-Introduction to Moisture Challenges in Powders: Discussion on the common types of powders in various industries (e.g., milk powder, lactose, talc, and mica) and the associated moisture issues they face.
-Preventing Physical Transitions: Exploration of the stages of caking and clumping in powders, and the importance of identifying the critical water activity point to prevent loss of flowability, using real-world examples like protein powder and rice bran extract.
-Moisture Migration: An explanation of how moisture moves from areas of high to low water activity, and strategies to prevent this migration, including examples and animations demonstrating the process with powders like whey protein.
-Determining Structural Changes: Understanding the differences between crystalline vs. amorphous and anhydrous vs. hydrate forms of powders, with examples like sucrose and calcium chloride.
-Production Challenges: Addressing common production issues like moisture target precision, variation reduction, avoiding rework, and energy consumption, along with solutions such as automation and operator training.
-The ΔT Approach: A detailed explanation of the ΔT method for correcting moisture variation during production, including how it works scientifically and the benefits of automation and real-time feedback through dashboards.
-AQUALAB Solutions: Presentation of AQUALAB products and services, such as the AQUALAB 4TE, VSA, MAT Software, and SKALA Dry, as solutions to these moisture-related challenges.
The webinar concludes with contact information, additional resources, and a Q&A session.
Dr. Zachary Cartwright: Hi, everybody. We're gonna get started here in just a minute. Thank you so much for coming to overcoming moisture challenges in powders. Just a couple housekeeping items, before we get going.
First of all, my name is Zachary Cartwright. I'm a lead scientist at AquaLab. I just wanna say thank you so much for being here and spending part of your day to, join our webinar. We haven't done a webinar in in quite some time, maybe a a year or two, but we've been getting lots of requests to bring this back and to cover this specific topic.
If you have any topics in mind or any feedback about this new format, please let us know. We'd really like to hear from you and continue to make these better.
Yes. I will be sharing a recording of this after. We will email it out as well as a copy of my slides. We will also put this on our website at some time. But if if you'd like it even sooner than that or if for some reason you don't get it, please reach out directly to me. My email is here on the screen, and I'm happy to get you a copy of this.
Here is my contact, so please write it down now. It will also be at the end of the presentation.
Of course, I will be taking questions at the end and trying to take some questions along the way. But if for some reason we don't get to your specific question, please follow-up with us, and and we wanna make sure that we get those answered. Well, let's go ahead and and get started. We'll be here today for about thirty to forty minutes with q and a at the end.
Now powders are found in many industries. Of course, they're found in the food industry, things like milk powder or or whey protein powder, cocoa powder, cornstarch, and and so on. I I'm sure you're familiar with many of these things, but there's also powders in other industries. Maybe you're joining us today from the pharmaceutical industry and and you work with things like lactose and microcrystalline cellulose, maybe magnesium stearate or another type of powder.
Or maybe today you're joining us from the cosmetic industry and you're working with things like talc or cornstarch or rice powder or something like that. So in all of these industries, there are powders and their challenges are similar no matter what type of powder or what industry you're in. Usually, when I meet with teams to discuss powders, these are the top challenges that I hear. Things like trying to prevent physical transitions.
This could be kicking and clumping or or loss of flowability of a powder. I think that's usually the the biggest challenge that we come across. But you might also be concerned with the shelf life and packaging and and looking for a way to quickly predict shelf life or understand whether or not you're using the right packaging for each of the powders and products that you work on. If you're somebody that blends powders together, then maybe you're concerned with moisture migration between different components or or different powders and wondering what what's gonna happen to the final moisture or the final water activity once we blend, separate things together.
A lot of teams that they meet with, they're concerned with hygroscopicity. They want a really effective, clear way to define how a powder is going to uptake moisture in different environments and compare the hygroscopicity of different powders or different excipients, to each other. Of course, temperature is is always a concern, and understanding how temperature changes may result in changes of quality or or even safety for different products.
Sometimes you you may be interested in looking at assessing the structure, understanding if something is crystalline or maybe if it's going to form a hydrate. And finally, production of powders can sometimes be very challenging and and very tricky to be consistent and avoid as much rework and lost, product as possible. So if if I had you here today, if I could talk directly to you, I I would like to know which of these challenges really stand out to you. Why are you here today? You know, which of these is your biggest pain point? And maybe you can write even in the chat here so I can really understand which of these, are maybe the biggest issue that you and your team were hoping to learn about, today.
