Manufacturing Reflections

Many people use the term “Golden Batch” as a strategy to control batch processes.  The concept is to compare a set of batches and find the ‘good’ batches to identify the best possible recipe then set up a control system to follow this recipe to improve batch results.

As a concept – this makes simple sense.  It matches how your mother makes cake or meatloaf.  But there have been many questions and doubts about how much good it delivers.  First almost every batch runs on a recipe – there is no problem with trying to do the same thing every time and most batch control systems include a recipe manager.

But the variation from batch to batch that you are trying to ‘control’ by repeating the same recipe is infrequently caused by failing to follow your design recipe.  Most variability is caused by: feedstock quality variation, utility system upsets, and by natural bio-organism or bio-catalyst efficacy differences.  These variations cannot be impacted by an identification of a golden batch except for the case where your feedstock tends to vary in higher or lower quality than the design and your innate process variability finds a better way to run with this shifted quality.

There is another limitation with most ‘golden batch’ recipes – time is the primary driver of when to do what. Life/bio-processes grow, digest, and convert at varying rates.  This is partially because of natural bio-processing rates, but a sense-and-respond capable control system responds to these causes of variation.  A better recipe is based on batch progress not from the simpler time.

We have delivered batch-quality control on bio- and other processes, most commonly in yeast fermentation. The focus has been on taking advantage of modified Michaelis-Menten kinetic models, tuned to a batch performance history and deploying a sence and respond system looking at all available batch state information.  We found another limit of time in that for our model-based dynamic control perspective it is not realistically differentiable (we cannot make time move faster as we can with batch progression).  A key for us is that interim QC sampling is provided so that batch progression can be confirmed or corrected while still actionable. End-of-Batch sampling is informative and can support the ‘next’ batch, but we do much better with interim results measuring quality progression. A quality model, backed-up by measurement lets you run much closer to an optimal batch trajectory every batch even with biologic, feedstock and utility variability.  This provides today’s golden batch – adjusted for what is possible with the ingredients that are available.

Mike T.