How does Sensitivity Analysis work in Bioprocess Engineering?

Sensitivity analysis is a powerful tool used in bioprocess engineering to evaluate the sensitivity of a bioprocess model to changes in input parameters. It helps to understand how variations in these parameters affect the output variables of interest, providing insights into the robustness, reliability, and optimization of the bioprocess. Sensitivity analysis allows engineers to identify the most influential parameters and focus resources on controlling or optimizing them.

Parameter Selection

The first step in sensitivity analysis is to identify the input parameters that have a significant impact on the output variables. These parameters can include physical, chemical, and biological properties of the system, process conditions, kinetic parameters, and model assumptions. It is important to consider both influential and uncertain parameters in the analysis.

Sensitivity Measures

Sensitivity measures quantify the effect of parameter variations on the model output. Various sensitivity indices and measures are used, including:

a. One-At-A-Time (OAT) Sensitivity – This approach evaluates the effect of changing one parameter at a time while keeping others constant. It helps identify the individual impact of parameters but does not account for potential interactions between parameters.

b. Global Sensitivity Analysis (GSA) – GSA methods, such as variance-based methods (e.g., Sobol’ indices), assess the overall contribution of each parameter and its interactions to the output variance. These methods provide a more comprehensive understanding of parameter importance and can capture nonlinear and higher-order effects.

c. Partial Rank Correlation Coefficient (PRCC): PRCC assesses the correlation between each parameter and the output variable, while controlling for the influence of other parameters. It identifies the parameters that have the most significant influence on the output variable.

d. Morris Method: This method evaluates the effect of parameter variations by perturbing them at different levels. It provides qualitative information on parameter sensitivity, indicating which parameters have the most substantial impact on the output.

1. Sampling Techniques: Sensitivity analysis often requires generating multiple samples from the parameter space to evaluate the model response. Sampling techniques such as Latin Hypercube Sampling (LHS) and Monte Carlo Sampling are commonly employed to efficiently explore the parameter space and capture parameter uncertainties.
2. Interpretation and Visualization: Sensitivity analysis generates a vast amount of data, which needs to be interpreted and visualized effectively. Various graphical and statistical techniques, such as bar charts, scatter plots, tornado plots, and sensitivity indices, are used to present the sensitivity results in a clear and concise manner. These visualizations aid in identifying the key parameters and their impact on the bioprocess model.
3. Optimization and Control: Sensitivity analysis provides valuable insights for bioprocess optimization and control. By identifying the most influential parameters, engineers can focus on controlling or optimizing those parameters to enhance process performance, yield, productivity, or robustness. Sensitivity analysis helps in determining the critical process variables that need to be tightly controlled or adjusted to achieve desired process outcomes.
4. Model Validation: Sensitivity analysis can assist in model validation by comparing the model predictions with experimental data. If the model fails to capture the sensitivity patterns observed in the experimental data, it may indicate model deficiencies or the need for parameter refinement.

Sensitivity analysis in bioprocess engineering is a valuable tool for understanding the influence of input parameters on process outcomes. It aids in identifying critical parameters, optimizing process conditions, and enhancing the robustness and reliability of bioprocesses. By utilizing sensitivity analysis techniques, engineers can make informed decisions to improve bioprocess performance, reduce costs, and ensure successful scale-up and commercialization of bioprocesses.