Batch to Continuous: Unloading Kinetics Impact Product Quality
By reviewing these sources of variability regularly and building systems to manage them, specifications can often be narrowed, leading to an ROI for the business.
In working with hundreds of brands with thousands of products, the marriage of batch and continuous operations can be a source of friction. Downstream batch systems, like truck shipments and pallet movement of finished goods, and how to accumulate product continuously into these operations are well defined. On the contrary, the reverse—batching into continuous systems—often leads to unintended quality challenges. The kinetics of unloading systems (the mechanisms governing how product exits a processing system) into continuous operations often falls into the realm of the Art of Production, but they can have a significant impact on quality.
At its core, Unloading Kinetics in a food processing line refers to the manner in which batched material is discharged from a specific unit operation. This simple process is actually a complex driving-force stew of fluid dynamics, thermal kinetics, and equipment design.
Failure of homogeneous batch theory
Batches of product are often thought to be homogeneous from the onset because they are mixed, but we often overlook edge effects. We are trained to overlook edge effects because they are complex, the bulk of our batches have a very low surface area to volume ratio, and in theory, edge effects are blended back in downstream. Large volumes protect consumers from some of these effects, but root cause analyses of quality failure will often drag them back out. Additionally, pilot plant trials may over-present certain effects that are not seen at a larger scale, thus making scale up simpler or impossible.
Jamie Valenti-Jordan is the Food Brand Program Manager for the Food Finance Institute at the Universities of Wisconsin, which provides business and finance education for food and ag entrepreneurs. He is also the CEO of Catapult Commercialization Services Inc.For example, when unloading a supersack of powder in a cold environment, the first material out will often have the desired bulk density. As the powder drains out, the more free-flowing material will selectively fall through a potential rat hole and into the downstream system. If the supersack has been stored in an ambient room prior to being brought into the cold room, moisture can form on the inside of the bag liner (most powders are not bone dry). That moisture can cause the powder to clump inside the bag, forcing the use of pneumatic thumpers, bag massagers, or manual intervention. Furthermore, the bulk density of that wetted product will be much higher, impacting weigh cells readings—and let’s not talk about the potential food safety considerations.
Batch homogeneity is not guaranteed just because it was once homogenous. Rheology—roughly defined as the study of viscosity—can inform a nuanced look at suspension of solids in a fluid, emulsion stability based on particle size, or shear stability in a thickened material. The amount of time a batch sits idle can significantly impact the difference between the first volume out of the system versus the last volume out. Consider whether your operation is oversimplifying your product by sampling the beginning and end of a batch for consistency.
Residence Time Distribution
The first step on this journey is attempting to account for unloading kinetics with a concept known as Residence Time Distribution (RTD). RTD describes the distribution of times that different volumes of the material spend within a vessel. Often, this concept is applied to look at the average RTD for process time optimizations, but in reality, it is a curve. Sometimes the curve is simple, but more often than not, downstream continuous systems are not ready to take the product from the batching system, so the batching system must wait.
A good fluid dynamics example of RTD’s impact comes from factors like channeling, dead zones, or short-circuiting as liquid products get moved around in pipes. The variations directly translate into inconsistencies in quality attributes due to various amounts of driving forces acting on each volume. For instance, in a continuous cooking system, product volumes that experience shorter residence times (due to short-circuiting) may be undercooked, compromising food safety. Conversely, elements lingering in dead zones might be over-processed, leading to flavor degradation, nutrient loss, or undesirable texture.
RTD data can reveal a much different picture about what is happening during production than was intended in the design of the process. Often buffer/day/run tanks are added to absorb all the product from a blending operation (especially with heating) to prevent large swings in product quality (end of batch to beginning of next batch).
Back end of the batching cycle
To address these inconsistencies, we should watch the “back end” of the batching cycle—in other words, the time from the completion of a batch to the start of the next batch. In many cases, this is the unwatched portion of the operation, but it is the most critical for understanding changes in product quality due to batch-to-continuous connection failures.
Classically, in the frozen soup industry, the filling systems are often incapable of taking an entire kettle’s worth of product at once, so the filler sends a signal back to the PD pump that is emptying the kettle to shut it off until more product is needed. In many cases, the soup was heated in a steam jacketed kettle that has a thermal mass of its own, even if the steam is turned off. When the soup level drops below the agitator’s reach, the residence time in that kettle continues to cook the product—reducing quality and quantity (evaporation) in products filled at the end of a batch. In a sensitive cream soup, this can result in burn-on, discoloration, and emulsion disruption.
By focusing time observing the back end of a batching cycle, observations can be made about unintended consequences from batch building, such as the steam jacket being left on during transfer. To reduce these sorts of issues, consider automation systems as production staff will be challenged to babysit back-end product needs—they need to focus on production of the next batch.
Conclusion
Every operation has a transition from a batch system to a continuous one somewhere—whether that is unloading a tanker of milk or dumping a tote of apples. Remember that edge effects and the back end of a batching cycle can play a significant impact on the quality of the final product through unloading kinetics. By reviewing these sources of variability regularly and building systems to manage them, specifications can often be narrowed, leading to an ROI for the business.
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Jamie Valenti-Jordan is the Food Brand Program Manager for the Food Finance Institute at the Universities of Wisconsin, which provides business and finance education for food and ag entrepreneurs. He is also the CEO of Catapult Commercialization Services Inc.
