3 Ways AI is Helping the Chemical Industry Address Process Challenges

Canvass blog outlines the top 3 AI use cases for the chemicals industry.
2 min
3 Ways AI is Helping the Chemical Industry Address Process Challenges

The chemicals industry faces several challenges today, including high costs of raw materials and energy, asset reliability, and increased incidences of production disruptions. For example, ARC Advisory Group estimates that up to 5% of production is lost to unplanned downtime.

What’s more, the cost of just three days of unplanned downtime can cost a plant more than $5 Million. Beyond their plant floor, the chemicals industry is also facing a sustainability challenge. According to the U.S. Energy Information Administration, the chemical industry accounts for about 10% of global total final energy consumption and 7% of greenhouse gas emissions.

Thankfully, advancements in AI mean that chemical companies can now address these challenges. Here are our top 3 use cases for the chemicals industry:

1.     Optimizing production to improve process efficiencies

A large chemical manufacturer used the Canvass AI platform to improve process efficiencies and Overall Equipment Effectiveness (OEE) metrics. The company was facing challenges in areas of raw materials, equipment, and energy. Using the Canvass platform, they quickly realized that there was a direct correlation between the energy levels in the furnace and the final product. The process engineers built a Machine Learning model to optimize the production and improve the product quality and yield. The predictive model recommended the energy inputs in real-time based on the operating conditions and raw materials being added to the furnace. This helped them enhance quality and optimize energy usage.

2.     Enhancing asset performance to reduce unplanned downtime

A chemical company was grappling with high operations and maintenance costs because of unplanned downtime. The company’s maintenance engineers used the Canvass AI platform to forecast and predict asset failure. With the aid of failure predictions, the maintenance team were able to avoid unplanned downtime and minimize production losses associated with this equipment.

3.     Optimizing energy consumption to reduce costs and CO2 emissions

After struggling to deal with high energy consumption, the chemical company used Canvass AI to accurately forecast their fuel and energy consumption of the boilers across this facility. This helped them to bring down their OEE metrics by 20% and reduce their overall Greenhouse Gases.

Our field-proven AI solution is helping the chemical industry reduce energy costs, increase yield, reduce raw material spend, improve quality of products and for asset monitoring and optimization. We are enabling the industry leaders to make proactive, predictive decisions to impact the outcome throughout the process.

Get in touch with us today to find out how we can help you optimize your production processes to accelerate your journey towards your business goals.

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