Understanding Scale up Methodology

Scale-up is the process in which experiments are carried out in much larger system sizes to understand how processes will work when it comes time to go into production. It allows for testing variables that may be too expensive or impossible to test experimentally with actual plant operations. This analysis is used throughout various industries, including petroleum refining, chemicals production, food processing, and even synthetic biology.

The scale-up methodology includes many different techniques, including statistical, experimental design, and optimization methods. This can all be divided into four main parts:
1) Design of experiments (DoE) for screening/process understanding
2) Optimization using response surface methodology(RSM)
3) Process Simulation
4) Process robustness and reliability

Scale-up is the last step in the development of a given process. It can involve many stages, including bench-scale studies on the effect of different variables on the process under investigation, pilot plant studies to develop commercial-scale conditions, and demonstration on an actual plant. A few examples include:
1) Designing experimental procedures for studying the behavior of triacylglycerol crystallization at oil concentrations between 20% and 40% w/w.
2) Evaluation of design options for biodiesel production with high acid-free algal biomass.
3) Development of a low-density polyethylene through RSM optimization.
Scale-up is needed in many different fields, including but not limited to petroleum refining, food processing, chemical production, and synthetic biology. An example of its use in the petroleum industry was changing the catalyst support “from a 40 to 100 L scale pilot plant system” because it produced double the product at half the time(5). Scale-up is also used in foods processing operations, particularly to increase efficiency(6), for example, by improving yields or reducing costs. Synthetic biology research often uses scale-up methodology because it allows for experiments that are impossible to do on a lab bench (e.g., using an entire cell rather than an isolated gene).

There are many advantages of practicing scale-up methodologies across various industries. However, some disadvantages arise when it is not used properly.
1) Allows for testing variables in much larger systems than what would be available to perform experiments on actual plant operations. This can give an accurate prediction of how a process will work during operation.
2) Predicted results of scale-up studies can eliminate many of the practical problems associated with the commercialization of new products and processes. Processes can be optimized at early stages to reduce cost and maximize product yield (3).
3) Improved process understanding through data collection, analysis, and interpretation.
4) Scale-up methods provide effective ways to design cheaper experimental studies for important process variables to obtain optimum values.
5) Allows for a much more efficient design of a commercial plant.

1) Inaccurate representation of certain aspects such as mixing due to larger-scale equipment.
2) Uncertainty in extrapolating from bench/scale studies to full-scale operations because there are often differences between lab and large scale testing.
3) Transferring processes to commercial plants may lead to problems associated with a non-uniform product, incomplete reactions, lack of control over operating variables, and lack of optimization.
4) Poor understanding of the process due to lack of studying factors such as agitation, shear forces, etc.

Understanding the Scale-up methodology will help you achieve your goals faster. This blog has given you some insight into the important elements of scale-up, but many more can be used to drive success in any endeavor.