As we move on today, our main goals are three things. We want to first understand each of these challenges. We wanna really get to the root cause of the challenge and recognize that all of the challenges that they listed on the previous slide, in some way, they are connected to the water that's in these types of products. And then we wanna talk about overcoming these challenges using the correct moisture insights. And and the reason I say correct is because I see so many teams try to overcome these challenges using moisture content alone, and it really requires an understanding of water activity and also moisture sorption isotherms in over in in order to use science to overcome, the challenges that I previously listed.
And then finally, we'd like to explore some available solutions and talk about different technologies and software and insights that's available for you to use so that you can get get over these challenges really quickly and and stop letting them, keep you up at night and cause you any headaches. We wanna make sure that we can solve these things as quickly as possible.
So today, I'm I'm gonna jump straight into it. I'm going to jump into each of these challenges and go through each of these goals. At the very end, we will jump into the solutions.
So the first challenge was preventing physical transitions. And, basically, what I mean here is is preventing any caking and clumping and loss of flowability.
So, of course, anytime a powder is exposed to moisture or to an environment with a higher humidity, then the powder is going to absorb some water vapor. And this in it moves through basically five distinct steps. First, we have a wetting phase where there might be just a little bit of an initial moisture uptake, then the powder can start to get a little bit sticky. We can start to move towards caking and clumping.
But, really, it's when we reach this agglomeration stage that caking and clumping has really taken off. Once we reach this point, we we've already gone too far. As you go further up, as you continue to, absorb moisture, then some compaction will occur. And finally, we may reach the point of liquefaction where it starts to go into liquid form.
So this process is affected by many different factors, primarily particle shape and size. The smaller the size of the particle, the quicker we're gonna reach the agglomeration phase. But things like temperature and time will also have effect. As temperature goes up, this can cause this to occur even faster at a lower relative humidity, and and we'll look at more on that, here shortly. And also time, the longer you're at a specific temperature and relative humidity, this can also cause us to go through these stages faster.
Finally, any changes in chemical composition and even applied pressure, these things will also change how quickly we're moving through these different stages.
Now when it comes to predicting kicking, it depends on three primary factors. The water activity of the powder, the temperature that that powder is stored at, and the time, the time that it's exposed to different, types of conditions that change by temperature and relative humidity.
And in in order to pinpoint the critical water activity, which we denote here as RHC, in order to know exactly where that critical water activity is, then it's really important that we use a high resolution moisture sorption isotherm. And so if you research isotherms, you're gonna find two methods that are used. DDI, which stands for Dynamic Dew Point Isotherm, as well as DVS, which stands for dynamic vapor sorption isotherm. And this graphic that I have here, it does a a really good job of showing you the difference between these two.
And I want you to pay attention to this orange curve, the DDI, because this is a very high resolution curve, a much faster isotherm to make compared to a DVS curve, which is more of a static curve that's really good for kinetic testing. So this gives you kind of an idea of the difference between this the the two curves. We're not gonna go into a ton of detail on isotherms today. We we have separate application notes and webinars on isotherms themselves.
But, as we continue to move forward today, I just want you to understand that the Dynamic Dew Point Isotherm, or the DDI, is what we really need to use for powders to really characterize how they pick up moisture in a dynamic form.
So, let's go through an example now about determining the critical point. And in this first example, we'll be looking at a protein powder that we're having some issues with some caking and clumping. So for this powder, the first thing we want to do is create the moisture sorption isotherm using that DDI method. You can see that for a very small change in moisture, we have this huge range of water activity and then we're reaching an inflection point where we get a lot of moisture uptake. So once we have the isotherm, we're going to take a a second derivative. Basically, we're using this derivative to look at how the slope of this curve is changing. And, really, what we're looking for here is a peak on the second derivative.
If we relate this peak back to the water activity on the curve, we see that at point six seven water activity, agglomeration has really taken off. And if I look at this second derivative a little bit closer, what I would say is that we have a lot of stability for this powder up to about point five water activity. Then we get a little bit of that initial sticking that we we talked about an, a slider two back. And once we reach point six seven, this is where agglomeration and clumping has really taken off.
So if this was your protein powder, if this was a powder that you were working on, we might set an upper limit of point five as our spec even though the critical water activity is point six seven. Once we've reached point six seven, we've gone way too far. So generally, what I recommend to teams is once you know where this agglomeration or this kicking and clumping or critical point is, maybe set a spec point one water activity units lower than that point to give ourself a little bit of a buffer to make sure that we're never reaching this point where we lose flowability.
Alright. Let's look at a a second example. This, ex example is for rice bran extract. And this is a a really interesting one because when you take the isotherm, you can actually see that there are multiple points here where we're getting some moisture uptake.
And in fact, when we take the second derivative of this curve, we see two critical points. We see one at point four water activity and a second at point six three. And what's going on for this second example is that we may see some caking and clumping in a glass transition point at that lower water activity of point four. And as we get even higher, once we have a second point at point six three, this is where some crystallization or some second type of transition is occurring.
