Process Control Through Real-Time Monitoring
Quality Control for Continuous or Batch Process Manufacturing
Using advanced data analytics models in real time opens up a whole new world of possibilities for improving your production processes. Not only does real-time process monitoring provide a level of confidence in your process performance, it can also help improve the overall quality of your production line output.
But to make it happen, you need to monitor and analyze your production data simultaneously using advanced multivariate data analysis (MVDA) models. Your models must be statistically accurate enough to determine when a production process is deviating from any normal operating condition, and perhaps even predict when a current process might start deviating from accepted conditions.
That’s why a multivariate data analysis (MVDA) tool for real-time process monitoring is essential.
A real-time process monitoring system based on multivariate data analysis can help your production floor staff to know whether a process is performing optimally and provide alerts if a process starts to (or is likely to start) deviating from acceptable production.
Why Real-Time Process Monitoring?
Live monitoring turns all the process data you are collecting into actionable insights and foresights. It helps:
Minimize operational costs
Increase confidence in your process performance
Ensure more consistent product quality
Maximize efficiency throughout operations
How Real-Time Production Monitoring Works
Real-time production monitoring utilizes to summarize all of the individual parameters from various operations into multivariate models so they can be monitored. This becomes very efficient in the control room because instead of looking at a large number of individual parameters or signals, you have a small set of summary parameters that let you monitor all the variables at the same time.
More easily monitor multiple variables from a few control charts
See process changes as they happen
Pull data from various data sources together
Monitor processes remotely from different locations
Set alerts for key process parameters
Respond to process deviations quickly in real-time
SIMCA®-online Monitoring Software
SIMCA®-online provides dashboards that let operators quickly see when processes are operating as they should (green), start to deviate (yellow) or when deviations occur (red). SIMCA®-online gives you:
multivariate predictive monitoring
fault detection and deviation alerts
root cause analysis (whenever a fault is detected)
automatic corrective recommendation
SIMCA®-online gives production managers a live view of multivariate data to easily see how each batch in a process is doing.
What You Can Do With SIMCA®-online
Monitor in Real-Time
You can create an ideal model of your process and then compare your actual data for the process to the model in real time. This works for both batch processes and continuous processes.
Predict with Confidence
The multivariate analysis model provides a basis for predicting quality parameters over time using advanced data analysis. With this tool, you can predict the final critical quality attributes with a high degree of confidence.
Control at a Glance
SIMCA®-online improves your overall understanding of process and equipment because you are getting updates about what is happening in the process right now. Plus, with real-time drill downs you can pinpoint issues and detect problems with equipment as they happen.
Ensure Electronic Records Compliance
SIMCA®-online supports compliance with 21 CFR Part 11, the FDA regulation for electronic records and signatures. SIMCA®-online provides a detailed audit trail that logs all transaction events, protects against tampering, and maintains electronic signatures with authentication.
Statistical Process Control
Statistical Process Control (SPC) is a data analytics method that is particularly useful for quality control of batches. This technique involves creating a control chart that shows the upper and lower warning and action limits based on a target value for your parameter. Using the control chart, you can look for deviations from a normal process behavior.
Model Predictive Control
Model Predictive Control, like Statistical Process Control, uses predictions to control and optimize a process. The goal is to find the best future settings of your process variables based on past results. For example, Model Predictive Control can be used to adjust the future values of the pH and temperature to optimize the future of a biological process.
Active Dashboard displays real-time data from SIMCA-online or OSIsoft PI servers to give you an overview of process performance across all your sites and productions lines. With web-based connectivity, Active Dashboard can be deployed across your organization.
Active Dashboard will also help you to see when your production starts to deviate, so that you can take action and save both time and money and most importantly improve quality.
An outstanding tool for Model Predictive Control is a tool inside SIMCA-online called Control Advisor.
Want to Know More?
To find out more about Control Advisor in SIMCA®-online, and how it can help optimize your processes and business results, book a free demonstration.
Applications Areas for Real-Time Monitoring
Paper & Pulp Industry
A multinational paper company reduced costs, achieved a more consistent product quality, gained a deeper understanding of their data.
Food & Beverage Industry
Data analytics and real-time monitoring are useful to prevent food fraud, manage quality, optimize recipes, predict shelf life and more.
A pharmaceutical company paid for their investment several-fold in recovered batches alone.
Want to Know More?
Find out more about how SIMCA®-online and how it allows you to monitor your process manufacturing in real-time.
Watch the recorded webinar.
Real-Time Monitoring Supports Digital Transformation
Embracing a total company-wide digital transformation enabled Amgen to align data across multiple systems to not only control, but also predict unacceptable deviations in time to make necessary adjustments. Read on to find out how they used data analytics to implement real-time process control.
Making the Shift to Continuous Process Manufacturing
Throughout the evolution of manufacturing, many industries have gradually shifted away from batch process to continuous process manufacturing as production technologies matured.