Improving the manufacturing process

We make good products better

using AI and Machine Learning

Packaging & Tissue

Water

Energy

Plastics

Metals, Mining & Materials

AI for Water

Bringing closed-loop technology to the Waste Water treatment Industry.

  • In the next decade, many WWT operations expect manpower shortages.
  • Automated control of operating facilities is becoming more important every day.
  • Our predictive and prescriptive solution enables a closed-loop system, similar to a self-driving car.

Using sophisticated tank monitors we are able to collect real-time data quality metrics to determine

  • Current contaminant conditions
  • BOD levels
  • Oxygen levels
Problems with the Industry

  • Aging infrastructure
  • Operator shortage due to retiring workforce
  • Climate change
  • Achieving EPA effluent standards
  • Compliance controls
  • Funding concerns

Packaging & Tissue

AI For Paper and Pulp Mills

Paper mills that implement ProcessMiner Cloud Platform realize clear benefits:

  • Enhanced quality measurements with less error and variability
  • Decreased operating costs by optimizing basis weight, chemistry usage and feedstock mixing
  • Minimized process variability through step-wise process changes
  • Improved quality consistency resulting in less downgrading
  • Increased line speed without compromising quality
  • Predictive “Soft Sensors” — ProcessMiner provides a real-time, mathematically driven measurement of paper quality metrics that cannot be delivered using any other method. Real-time predictions provide high-frequency data every 15 to 30 seconds. For the first time, papermakers can have a real-time quality profile of machine-direction properties for the entire length of the reel.
  • Machine Learning — Due to the ever-changing environment of the papermaking process, it is crucial to continually update predictive analytics platforms. Using the machine learning capabilities built into the platform, OPTIX measurements remain robust and accurate in the face of changing machine conditions. Adaptive predictive models allow for more informed, on-the-fly process changes, rapid change detection, and process control optimization without requiring periodic model tuning.
  • Prescriptive Recommendation Engine — Using deep learning and advanced data analytics methods, ProcessMiner prescribes real-time actionable insights for optimizing the manufacturing process. Operators can apply data-driven recommendations to maintain manufacturing conditions or target optimized machine efficiency.
  • Model Diagnostics — Each ProcessMiner model comes equipped with a self-diagnostic prediction accuracy monitor. The platform evaluates the model accuracy using a metric related to the difference between the prediction and actual value of the quality measurement.
  • Reduced Error
  • Reduced Variability
  • Improved Quality Consistency
  • Adverse Event Detection
  • Increased Production Volume
  • Learning and Training

AI for Energy

AI for Plastics

AI For Plastics

Plastic business owners must be prepared and learn which groups of workers or processes AI will replace, so that they can enhance their competitive capacity both in terms of quality and production capacity as well as reduce waste and defects. In addition, they should also offer training to equip their employees with data processing skills.
While computers will handle data management, it is humans who will be teaching computers how to pick out useful data. Furthermore, as robots and automobiles in the future might feature more plastic parts and fewer steel parts, we might see a rise in the demand for durable, high-quality, lightweight plastics. It is very important for entrepreneurs to prepare their production to meet this demand.

Metals, Mining

AI For Metals, Mining and Raw Materials

We can predict defects, rejects and reduce your scrape to 0%. Get higher production rates and better traceability. The ProcessMiner AI solution identifies an optimum operating archetype to reduce variation. We’ll recommend proactive actions to achieve the optimum yielding results so you can get more predictable plant performance.