Monthly Archives

September 2017

OSIsoft Intern Creates System to Predict Energy Pricing

By | Industry Presentations

OSIsoft customer support intern, Miwa Teranishi, leverages the power of OSIsoft PI System and Falkonry LRS ready-to-use machine learning system to create an energy price prediction system to understand electricity pricing in Kyushu, Japan. With her system, energy consumers could identify the best time to consume or avoid electricity use during a day. Let’s take a look at her project in this video:

As Miwa highlighted, Falkonry LRS “makes creating models quite simple with no programming and no knowledge of data science algorithms required.” Further, the integration between OSIsoft and Falkonry allows customers to quickly unlock value from existing data assets and deliver insights from their PI System.

Thanks Miwa for a very interesting use of PI System and Falkonry LRS. This is a creative example of how automated machine learning could be applied to everyday challenges, like the daily, fluctuating price of electricity.

In addition to PI System, Falkonry LRS machine learning is easily integrated into many popular solution platforms such as Splunk and Microsoft Azure IoT, accelerating the time to value for customers.

If you’re interested in learning more, check out our Product page to watch a short demo of Falkonry LRS ready-to-use machine learning system.

Ciner creates early-warning system for mining operations with Falkonry LRS

By | Industry Presentations

At the OSIsoft User Conference in San Francisco, joint customer, Ciner Resources (pronounced “jin-ner”) spoke about the digital transformation they were experiencing by employing OSIsoft’s PI system along with Falkonry’s condition recognition software.

The three Ciner speakers included their CIO, SMART plant lead and their process engineer.  They spoke about the challenges, triumphs and future goals of digitally transforming their business.  Ciner’s mine and processing plant in Wyoming has a ball mill, called a vertimill, that was experiencing unexpected downtime due to fluctuations in ore grade. Each time the vertimill clogged due to “unknown” low quality ore grade, it cost the company $30,000/hour of production potentially leading to a loss of $720,000 per day.

Ciner’s engineers and practitioners worked with OSIsoft and Falkonry to look into historical data to devise a solution. Together they employed the PI System and Falkonry’s machine learning software and within a few weeks, were able to recognize the patterns that led to blockage conditions. From there, an early-warning system was created to detect low-quality ore grade upstream of the vertimill in order to mitigate downtime events. Where the raw material took two hours from intake to reach the vertimill, Falkonry discovered 10 hour early warnings which surprised the process engineers.

In the video below, Wyatt Keller, Process Engineer at Ciner, shares his experience using OSIsoft and Falkonry LRS ready-to-use machine learning system.

If you’re interested in learning more about our success with Ciner, check out our customer video page to watch additional videos.