Generally, for any powder, we wanna stay below all of the physical transitions. And in this case, if this was something that you were working with, then we might wanna stay around point three water activity to make sure that we're avoiding any of these physical transitions.
So in summary, when we look at a powder, we wanna take an isotherm, shown here on the top graph. We take the second derivative, shown in the bottom graph, and we're looking for any peak points, and we're using this information in order to set the right spec for each of the different powders, that you want to work on.
Now, when it comes to crystalline powders, I saw somebody here in the chat, asked about, crystal sugar and other crystalline substances.
Crystal powders are things like salts or sugars, certain acids and vitamins, or even active pharmaceutical ingredients.
And these are really unique because they don't absorb any moisture. Instead, the moisture stays on the top, on the surface until there's enough energy or enough water activity to actually break apart the crystal lattice. And when this happens, this crystalline powder will go immediately from a solid form to a liquid form, and and we call that Deliquescence.
So if we look at isotherms for any crystalline powders, this is the type of shape that we see. So here we have Sodium Chloride and also Sucrose and both of them have a similar shape and you'll notice that at a a wide range of water activity, there is almost no moisture uptake. And then we reach a deliquescence point where this suddenly goes into solution. So these have a very unique shape, and it's really easy to pinpoint exactly where that Deliquestance point is for these types of samples.
We can get some kicking and clumping, in crystalline powders. This mainly occurs when there's fluctuations in relative humidity. So if you're going from a high relative humidity to a low and back and forth over and over, basically what happens is we're going through these cycles of deliquescence and crystallization over and over. And as we do that, we start to form some bridges between, the different particles or the different crystals in in these types of powders, and then that can lead to kicking and clumping. So in order to study that, you might want to use some type of isotherm generator or or vapor sorption analyzer where you're able to set different relative humidities and go back and forth to determine how that cycling may lead to kicking and clumping.
One other thing that I wanted to mention here that that I think is really interesting is that the deliquescence point of a mixture so if you take two different types of crystalline powders and mix them together, sometimes that deliquescence point can be even lower than the deliquescence points of the individual components. And and this is just a a really interesting phenomenon. If if you're somebody that really understands why, that occurs, I I'd really be happy, to discuss that with you, in more detail.
Alright. Our second challenge is all about shelf life and packaging. And so anytime you create a powder and it's free flowing, the worst thing that we want is is to package it and then for it to get to your client or to the end user and then it to be all caked and clumped together. So it's extremely important that we package correctly and we determine exactly what the packaging needs are for each of the powders that you're working on.
We can use do this using Fick's law of diffusion. The the equations are here on screen. These are well known, well published equations. They're they're not something that Aqualab came up with, but we have put them in a a form, in a calculator that's really easy to use, and we'll look at that in just a moment.
The other thing that's really important to use and and often where I see the most mistakes is using the right moisture model to make these types of calculations and predictions.
So if we use this approach, we can take into account different types of packaging, including the surface area of the packaging and the amount of product inside the package. We can take into account different storage conditions, so different temperature, relative humidity, and atmospheric pressure. And finally, we take into account the sorption properties, and we turn that into a model as well as the critical point or the critical limit that you've set for the powder to make sure that it's free flowing or to make sure that no microorganisms are going to grow in these types of products. So the
first step is to determine the critical point and then to set the right spec. So let's look at an example for cocoa powder. And for cocoa powder, here's what the isotherm looks like. Again, we're gonna take the second derivative.
We're looking for a peak point on this second derivative, which occurs at point four six water activity. And then using that information, I'm gonna set an upper spec of point three six. So again, giving myself a little bit of a buffer to make sure that I never reach the sticking or the agglomeration phases, through the the kicking and clumping phases.
The second step, and again, this is often where I see the most mistakes, but the second step is to choose the right model and also the right water activity range. So let's say here's our our isotherm and these are different models that we can use. There's three different models here, a linear model, a DLP model, and a GAB model. There are over a hundred models that have been published.
And generally here at AquaLab, we use the DLP, the Double Log Polynomial Model. And you can kind of see it here behind the data. The raw data are the green data points to create the isotherm. And then the model is here in blue.
And you can see that it fits over the data pretty well. But if you were to zoom in on this, I want you to notice that the model increases in moisture and water activity, and then it decreases in moisture slightly before increasing again. And we really want this model to always increase from left to right. If we don't fix that now, then we're gonna get some calculations that don't really make sense.
