Air Vol (cfm) Product DP (m mwc)

In this example, product differential pressure rises substantially and the trough to peak values expand as batch size increases.

Process Air Volume (cfm)

Summary

The fluidized bed process has a long list of variables, but only a few are likely to be critical process parameters DoE offers many benefits for product/process development and is very useful in scale-up

Keep it simple! Discover the unexpected Critical process parameters and operating ranges are derived experimentally

Electronic data is a very effective tool for retrospective troubleshooting ­ the more factors collected, the greater the impact on problem solving OWI-Consulting Inc

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Any Questions?

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