Crossing borders

A few months ago I found myself (a chemical engineer with a Ph.D. in mechanical engineering) reading a 40-year old article from the Journal of Theoretical Biology in preparation for a meeting with a colleague, Krista Yu, from our School of Economics at De La Salle University. The article, which upon further inspection turned out to be the work of a fellow engineer by the name of Bruce Hannon, outlined how energy flows in natural ecosystems could be modeled using a set of linear equations bearing strong structural resemblance to Leontief’s celebrated, Nobel prize-winning input-output framework used to describe economic systems. My own interest in input-output models dated back to my own Ph.D. work on life cycle assessment (LCA) and industrial ecology (IE) which make use of similar linear calculations to represent complex networks of connected processes. At the time that I read the article, I had just recently become aware of more recent extensions of input-output techniques for disaster risk modeling, developed in 2001 by the research team of the renowned risk analysis guru, Yacov Haimes, of the University of Virginia. The reason for the meeting that day was the aforementioned colleague’s interest in this field as the topic of her own Ph.D. research; the reason for my reading Hannon’s paper was a personal habit, developed during the course of a productive research career of a decade or so, of seeking clues to solving research problems from seemingly unrelated fields.

While it remains to be seen if the hours spent reading Hannon’s article will eventually bear fruit, I still believe that this sort of willingness to look beyond the confines of one’s discipline is an essential habit for a researcher seeking to do significant work. There are, in the scientific literature, countless examples of such success stories drawn from insightful recognition of similarities of analogous problems from different disciplines. Here I will mention a few that I have had experience with in my own career. For example, in the field of artificial intelligence, various computational algorithms have been proposed based on analogs of natural phenomena. Simulated annealing (SA), for example, was developed in the early 1980s by a group of researchers at an IBM lab using mathematical analogies with the behavior of atoms in gradually cooling crystals. Genetic algorithm (GA) performs calculations on populations of coded strings of binary digits under selection pressure, just as the DNA profiles of organisms evolve due to natural selection. Other algorithms mimic the behavior of animals. For example, particle swarm optimization (PSO) makes use of groups of virtual agents (behaving like stylized flocks of birds, or schools of fish) to find near-optimal solution to difficult optimization problems; this algorithm, which now finds applications in numerous problem domains, was the product of the unlikely collaboration between an electrical engineer and a social biologist!

Other notable examples are evident in the evolution of the field of pinch analysis, which was originally proposed in the 1970s as a means of identifying optimal energy budgets (i.e., “targets”) through heat recovery in industrial plants. In the late 1980s, recognition of the parallels between heat and mass transfer phenomena led to a new range of pinch analysis applications for efficient material recovery in industry. Over the past decade, many more applications of pinch analysis have been proposed (including some of my own work) such as production planning, human resource allocation, and energy sector planning (under various environmental constraints such as CO2 emission or water footprint limits). Likewise, a relatively new discipline which is often referred to as industrial ecology (IE) first came into focus following the publication of an article by Frosch and Gallopoulos in Scientific American in 1989. One of the fundamental tenets of this discipline is the closing of material flow loops in industrial systems, in an attempt to emulate the largely cyclic flows that exist in natural ecosystems. While IE has yet to achieve this sort of closed-loop metabolism in practice, awareness of the thermodynamic ideal has grown, along with a whole array of tools and metrics to put hard numbers to the often vague concept of sustainability.

The list of such stories goes on and on. The point here is that researchers, rather than being bound by textbook definitions of their disciplines, should be willing to step outside their comfort zones and invest in a bit of time and effort to seek insights from other fields. This sort of intellectual enrichment is somewhat missing in local research culture, which is perhaps why the Philippines is slowly slipping behind neighboring countries in various key metrics of scientific productivity. Of course, it’s not too late to turn the tide. But we do need the new generation of young researchers to be willing to embrace a new mindset, and to be eager to cross borders.

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Prof. Raymond R. Tan is a university fellow and full professor of Chemical Engineering at De La Salle University. He is also the current director of that institution’s Center for Engineering and Sustainable Development Research (CESDR). He is the author of more than 80 process systems engineering (PSE) articles that have been published in chemical, environmental and energy engineering journals, including three highly cited papers in IChemE (Institution of Chemical Engineers, UK) journals. His Scopus h-index is 20 and he is a member of the editorial boards of the journals Clean Technologies and Environmental Policy, Philippine Science Letters and Sustainable Technologies, Systems & Policies, and is co-editor of the book Recent Advances in Sustainable Process Design and Optimization. He is also the recipient of multiple awards from the National Academy of Science and Technology (NAST) and the National Research Council of the Philippines (NRCP). He may be contacted via e-mail ([email protected]).