Last year, everyone was talking about optimizing processes. Today, it’s energy optimization.
Soaring energy prices are threatening profit margins for manufacturers around the world. In this risky environment, the challenge becomes: how can you reduce energy consumption without affecting the quality of your products?
Machine learning can help you achieve this impossible-seeming goal. Powered by sophisticated algorithms, these solutions can take in the complex volumes of data generated on the factory floor and figure out how to do the same thing with less energy—whether by lowering temperatures and shortening process times, or taking advantage of alternative energy sources.
Here are 3 ways machine learning can optimize energy use:
Figuring out how to lower temperatures without risking quality
Turning down the temperature for a given reaction is one easy way to use less energy. But if you lower it too much, your product quality will suffer. Where to set the levels without harming your products? That’s where machine learning comes in.
One example comes from continuous commodity chemical factories, which face the challenge of prolonging catalyst life while keeping energy costs low and quality within specification. With a machine learning solution, engineers can run an analysis that quickly models catalyst performance and tells them how to adjust feed rates and steam to operate more energy-efficiently. This allows them to save unplanned downtime and energy costs for time periods where they might be otherwise operating with highly inefficient catalyst.
Internal data scientists can set up similar recommendation engines; however, typically, this takes weeks and lengthy back-and-forth collaboration between the engineering and data science teams. A machine learning analysis can be set up in as little as two hours and doesn’t require input from data scientists, hastening the time between idea and efficiency improvement.
Figuring out how to shorten process times without risking quality
In an effort to avoid costly failure, operators will typically err on the side of caution, using more raw materials than they may need and lengthening process times to where they can be certain of success. Machine learning software can help you avoid such inefficiencies.
Say you’re making a paste that must be dried to a specific level. Operators might over-dry it, just to be safe. With machine learning, this process looks completely different. Software can tell you how long to dry the ingredient in order to achieve the desired quality. That way, you’ll not only save energy, you’ll also save money—increasingly important as energy costs rise.
Helping teams adapt to alternative energy sources
Many companies are shifting towards new mixes of energy sources, striving to meet their green goals. However, this also introduces variability into the process, which can result in new kinds of waste.
In the cement industry, for example, many companies are achieving their sustainability benchmarks by changing the energy they use to produce heat, often to waste energy sources. This has a strong environmental benefit, avoiding landfilling and also getting energy from waste and even biogenic sources. But introducing these fuels can create variability and process instability, resulting in overconsumption of energy and potentially lower quality or off-spec product.
With machine learning-powered recommendations, cement companies can understand the parameters needed to minimize process instabilities. This reduces uncertainty and allows companies to maximize the substitution of fossil fuels with waste-derived energy.
In today’s environment, saving on energy costs is crucial. Companies that employ creative solutions to optimize their factories will likely see benefit in the years ahead.