And in order to fix this, all I have to do is select a smaller range of the data, maybe a range that makes sense for the shelf life calculations that I want to do, and then once again fit the model over it. So it's the same data set. I've just selected a smaller portion, and now I'm just going to use the DLP. And as expected, the model always increases from left to right.
Okay. Now that I've fixed the model, now we can look at using our calculator and and making some calculations.
This is our shelf life calculator, and it's found in the moisture analysis toolkit software. And we'll talk a little bit more about that at the end of this webinar. So let's just walk through an example. Let's say that this cocoa powder is gonna sit at sixty five percent relative humidity.
Let's say that it's around room temperature at twenty five Celsius, and in this case, we're at sea level. We just put in the total dry mass of the product in the package, the surface area of the packaging, and finally, your current water vapor transmission rate. So this value should already be supplied to you by your packaging provider. It shouldn't be a hidden value. It should be something that's readily available to you.
From here, we'll put in the initial water activity. This is the water activity at the time of packaging and then the critical limit that we've set. So again, I'm using point three six. I'm giving myself a little bit of a buffer before I reach that critical kicking and clumping around point four six.
From here, I just use and select in my isotherm. I'm using that smaller range. In the background, it's automatically being turned into that DLP model. And then when I hit calculate, this will give me a shelf life.
So in this example, I have a hundred and twenty five days for my initial water activity to reach the critical limit under the conditions that I've set. So you see, this is a really powerful tool because it maybe took me a day to create the isotherm. And now I can very quickly change all of these different parameters that I may be concerned with. Instead of having to wait months for accelerated testing or or maybe a year for show for a full shelf life testing, this can really quickly give me the insights I need, especially because I'm trying to stay within a very specific water activity range.
There are different versions of this calculator that may be helpful to you. For example, in in this calculator, we can calculate the water activity over time. So everything basically looks the same. The main difference is that I can put in, the number of days at a specific condition.
So let's say that seven days, I'm gonna store the seven days at this specific condition and I wanna know the water activity after that time. Again, I'm gonna use the same isotherm and this time the output is the water activity. So you can use this calculate calculator to simulate maybe at different steps of your process, maybe storing at your warehouse and then sitting in a hot Amazon shipping container and then sitting on shelf and then going to your final user's environment. All of these have slightly different conditions, and this type of calculator would really allow you to break down how the water activity may be moving up and down.
I see a question here about point three six being the spec. And, yes, I'm using point three six as my limit. Even though kicking and clumping really takes off at point four six, I wanna give myself a little bit of a buffer, and I wanna make sure that I'm never having any of that initial sticking, together.
Finally, there's a third version of this calculator in the same software. In this final version, we can calculate exactly what water vapor transmission rate we need, in order to hit a specific shelf life. So again, it looks very similar. The main difference is that I'm gonna put in my desired shelf life.
So in this example, let's say I really need this to last one year and stay within the right water activity range. Again, I'm gonna use the same isotherm and hit calculate And then this time, the output is the water vapor transmission rate I need in order to keep and hit this shelf life. So you could take this value directly to your packaging provider to make sure that you're not over or under packaging, that you're really hitting the sweet spot for each of the powders or each of the products that you work on. I see an another really great question in here about how would you factor in a desiccant pack or or maybe adding silicates or something like that.
These equations don't take those into account. But usually, how I see this, incorporated is when you add something like that, they usually say that this may extend the shelf life by fifty percent or or something like that. And then you could add that, to your calculation as well. And using our equipment, there may be ways to also add desiccant packets with sample in there to really study how the desiccant packet can really help to slow down this process or how that desiccant packet may even affect where the critical point is.
So there are some ways to study that. And if we wanna get into more details, I'm I'm happy to talk to you more, after this webinar.
Alright. Next, let's talk about avoiding and predicting moisture migration. So if you're somebody that blends multiple powders or multiple dry ingredients together, then this section may be really helpful to you. So anytime you mix powders, the water activity of the final product is going to change.
But luckily, this happens in a a very predictable way. And it requires that we have an isotherm for each ingredient or each component that we're mixing together. And then we have to use that same DLP, model, and we can use that to simulate how these different things will mix together. So, for example, if we have a whey protein powder, and let's keep it easy.
Let's say that we just have three components. We have a a whey protein blend. We have maltodextrin, and we have sunflower lecithin. Let's say that each of these has its own unique, isotherm.
Each one has its own unique shape. And using that modeling, we can predict how the water activity is going to come to an equilibrium once these things are all combined and given enough time to to reach that equilibrium point. So just kind of as a a summary, if we have an isotherm for each ingredient, and and we can do this for as many ingredients as you want. Usually, we pick the top five to eight ingredients. But if we do this for each ingredient, once we have their isotherms, then we can predict the combined isotherm using DLP modeling, and you see it here on your screen there in red, and the equilibrium water activity.
