Bioprocess Scale-Up & Scale-Down: Industrial Optimization

Posted on Sep 15, 2025 in Biotechnology

Bioprocess Scaling Up: Lab to Industrial Production

Bioprocess scaling up is the methodology used to develop an industrial bioprocess from knowledge acquired at the laboratory scale, often based on mathematical and physical models.

Approaches to Bioprocess Scaling Up

Scaling up can be approached in two primary ways:

  • Increasing the number of reactors: This method carries no risk of scaling errors since all reactors are identical. However, it incurs significant expenses for monitoring and managing numerous vessels.
  • Increasing the volume of a single reactor: This is often considered the more logical approach. Direct scaling is not feasible; instead, a thorough understanding of the complete process nature is required.

Impact of Scale on Bioprocess Dynamics

As the scale of a bioprocess changes, several critical aspects are affected:

  • Difficulty in maintaining homogeneity in large systems.
  • Changes over time in reactor conditions, such as volumes and concentrations.
  • Increased importance of mass, energy, and momentum transport phenomena.
  • Variations in response time, which is the duration a system needs to react to a specific stimulus.
  • Alterations within the hydrodynamic regime.

Key Phenomena in Bioprocess Scale Changes

Understanding the phenomena involved in scale changes is crucial:

Scale-Independent Phenomena:

  • Thermodynamic equilibrium and reaction kinetics.
  • Intrinsic kinetic parameters and reaction rates.

Scale-Dependent Phenomena:

These primarily involve transport phenomena:

  • Mass transport: Including diffusion and distribution coefficients.
  • Heat transport: Involving thermal properties and parameters.
  • Momentum transport: Related to fluid dynamics.

Standard Bioprocess Scale-Up Procedure

A typical bioprocess scale-up procedure follows these stages:

  1. Obtain initial results at the laboratory scale.
  2. Conduct experiments in a small pilot plant.
  3. Perform experiments in a larger pilot plant.
  4. Execute experiments in an industrial plant to achieve full production.

Note: If any step fails, the underlying hypothesis must be reexamined, and new data defined.

Environmental Changes with Increased Scale

Varying the dimensions on a larger scale significantly alters the physical environment of the cells, impacting factors such as:

  • Nutrient availability
  • Oxygen concentration
  • Shear forces
  • pH levels
  • Temperature
  • Viscosity

The primary goal when scaling is to reproduce the most critical conditions from the smaller scale.

Bioprocess Scale-Up Criteria

When scaling up, based on factors like mass transport, shear stress on cells, and homogeneity (mixing time), it is essential to establish which scale-up criteria will control the process and which parameters will remain constant. The dependence on operational conditions and vessel geometry differs for each criterion. These criteria help identify the rate-limiting step and determine optimal operational conditions and geometrical parameters.

Common Scale-Up Criteria for Bioreactors

  1. Power Input (P/V): This refers to the amount of power transferred per unit volume from the agitator shaft and impellers. It can be considered for both aerated and non-aerated systems.
  2. Impeller Tip Speed: Directly related to the shear rate generated by impellers moving the cell culture media. Both power input and impeller tip speed are crucial for managing shear stress on cells. If scale-up is based on constant P/V or tip speed, mixing time may decrease.
  3. Mixing Time: The duration required for the reactor to achieve a homogeneous environment. This is particularly vital in viscous broths. Maintaining a constant mixing time necessitates a consistently homogeneous environment.
  4. Superficial Gas Velocity: Represents the volume of gas within the vessel. It is essential for ensuring adequate oxygen supply to cells. Scaling bubbling columns based on this criterion can be exceptionally challenging.
  5. Liquid Circulation Rate: Impellers perform a dual function: pumping liquid within the bioreactor vessel and facilitating local turbulent micro-mixing.
  6. Reynolds Number: The heat transfer coefficient is a function of the impeller Reynolds number, which is especially significant for thermophilic organisms.
  7. Volumetric Mass Transfer Coefficient (KLa): Typically applied to aerobic systems where oxygen concentration is paramount and directly impacts microbial cell metabolism.

The choice of scaling criterion depends on the most critical phenomenon for the specific bioprocess (e.g., mass transfer, shear stress, or homogeneity). Each criterion leads to a distinct operating situation and exhibits different dependencies on process parameters. It’s important to note that most studied criteria are for stirred tanks, and quantitative information for other reactor types may be limited.

Objectives of Bioprocess Scale-Up

The primary objectives of bioprocess scale-up methodology include:

  • Maintaining Yield: Ensuring that the degree of conversion and productivity achieved at the laboratory scale is replicated at the larger industrial scale.
  • Preserving Cell Physiology: Preventing negative effects of scale changes on cell physiology and biological response.
  • Consistent Performance: Achieving similar process behavior across scales, often indicated by overlapping performance curves or consistent key parameters like KLa values between small and large scales.

Bioprocess Scale-Down Methodology

Scale-down methodology offers an alternative approach to address challenges encountered during scale-up. It involves simulating industrial-scale limitations within a laboratory setting. Instead of optimizing industrial operations where limitations might not be apparent, researchers introduce these anticipated industrial constraints into laboratory experiments. This approach allows for easier parameter evaluation and reduces costs.

Key Characteristics of Scale-Down:

Regime Analysis:

Based on characteristic times, regime analysis is a valuable tool for process optimization. It helps identify and provide information about rate-limiting mechanisms, rate limitations, and concentration gradients.

The steps involved are:

  1. Identify significant process variables.
  2. Quantify these process variables.
  3. Derive rate constants, which lead to characteristic times.
  4. Determine the slowest rate that governs the overall process.

Parameters affecting characteristic times include fluid dynamics, mixing, mass/heat transfer, and reaction kinetics.

Simulation of Rate-Limiting Mechanism(s):

This involves predicting bioreactor performance by studying the dynamic response of cells to micro-environmental changes within the selected bioreactor.