Quality and yield losses are a perennial problem for most chemical manufacturers; from quality variabilities, to impurities, to incomplete reactions, to losses during separation and purification — to name just a few. These production losses are costly, hurting manufacturers’ bottom line and sucking up previous time and resources from their manufacturing teams.

What if you could prevent these losses from occurring in the first place?

That’s what one global manufacturer of Ethylene Dichloride managed to do — saving nearly one million euros each year on a production line that, until then, had been suffering significant annual losses.

The primary problem was a toxic side product (trichloroethane) that kept forming during the production process. Despite the best efforts of the company’s process experts, the cause of this process inefficiency remained frustratingly elusive for years.

The limitations of generic AI and analytics tools

Part of the problem was that the production process in question was relatively complex, with some 4,400 data tags. The analytics tools used by their process experts, advanced as they were, still limited them to conducting ad hoc analyses of a select number of tags. This was of little help, since once their existing theories were exhausted they had no idea where to continue looking for the root cause of the problem!

Due to the significant financial impact of this inefficiency, the company decided to invest in Industrial Artificial Intelligence.

But attempts to use generic Artificial Intelligence solutions repeatedly failed to yield useful or accurate results, due to the complexity of their production process. These solutions weren’t made specifically for continuous manufacturing, and were therefore unable to cope with the unique complexities of the process and resulting data.

As their VP Manufacturing noted:

“These vendors touted some powerful AI technology. But continuous chemical manufacturing processes like ours produce uniquely complex and messy data that their algorithms simply didn’t understand. So the end results either made no sense or were clearly inaccurate.”

The solution: Process-Based Artificial Intelligence with embedded process expertise

Things finally turned around when they turned to Seebo.

Seebo’s proprietary Process-Based Artificial Intelligence is designed specifically to solve complex continuous manufacturing process inefficiencies. First, Seebo’ automated root-cause analysis reveals the hidden causes of their losses. Next, the Seebo solution provides predictive recommendations to reach their optimal process settings to avoid losses and maximize capacity. Finally, proactive alerts enable their manufacturing teams to prevent losses before they occur.

Using the Seebo solution, the company’s process experts were finally about pinpoint the primary causes of their yield and quality issues, as were armed with the capabilities to maintain optimum process settings and prevent similar problems in the future.

The results were impressive: a 65% reduction in the amounts of toxic side products – which translated into annual savings of one million euros for that single production line.

Download the full case study to learn how they did it.