Because we can already predict the isotherm, we can use that isotherm to go back and even start to make some different shelf life, calculations. So a lot of teams that use this approach, they make an internal library of isotherms for their powders. And then at your computer, you can simulate and think about what's gonna happen when you mix them, together before you have to physically go out and start to mix all of these different things.
Alright. The next challenge is all about evaluating relative hygroscopicity.
And when it comes to hygroscopicity, this is the tendency of a substance to absorb moisture. Powders do this to to a great extent, especially compared to a lot of lot of other products. And the amount of water that's picked up by powders is really a function of the temperature and the humidity of the environment.
A DDI, a dynamic dew point isotherm, is really a great method to understand how this is done. Again, it's a very high resolution way to really get an idea of how different powders, or different excipients are going to pick up moisture. And this is especially important. If you're somebody from the pharmaceutical industry and you're trying to make a selection among different excipients, then you can use a DDI method to really think about the solubility or the moisture scavenging properties of excipients or even to look at different sorption kinetics and and pinpoint where deliquescence occurs.
So I have an example here. These are just a a list of different excipients or or powders that you may use. And in order to compare relative hygroscopicity, we're just gonna look at how the moisture content is changing with respect to the water activity. So basically, we're just looking at the slope of these different curves.
So if we look at this, I would say that this Cross Carmelos is the most hygroscopic. It's the one here in red. And the reason that I say that is because it has the greatest slope. It's picking up the most moisture as we go up in water activity.
Whereas something like mannitol, you see mannitol here, kind of hidden behind these other ones in dark blue. But mannitol, I would say is non hygroscopic because at a very high water activity, you still have very low moisture uptake.
Other things like sucrose, which is crystalline, is very non hygroscopic until we reach a a deliquescence point and then it suddenly goes into solution.
So really comparing hygroscopicity depends on the slope of these curves, but also the range of water activity and where you're looking on these graphs. So keep that in mind as you try to pick different excipients or as you compare the hygroscopicity of of your different powders.
Okay. Moving on, let's talk a little bit about the impacts of temperature fluctuations and how this can impact both quality and safety.
So when it comes to temperature, as you go up in temperature, this is also going to usually increase the water activity of your products and and your powders. This also lowers the critical water activity so it can lower where the kicking and clumping point happens or even where the deliquescence points, happen. So an example that I always like to share, this is an example for milk powder. And if we create an isotherm at fifteen degrees Celsius, then it kicks in clumps pretty close to about point five water activity.
But each of these is an increase in five degrees up to forty degrees Celsius. And at forty degrees, this is gonna kick and clump closer to point three water activity. So this does a a really good job of helping us understand how the shape of the curve in that critical point is affected by temperature.
In order to predict the critical point in the water activity at any temperature, then we need at least two isotherms, if not three, in order to start to make some predictions. This is done using the Clausius Clapeyron relationship. This is just a mathematical model that we use to estimate vapor pressure at any temperature. And then we can also use a linear regression analysis to really extrapolate what's gonna happen at a wider range of temperatures.
So for example, here we have rice powder, and we have two isotherms that we've created at twenty five and thirty degrees Celsius. So at room temperature at twenty five, the water activity is point four five, and the critical point that we need to stay below is point five five. If I use those equations from the previous slide, then if I extrapolate this out to thirty five degrees Celsius, you can see that the water activity is now above the critical point that we've discovered.
And if I extrapolate this even further, then we see that at seventy degrees Celsius, this is when the water activity has now exceeded a safety limit, and we've gone above the microbial limit of point seven. So by really, extrapolating all this data out, I can pinpoint and understand where a kicking and clumping issue may occur and where a safety issue may occur as it relates to temperature. And, of course, this is gonna change for every different type of powder or formulation that you work on, but I think this gives you an idea of how you can really look ahead and understand how temperature changes may affect quality or safety issues for some of the powders that you're working on.
Okay. Next, we have determining structural changes. Keep in mind that there's different types of structures that we may want to study. And when we look at an isotherm, when we define that relationship between water activity and moisture content, This is really based on the structure of the product. And as the structure changes, then we're gonna see different trends in the data and in the shape of the isotherm.
So this might be for crystalline versus amorphous, powders and we can talk about the degree of transition that's occurred between these two, different types of powders or maybe you're working with an anhydrous versus a hydrate. And again, this is really important for the pharmaceutical industry, especially if you're trying to prevent hydrate formation. So let's quickly look at an example, of each of these starting with crystalline versus amorphous.
