One of the main ongoing concerns for manufacturers is how to enhance productivity. This includes analyzing, measuring, modeling and implementing specific actions to optimize their production lines.

The reason production optimization plays a huge role in manufacturing, is because it is a core means to increase revenue and reduce costs. Process failures in production cause companies to suffer from losses in quality and yield – which translate directly into revenue loss.

Let’s take a look at the chemical manufacturing industry, as the birth of the heavy chemical industry coincides with the beginning of the Industrial Revolution. The chemical industry comprises about 15% of the US manufacturing sector,  manufactures more than 70,000 different products, and is responsible for 90% of our everyday products.

The Challenges Chemical Manufacturers Face

Just as broad as the chemical manufacturing industry is, so are the process optimization challenges it faces.

In order for chemical manufacturers to optimize production lines, they need to address different process inefficiencies, such as the formation of undesired side products, process instabilities, losses due to impurities and more, on an ongoing basis.

Given the complexities of chemical manufacturing, it’s extremely time-consuming and difficult to understand the root causes for these process inefficiencies, let alone anticipate when they are going to happen. Often times, it is the specific behavior of the combination of multiple production parameters, or tags, that cause the inefficiency to happen.

A growing number of chemical manufacturers are turning to Industrial Artificial Intelligence solutions to identify and anticipate process inefficiencies leveraging methods of supervised and unsupervised machine learning.

According to recent research by Accenture, companies that have implemented Industrial AI in the chemical sector are seeing big benefits—a whopping 72 percent report a minimum 2x improvement in some process KPIs, and 37 percent a 5x improvement. For example, a manufacturer of Ethylene Dichloride implemented process-based Industrial AI to solve a number of process inefficiencies, and by doing so increased yield by €1.7M in less than 12 months.

With the capabilities Industrial AI has to offer, chemical manufacturers can utilize their data, improve their processes, and continually adapt them.

How Industrial AI is Revolutionizing the Chemical Manufacturing Industry

Chemical manufacturers need to identify and avoid process inefficiencies to improve chemical process control.

A production disturbance is any unintentional event in the chemical production process that leads to process inefficiencies, unplanned stoppages, rework, or scrap

By implementing Industrial AI solutions to chemical production lines, manufacturers have the ability to leverage different AI technologies that are critical to identifying production disturbances and optimizing production:

  • Real-time data connectivity and capture – manufacturers use  industrial IoT connectivity to securely connect to the production line assets and capture data in a central time-series repository
  • Process-based machine learning – manufacturers use process-based AI to get visibility into the full manufacturing process in detail, and holistically, and to discover and surface process issues that need attending.
  • Digital Twin visualization – manufacturers use a digital twin, which is a virtual representation that matches the attributes and operational metrics of a “physical” production line through the captured production-line data. This enables production teams to quickly pinpoint performance anomalies and their root cause, providing them with actionable insights, and presenting them in the context of the production line. This eliminates the need for data scientists.

 

AI chemical

 

Let’s dive a bit deeper and look at how specific Industrial AI technologies can be used to identify, anticipate and prevent chemical process inefficiencies:

The first step manufacturers should take in order to identify the specific process inefficiencies, is implementing digital twin visualization for them to easily track their main KPIs and receive actionable insights of process anomalies.  

Then, Automated Root Cause Analysis can be performed in order for them to get fast and accurate insight into process inefficiencies. The Automated Root Cause Analysis enriches historical and real-time asset data, and applies machine learning algorithms to automatically trace the causal chain of events leading to production failures.

Once process inefficiencies have been identified by using the analyzed data, it’s important to translate the data into insights. This can be done with Industrial Predictive Analytics.

Machine learning algorithms can be implemented to identify relevant events and predict their outcomes.

By having the ability to prevent specific inefficiencies and production disturbances, process teams can increase production yield while preventing failures at the same time.

Summary

By using process-based machine learning, manufacturers get focused and contextual predictive alerts. This is a huge opportunity for chemical manufacturers, since operational technology (OT) data is already well organized and captured within data historians.

Leveraging this data with process-based AI means being able to pinpoint the root cause of process disturbances with extreme accuracy, and predict process instabilities and failures before they have the chance to affect production.

So with Industrial AI, chemical manufacturers can reduce quality and production losses, saving them great amounts of time and money.

 

Ready to get started with process optimization, driven by data and machine learning?

Request a demo of Seebo Process Optimization today