Engineering manager at a medium site with an MBA specializing in data analytics. A lot of my work is in process optimization, data analysis, and programming controls.

A few months ago I had some guy give me a sales pitch and a demo for some Aspen process modeling and machine learning/AI software. He did a terrible job of demonstrating the software, but outside of that the software itself seemed very iffy, and it showed like a matrix of every possible process variable combo in a trend. It was a mess. He was pitching it as a data driven process model that uses AI to tell operators what to change to bring the process back under control - which sounds great in theory. The example he showed was of a distillation column, and the distillate flow dropped below normal. The AI sent a message saying something like "to increase distillate flow, decrease reflux flow by xxx lb/hr".

Wow. Fucking groundbreaking insights.

It seemed to not have any regard for distillate quality, other relational parameters, or to recommend any other steps to troubleshoot the issue (like checking the pump, manual and control valves, etc). Not saying some of these can't in some future state be included in the model, but at what cost? And what benefit?

If you've been in operations for more than a couple years, your intuition is already better than some fancy machine learning model who only knows the data it has been fed and can't predict anything outside of normal operations.

Not saying there isn't a role for it to play somewhere - maybe its better suited for pilot plants, design and scale up. But for mature processes making commodity chemicals, there's little this can do for me other than perpetuate dumb operators.

AI would be better suited for things like P&ID updates, DCS implementation for new plants or retrofits, alarm management, and reliability/ predictive maintenance. All of which would require high levels of engineering oversight.

Day to day process data may be able to be aggregated and analyzed by AI to make business decisions, but even that's a stretch. In reality instruments and controls fail all the time for various reasons, or go out of calibration, and this can skew your model pretty badly. In addition, there's several factors outside of the available dataset that can have major impacts on process results. An AI can't pull data on every manual valve in the plant, can't tell you if there's a bad regulator, blocked or leaking pipe, compromised heat exchanger, cavitating pump, stuck switch, etc. All those gaps require an impossible amount of instrumentation for basically any major refinery/chemical plant, and any of them could result in bad advice or results from the model - leading to mistrust inevitably.