So if we look at sucrose, this is what it's going to look like for a crystalline, sample. Keep in mind that crystalline is is very structured. It has this molecular structure and you can see it here in orange. And just like we looked at in the past for crystalline, we have almost no change in moisture content. We reach a deliquescence point, and then this suddenly goes into solution.
However, if we look at this in its amorphous form, this is not not as structured as as before. It's a little bit more random. Then for the second, isotherm, you'll see that we might get at some initial kicking and clumping. We have a little bit of a slope change here at a very low water activity before reaching a deliquescence point, higher in the curve. So we can use this to really understand maybe what type of structure we have based on the shape of the isotherm.
Now, if we're looking at hydrate formation, this is looking at calcium chloride. Hydrate formation has a very, unique shape that we'll see in an isotherm curve. So in this example and and I think these are actually labeled backwards. The the dihydrate is here in orange. But these isotherms move from left to right. We're increasing in water activity and moisture content. We're reaching a point where we suddenly go down in water activity even though the moisture content has gone up, and then we continue along, the isotherm.
So anytime we see this zig zag shape where we have a sudden decrease in water activity with an increase in moisture content before continuing up the curve, this is usually an indication of a hydrate. And what I mean by a hydrate is this is any time that water molecules become trapped or part of the structure of the powder that we're studying. And this can be really detrimental, especially if you're working with an active pharmaceutical ingredient or something like that. We usually want to avoid these hydrates from being formed. And if you know the water activity and the conditions that cause these hydrates to be formed, then you can set the right spec to make sure that we're avoiding that.
Okay. Our final challenge, to go over today is all about production. If if you work in production, if you're on a production team, then you know that this can often, be challenging. And I'm sure that you have some big goals, maybe this year related to energy savings or reducing variation and wondering how how you're gonna hit, those goals.
So some common challenges when it comes to production include hitting your moisture targets and and increasing average moisture in your products, reducing the variation and being as consistent as possible. Of course, avoiding any type of rework or lost batches. We wanna avoid as much waste as possible. Energy consumption, I I know there are some big goals, from teams that we work with in order to reduce the amount of energy and and make sure that we're not overdrying when making these types of products.
Training operators continues, to be very problematic because we've had people in the industry for thirty, thirty five years or longer. And now they're being replaced with people who don't know all the little intricate details about running a spray dryer or running different systems and they need to be trained very quickly. And then finally, a lot of teams are looking towards automation and making this as hands free as possible to consistently make, the same product.
What's needed in order to overcome all of these production challenges is a way to correct variation as it happens. And we need to be able to detect moisture changes before the product leaves the dryer. So a lot of teams currently, they do downstream sampling. They go through the spray drying process or any type of dryer and then they take a reading downstream and they try to use that information to go back and adjust the spray dryer settings.
But usually it's already been twenty, thirty, forty minutes and more product has gone through and it's too late to make the adjustments that are needed. So what we need is an ability to adjust the dryer settings in real time. And what we're looking for is to take our current control. So in this example, the current control is here in orange.
We have a a pretty wide variation. The first thing that we need to do is go from this current control to improve control and reduce that variation.
And once we reduce the variation, then we can increase the average moisture content. So you see here that now the average moisture content has shifted to the right. We still have the same limit, but once we're able to shift this to the right, this is when we get a production increase and we increase our yield, as well as a reduction in the energy that's required to make these types of products. So the way that this works, the science behind it, is that the key number that we need to watch is temperature and not moisture. And I think that's kind of funny for us to say at AQUALAB because we focus so much on moisture and water activity. And we've even tried different ways looking at NIR and different approaches to measure moisture or water activity in line. But what we have found that the key number to watch is the temperature.
Especially the temperature differential, the delta T, that occurs through this process. So delta t works on the principle of evaporative cooling and the temperature differential that this creates. So we're looking at the temperature, the hot temperature at the burner, and then the temperature, after it's been cooled, after it moves through the product, and maintaining the correct temperature differential is, critical in order to hit the correct moisture spec. So as we look into automation, if we're looking at a spray dryer or you might even have a fluid bed dryer that's connected, we're using two different loops and, two different feedback loops in order to automate this process.
The first loop is a fast loop. It's gonna make continuous automatic adjustments based on the data that we're receiving from temperature sensors. And these sensors are almost always already in the drying system themselves. This means that this can be applied without any downtime.
We just have to be looking for the right data and the right numbers. So in this example, in the spray dryer, we're looking at the difference between the hot and the cold points. Or in this fluid bread dryer, same idea. We're looking at the temperature differential between this hot and this cold point.
There's also a slow loop, and the slow loop feedback is coming, is a way for us to verify that the specs are still in the right range, and it allows us to make any long term adjustments. So this comes from any downstream sampling. We still want to conduct downstream sampling and take water activity readings of product after it's gone through this process just to verify that we're continuing to stay on the right road and and headed in the right direction.
