Guest Editorial: Drying Technology—The Next 25 Years

Congratulations to the many contributors to Drying Technology for 25 years of publication resulting in a much improved understanding of drying science and technology. And congratulations to Arun Mujumdar for his vision and leadership for Drying Technology and the International Drying Symposium (IDS). Sometimes we fail to observe growth in the short term and only in reflecting over a longer period do we observe the extent of the growth. Having just completed editing a section of the eighth edition of Perry's Chemical Engineers' Handbook, the progress in drying technology has been quite remarkable. Both the Journal and IDS have created connections and a community of drying enthusiasts that have made a major impact on our understanding of the drying process.

Now is a good time to reflect on the major challenges to address in the next 25 years. We need to enable greater drying innovation. I see the challenges falling into four major areas:

1.	Better understanding of the underlying transformations of the drying process and the mechanisms controlling these transformations.
2.	Better understanding of moisture binding, hydration, and moisture solvation.
3.	Creating greater capabilities to learn at the small scale: to “make our learnings on a small scale and our profits on a large scale.”
4.	Capturing our knowledge in “living models” that enable us to move from “data rich, knowledge poor” to “knowledge rich.”

TRANSFORMATION UNDERSTANDING

Most drying unit operations really consist of multiple transformations. For example, key transformations for spray drying include atomization, agglomeration, and attrition in addition to the drying transformations itself. Drying is a useful transformation and attrition is usually a harmful transformation. It is generally helpful to explore the mechanisms that control these transformations such that we can further improve the positive and reduce the negative. Certainly, in many industries, the benefits of a deeper transformation understanding can lead to improvements in key product quality attributes. For example, a monodisperse particle size distribution would be a clear product quality improvement for most products that are spray dried. This deeper level of transformation understanding requires that we dig deeper into drying phenomena. This implies measuring things we have not measured previously, perhaps because the measurement capabilities did not exist. And this frequently requires that we study the transformations at smaller and smaller scales.

MOISTURE BINDING, HYDRATION, AND SOLVATION

As noted above, drying has a great influence on product quality attributes. This makes it important to understand the “binding mechanisms” of drying. How is moisture held in the product and what are the possible consequences with regard to product quality when it is removed? Some business sectors (foods) have explored this area fairly extensively and others have not. For example, managing moisture in food products has long been an area of extensive research by Levine, Slade, Karel, Roos, Labuza, and others. We need to build on and extend this knowledge. And we need to connect and effectively share information across industries.

LEARNING ON A SMALL SCALE

We need to “make our learnings on a small scale and our profits on a large scale.” ^[1] There is no better example of this today than in the field of genomics and biotech. The human genome was mapped just six short years ago and yet today we have the GeneChip®. ^[2] A genome chip contains well over 30,000 genes—every gene in the human body. These chips can be used to screen new beauty, health, and pharmaceutical actives better, faster, and cheaper.

This begs the question: where is our gene chip for drying? I do not yet expect nano or microscale capability, but I would like mini-scale capability. Where is the mini-scale spray dryer? We have these dryers today, but they do not produce products that are identical to those made on full-scale dryers. A lab-scale spray dryer produces powders just a few microns in diameter. Consequently, they have limited learning capability. The challenge is to produce the same key transformations in a mini-scale dryer as those in the full-scale dryer. This is a difficult but obtainable objective.

CAPTURING OUR KNOWLEDGE IN LIVING MODELS

Think of a “living model” as an “open source” model that gets better and better, deeper and broader as it is used and improved. Models are a great way to capture and apply data in a form that creates significant value for the user. We have some models today, but they tend to be supplier, industry, or academia specific. And we do this because of the perceived benefits of our proprietary knowledge. We need to identify some enablers to go beyond the current state to more of an “open source” situation for sustainable improvement.

We can think of models or simulations as the smallest scale (virtual scale), to make our learnings. Good models enable us to test scenarios that would otherwise be cost prohibitive. Hence, they enable greater innovation than is possible with even the smallest scale physical prototypes. We need good models across both the science and technology of drying covering process, product, sorption isotherms, psychrometry, and the like.