it is about advanced technologies in bioprocess engineering

1. PROCESS ANALYTICS
AYEBALE CHARITY
20/U/0412
2000700412

2. Process Analytical Technology
• Process Analytical Technology (PAT) was defined by the Food and
Drug Administration guidance “PAT- A Framework for Innovative
Pharmaceutical Development, Manufacturing and Quality Assurance”
as a mechanism to design, analyze, and control pharmaceutical
manufacturing process through the measurement of Critical Process
Parameters (CPP) with Critical Quality Attributes (CQA) of the
product.

3. Spectroscopy for process monitoring in
chromatography.
• UV/vis spectroscopy
• This measures the absorption of proteins generally in the range between 240 and
340 nm. Particularly at 280 nm, is widely used to measure protein
concentrations due to the significant absorption of aromatic amino acids. Its
sensitivity, reproducibility, and robustness make it a primary choice for protein
concentration detection.

4. • Fourier-transform infrared spectroscopy (FTIR)
• This is a well-established spectroscopic method in the analysis of small
molecules and protein secondary structure.
• FTIR is a widely used method for assessing the structural integrity of proteins
during protein purification and formulation.

5. • Near-infrared spectroscopy (NIR)
• NIR spectroscopy is a non-invasive analytical method that operates
based on the principle that the atoms of molecules are in constant
motion and vibrate at specific frequencies. It is used to analyze the
chemical composition of materials by measuring the absorption of
near-infrared light.

6. • Nuclear magnetic resonance (NMR)
• NMR is a spectroscopic technique that utilizes magnetic fields to study atomic
nuclei of a sample. Since this method provides information for each nucleus in the
entirety of a sample and is directly proportional to the intensity of the signal
measured, NMR can be used to provide information about the characterization,
and quantification of a variety of different materials.

7. • Raman spectroscopy
• This is a form of vibrational spectroscopy that can provide
information about chemical properties and molecular interactions
of various compounds, which in turn can provide a molecular
fingerprint of any given compound.

8. • Dynamic light scattering (DLS)
• This is a technique that correlates the Brownian motion and resulting
light scattering of particles in solution and is used to provide
information about particle size and distribution.

9. • Particle count and imaging probes
• Technological advancements in PAT that utilizes biochemical and
biophysical analyses make it possible to monitor processes in-line and
in real time often by probe insertion directly into the manufacturing
line in reactors, connecters, and vessels.

10. Strategies for Bioprocess Control
• Bioprocess control comprises of a set of operations that supervise the
process in an unpredictable environment with the objective to maintain
the process within the desired design space.
• The control strategy is created during process development.
• Creation of a robust control strategy depends on how deep our
understanding of the process is and if we have accurate process
models.

11. Control Strategy Description Control Structure
Open loop
control
• Pre- computed and sequential control actions are
stored in a controller and executed on demand.
• Control action cannot be adjusted based on system
response.
• Can only give instruction to the equipment
• No means of data acquisition
Closed loop
control
• Outputs are continuously compared with the set
point.
• Incase of deviation, controller output is adjusted to
minimize the error and forcing response towards
the set point.
• Applied in automation for continuous production of
biotherapeutics.
Cascade control • Input to the primary loop process transfer function
is obtained from the output of secondary loop
process transfer function.

12. Artificial neural
network-based control
• Comprises of input layer, hidden layer, and
output layer
• • Hidden layer has weights that transform
input into a quantity that can be used by
output layer. The technique adjusts hidden
layer to match the desired output.
• • NN are designed for pattern recognition,
classification, clustering, and prediction.
• • Limitations include the extensive amount
of data required and computational time
Model based control • Implemented in 4 steps: plant modelling,
analyzing and developing controller ,
simulating plant, and controller.
• Simple and intuitive design with better
performance than PID
Model predictive control • Multivariate control algorithm that uses real-
time process model for making predictions by
optimizing cost function at each step to reach
the reference value.
• Improves steady state response, predicts
upcoming disturbances, and guarantees

13. • Why bioprocess control and automation
• Off line, in line, up line, in real time processes
• Labview, plant simulation

14. THANK YOU