It is a well-established fact that chemical manufacturing can produce harmful byproducts and emissions. Now, a new EPA rule aims to reduce those emissions by requiring plants to tighten controls over their processes.
Proposed April 6, the rule would affect roughly 200 facilities across the US. It aims to slash ethylene oxide emissions nationwide by about two-thirds and chloroprene by three-quarters from 2020 levels. Emissions that worsen smog would also be reduced.
If this rule goes into effect, it will require many chemical manufacturers to monitor and report new types of emissions. Plant managers will have to run more lab tests to check for concentration levels and implement monitoring tools if they have not done so already.
Here are 3 ways artificial intelligence and machine learning technology can help chemical companies comply with the new regulations:
- Predicting hard-to-measure emissions in real time: Machine learning algorithms can analyze current and past data from sensors and process control systems and provide accurate emissions predictions in real time, so you know when you may be at risk of exceeding regulated levels. These solutions can also create overall reports that cover specific time periods, so you can estimate your total emissions over months, or even years. With such a holistic view of your emissions profile, it’s much easier to identify hotspots and optimize emissions reduction strategies.
- Helping you understand how your operations relate to emissions: Mapping out the relationships between how you operate and your emissions numbers is often not straightforward. Machine learning software can help you understand exactly how specific inputs in your process relate to your emissions; it can also suggest, based on that data, which process changes will minimize emissions. This helps chemical companies proactively make setpoint changes in their process to ensure environmental compliance while still meeting business-driven production goals.
- Providing a testing ground for operational changes: You may have in mind operational changes to reduce emissions, but how do you know they’ll work? A digital twin can let you virtually test out various changes ahead of time so you don't inadvertently increase emissions. Using the digital twin, engineers and operators can experiment with different process changes and understand their impact on the overall production process, identify potential risks or disruptions, and optimize performance without affecting the physical production environment. Digital twins can even be used to help explore how to respond to abnormal operations conditions, such as startup, shutdown, or unexpected spikes in production.
As sustainability shows up at the forefront of the national conversation, we can expect there to be an increasing amount of emissions regulation. New technology can help plant and business leaders adapt seamlessly to this regulation so it doesn’t cause headaches—and can even make companies more profitable.