Joshua D. Howe, Postdoctoral Researcher, Georgia Tech: The Future of Chemical Engineering

Josh Howe is conducting postdoctoral research at Georgia Tech under the direction of Prof. David Sholl. He earned his PhD in chemical engineering at the University of California, Berkeley.

During AIChE’s centennial year of 2008, AIChE interviewed chemical engineers to learn their perspectives on the profession’s future. In today’s blog post, Dr. Howe presents his visions for chemical engineering post-2018.

Looking 25 years into the future, how do you expect your industry/research area to evolve?

My research is in the computational study of materials for energy and sustainability, especially nanoporous materials. 

The past ten years have seen great advancements in our ability not only to handle and analyze large sets of data, but also in our ability to model materials with a high level of accuracy due to development of more powerful and more accurate computational tools. Over the next 25 years, these technological opportunities will pave the way for such large data sets and increased modeling capabilities to interface with traditional tools for process modeling and optimization. This is particularly relevant to the modeling of adsorption separation processes and predicting the behavior of novel sorbent materials. A great challenge related to generating and gathering large amounts of material data is how to meaningfully and effectively use these emerging resources, a problem we’re only beginning to tackle with “big data” approaches to materials chemistry problems. Opportunities exist to develop next-generation tools to aid in the utilization of the wealth of data we generate for designing, optimizing, and controlling processes. 

There is need in the materials chemistry community to not only understand the sources for observed variations in material properties, but also to understand how to best describe materials characterization in light of this understanding. 

The direction of the market opportunities with regards to anthropogenic carbon emissions mitigation — which within the United States is currently driven largely by political forces — is more difficult to predict in terms of how this will manifest as a force for directing the future of chemical engineering. It is highly likely, however, that climate concerns will come to dominate the landscape of opportunities at least in the energy and sustainability sector in coming years.

Core areas of ChE expertise are being augmented by new expertise in science and engineering at molecular and nanometer scales, in biosystems, in sustainability, and in cyber-tools. Over the next 25 years, how will these changes affect your industry/research area?

The developments of new tools, particularly in sustainability and cyber technology, will strongly direct my research area over the next 25 years. New developments in my field are driven almost entirely by emergent understanding of nanoscale phenomena and how we can apply these observations in novel ways. As new tools are developed, so will our efforts evolve to utilize newly available resources. With the current trends toward large databases of empirical and modeled data, the availability of these data will favor those who can effectively navigate it to extract useful information, or who can build infrastructures that take advantage of this wealth of data. 

Sustainability will continue to be a large driver of research in materials chemistry as we work to design materials for targeted applications that are energy efficient, stable, and made of abundant materials. As we learn more about how to control separations at the nanoscale, our ability to develop novel materials in sustainable ways will continue to improve.

Chemical engineering has been and will continue to be a very diverse field at the intersection of many disciplines.

What new industries/research areas do you foresee?

With the availability of large material datasets in the form of databases, approaches exclusive to having lots of data are suddenly much more easily accessible in materials chemistry. Historically, we have often based our evaluations of materials on direct comparison of sets of measurements on a single material, or validated our modeling results against such measurements. The availability of more data for comparison enables us to take more of a “big data” or consensus approach to evaluating materials and validating modeling results. There is need in the materials chemistry community to not only understand the sources for observed variations in material properties, but also to understand how to best describe materials characterization in light of this understanding.

There is likely a wealth of replicate data that is unpublished and therefore widely unknown, and figuring out how to incentivize and credit release of this data to build on our understanding of materials will be a key to advancing the field in coming years. This trend is expected to continue to impact other fields of chemical engineering and will influence how we think in the future about chemical engineering problems.

Taking into account the ongoing evolution of the professions — including the need for new modes of education; high standards of performance and conduct; effective technical, business, and public communication; and desires for a more sustainable future — what do you think the chemical engineering profession will look like 25 years from now?

The future of chemical engineering remains in the expertise related to acquiring, interpreting, and applying data. Chemical engineering has been and will continue to be a very diverse field at the intersection of many disciplines. Future chemical engineers will need to be even more adept at incorporating the diverse sets of expertise coming from related fields and bringing them to bear on specialized problems in our core areas of transport, separations, and the like. The core motivations and professional needs highlighted above will remain central to success as a chemical engineer, and changes will be driven by the need to effectively process and utilize massive datasets.

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