The benefits of using this delta t approach is that we can greatly reduce the variability and eliminate any over or under drying of the product. Usually, we see increases in yield between about a quarter of a percent, sometimes up to a percent in extreme cases for powders. For other types of products, things like pet food, we can also use this application and see increases of several percent in moisture content.
Using this approach, we see a lot less operator error, and this is because using these insights gives you a really quick resolution of different drying issues. And if there's any type of mechanical issues, by looking at these specific numbers, you can really tackle that quickly, to make sure that you're improving your efficiency.
Using a a delta t approach allows you to have very clear operating parameters even for different products, and this means that you can reach that steady state production very quickly.
We see lower energy consumption usually between five to ten percent, really depending on the system. Sometimes it can be more or slightly less, but it really, requires an analysis of the current system and and really understanding what improvements can be made. And for this type of system, we see a a very quick turnaround time or a really quick return on investment.
I've seen this done within a month sometimes because if you're a powder producer, if you're somebody producing millions of tons of powders, then you understand that a point two five percent in moisture content goes an extremely long way. So if that's some something that you're working on or something that you wanna learn more about, please reach out to us after this.
Okay. These last couple of slides, I I know we're here at about forty minutes. But just to wrap things up, I just wanna talk about the solutions that we have at Aqualab. Aqualab, we really specialize in the right technologies and solutions for of overcoming all of these different challenges.
Many of you already know who Aqualab is. We've been around for forty plus years. I know our name has changed several times, but Aqualab, our brand, has been around for for quite a while. So in these next couple slides, I just wanna highlight a few of our solutions.
But if you'd like to get together, after this presentation and dive deeper into your specific, challenges and and talk about how we can overcome them, we really want to do that with you. And again, my contact information will be here at the end.
So usually, our our most common device that's used to take quality checks and and also used in r and d to get a single water activity reading is our AQUALAB four t e. I'm just gonna bring up all the the features. I I don't wanna go through all of this, but I just want you to know that this uses a dew point sensor. This is a primary direct way to measure water activity. If you also wanna get moisture content at the same time, then you can connect this to our Scala data or, excuse me, data management system. And you can use that to use an isotherm like we've been talking about to get water activity and moisture content, from the same device.
We talked a lot today about moisture sorption isotherms. These are created using our vapor sorption analyzer. Something really unique about our isotherm generator is the ability to create both of the isotherms that I mentioned earlier. And, again, the DDI, the Dynamic Dew Point Isotherm, is really what we need to very clearly define how powders uptake moisture. Again, here's just some different specs about this instrument. I I don't wanna go into it too much, but I want you to know that we do have a solution for creating these isotherms. And once we create those isotherms, then we can put them into the software that I brought up earlier called the moisture analysis toolkit.
This toolkit has all of the different, tools that we mentioned throughout this presentation.
Things like ingredient mixing, looking for those different transition points, and quickly calculating the shelf life. All of these things are in the software and really easy to use. And if this is something that would be beneficial to your team, I'd be happy to give you a a a more of a rundown of the software and and walk you through, some examples.
And then finally, for production, our our solution here is called Scala Dry. This is our model based control system. It uses that same delta t approach that we talked about a few slides ago. This is a great application if you're using a spray dryer or fluid bed dryer or really any type of dryer this can be applied to.
It provides really early and precise control. It's using the moisture, leaving the product. We're looking at the moisture, but we're focusing on the temperature differential to hit the moisture spec. And we can take into account that differential as well as the production or the feed rate to make sure that you're as consistent as possible.
So I know that that was just a quick highlight. I will send out a copy of these slides. There are points in here, like here you can click to learn more, so it's really interactive.
There are lots of other things as as well that you can click on throughout the slideshow that that may be helpful for you.
Just to kind of wrap things up, I I just wanna return quickly to our goals. Our goals today were to understand each of these challenges. If there was a challenge that we meant missed or something that you would like us to go over in the future, please let us know. We talked about overcoming each of the challenges using the correct moisture insights. You can see that includes understanding water activity and using the right type of moisture sorption isotherms.
And then very quickly, we highlighted and explored available solutions. And if you'd like to talk more about those, in the future, please reach out to us.
There are lots of different, additional resources, that you can go into. It looks like we have a a question here, that just came on screen. So if we try to determine the clumping point of a sugar substitute, what will be a reasonable step by step approach? Should we take the samples and test them throughout all phases from free flowing to clumping or is there a smarter way to do this?
