The promise of artificial intelligence in chemical engineering: Is it here, finally?

Abstract

The current excitement about artificial intelligence (AI), particularly machine learning (ML), is palpable and contagious. The expectation that AI is poised to "revolutionize," perhaps even take over, humanity has elicited prophetic visions and concerns from some luminaries.1-4 There is also a great deal of interest in the commercial potential of AI, which is attracting significant sums of venture capital and state-sponsored investment globally, particularly in China.5 McKinsey, for instance, predicts the potential commercial impact of AI in several domains, envisioning markets worth trillions of dollars.6 All this is driven by the sudden, explosive, and surprising advances AI has made in the last 10 years or so. AlphaGo, autonomous cars, Alexa, Watson, and other such systems, in game playing, robotics, computer vision, speech recognition, and natural language processing are indeed stunning advances. But, as with earlier AI breakthroughs, such as expert systems in the 1980s and neural networks in the 1990s, there is also considerable hype and a tendency to overestimate the promise of these advances, as market research firm Gartner and others have noted about emerging technology.7 It is quite understandable that many chemical engineers are excited about the potential applications of AI, and ML in particular,8 for use in such applications as catalyst design.9-11 It might seem that this prospect offers a novel approach to challenging, long-standing problems in chemical engineering using AI. However, the use of AI in chemical engineering is not new—it is, in fact, a 35-year-old ongoing program with some remarkable successes along the way. This article is aimed broadly at chemical engineers who are interested in the prospects for AI in our domain, as well as at researchers new to this area. The objectives of this article are threefold. First, to review the progress we have made so far, highlighting past efforts that contain valuable lessons for the future. Second, drawing on these lessons, to identify promising current and future opportunities for AI in chemical engineering. To avoid getting caught up in the current excitement and to assess the prospects more carefully, it is important to take such a longer and broader view, as a "reality check." Third, since AI is going to play an increasingly dominant role in chemical engineering research and education, it is important to recount and record, however incomplete, certain early milestones for historical purposes. It is apparent that chemical engineering is at an important crossroads. Our discipline is undergoing an unprecedented transition—one that presents significant challenges and opportunities in modeling and automated decision-making. This has been driven by the convergence of cheap and powerful computing and communications platforms, tremendous progress in molecular engineering, the ever-increasing automation of globally integrated operations, tightening environmental constraints, and business demands for speedier delivery of goods and services to market. One important outcome from this convergence is the generation, use, and management of massive amounts of diverse data, information, and knowledge, and this is where AI, particularly ML, would play an important role. Some of these are application-focused, such as game playing and vision. Others are methodological, such as expert systems and ML—the two branches that are most directly and immediately applicable to our domain, and hence the focus of this article. These are the ones that have been investigated the most in the last 35 years by AI researchers in chemical engineering. While the current "buzz" is mostly around ML, the expert system framework holds important symbolic knowledge representation concepts and inference techniques that could prove useful in the years ahead as we strive to develop more comprehensive solutions that go beyond the purely data-centric emphasis of ML. Many tasks in these different branches of AI s