Okay. Great question, Mofin. So in order to determine a clumping point, what we would want to do is we would take, a sample that's free flowing before it's clumped and if you're using a different sugar substitute then we would probably want to compare the original powder and then also the one with the sugar substitute. We would create the dynamic dew point isotherm.
And then using that, then we would, compare the shapes of the curves and use that second derivative analysis to really pinpoint and understand how that sugar substitute is affecting, where we see the the kicking and clumping points.
Okay. We have, another question that just came in. Does increase or does decreasing the moisture below BET mono layer moisture solve all of the problem occurred in powder like caking, flowability, and stability?
Usually, decreasing the moisture can be helpful, but we wanna really focus on decreasing the water activity because water activity is a much higher resolution measurement. And it's gonna really help us understand where we are on the isotherm to make sure that we're low enough in terms of water activity. Again, I see lots of teams try to do this looking at moisture alone, but most moisture methods don't have the resolution that we need to be to to get the insight that we need to prevent, the kicking and clumping.
Great question, Adit.
Okay. Thank you guys so much. In my presentation, there were a few additional resources.
I'm not sure if, my producer can bring my my screen back up, But there are some additional resources in the presentation that you can go through. We have all types of different application notes, videos, previous webinars, and so on. So there are lots of additional, resources here in my presentation as well as on our website.
To to finish things up, here's my contact information. If you know your regional AQUALAB advisor, please feel free to reach directly out to them. But if you'd like to reach out to me, if you have more technical questions, please follow-up. And of course, I I always like to plug our podcast is called The Drip. This is where we focus on some science, some music, and some mantra. Please listen and subscribe. If you're interested in in even being a guest on our show, please reach out as well, and and we would love, to explore that opportunity with you.
So we have just a a few minutes left. Thank you so much for staying the entire time, but I would like to take some questions at this time if there's any out there.
Alright. Thank you, Daisy, for your comments. Thanks, Julio.
Thank you, Eric.
Yeah. Thank you all so much for being here. I'm just gonna take just a question or two. I have one that just came in. Somebody's asking, what does delta t actually look like for the operators?
I have an example here. Let me see if I can pull it up, quickly.
Here's an example of what it actually looks like if you're running delta t. I know there's a lot on the screen, but I I just want to point out a few things. Here in this blue, this is that fast loop. This is where you're able to set the delta t that you need to keep. And then over here to the left, this is the slow, automation automation feedback loop. This is where you would enter your water activity of samples downstream.
All of these zones here to the upper left, these are the temperature differentials of different zones within the dryer. And then you can also put in the feed rate. And once you put all of this in, then you can very quickly see on screen how you can take all this variation and reduce it and then be much more consistent. So you can think of the Delta T approach as a way of of turning on cruise control and really keeping things within the right limit once you have started to reach it. You can turn that off at any time and and go into manual mode. But really, the this is designed to hit that cruise control, especially if you're having a a long run time.
Let's see.
Any other questions?
I have another one that just came in. When using the isotherm predictive modeling for a blend of different powders, does this only apply when powder powders are blended in equal parts, or is there a way to correct for percent composition? This is a great question, Faith. So using the DLP modeling, you can take into account different mass ratios.
And so when you use our software, you put in the ingredients, you select the isotherm, you put in the starting water activity, and then you put in the amount. So you can play with different mass ratios, and that will affect the final isotherm as well as the equilibrium water activity that's calculated. So, yes, that can be taken into account. Thanks, Faith.
Why don't we take, if there's one final question, we can take that now. If there's any more Alright. Let's take this final question. Thank you, Tania. Did you do a comparative study between an experiment with a real sample using packaging x and what was estimated in the software to validate the calculations?
Yes. We often do this with clients that we work with. We do validation studies directly with our clients, to prove that they can completely or at least partially replace some of their shelf life testing, with these types of insights. So using these calculations is not a perfect replacement for shelf life testing.
Most of the time, our calculations underestimate the shelf life by about five to ten percent. And I I think this is actually more desirable instead of overestimating so that you can keep that in mind. But it does require sometimes validations test testing to make sure that we're on the the the same page or the right page. But we have done this in the past with different types of products directly with our clients.
So I I think that we should continue to work on this. If you're somebody researching this and you would like to work on a research product, then we're happy to to work on that with you.
Well, thank you guys so much for being here. I I know we went, a little bit long, but there was lots to cover today. I hope these insights were really helpful. Again, if you have any suggestions for topics for future webinars, please reach out to us. If you'd like to go over costs, I see a question in here about costs. Please reach out to me. We'll put you in contact with your AQUALAB advisor to give you the correct pricing information.
Thank you guys so much again for being here. I I hope you have a great, rest of your day, and hopefully we'll see you at the next webinar. See you next time.