In my previous blog, I discussed why it is critical for continuous flow manufacturers to create end-to-end data models, or digital twins, of their entire production process. In this blog, I want to give you insight into how these digital twins help manufacturers optimize production.

At Sight Machine, we have been working for years with continuous manufacturers struggling to use data to lower costs and address quality issues. Most have yet to develop scalable methods of analyzing the massive amounts of sensor data generated during production.

So how can an end-to-end digital twin transform the way continuous manufacturers use data to drive decision making? Here are the 4 biggest impact areas:

  1. Improving overall production efficiency vs. optimizing a single process or piece of equipment

Optimize Continuous Flow Processes

Traditionally, process manufacturers have focused on efficiency improvement efforts on single machines or processes within a production line. But this often leads to challenges:

  • If that process/machine is not a bottleneck in the overall process, then the manufacturer isn’t improving overall production efficiency.
  • Optimizing one piece of equipment or process will have upstream or downstream effects that may negatively impact the overall system.

I’ve seen this problem first hand: After analysis of a particular piece of equipment, manufacturers often find that they can relax tolerances to improve output but still maintain overall quality at that given machine/tank/process. However, this increased variability inevitably impacts downstream processes, causing more overall defects.

Tracing data across the whole process allows manufacturers to take the upstream and downstream dependencies into consideration and determine the most effective improvements. Read more about Sight Machine’s customer case studies here

Lesson learned: Optimizing one machine at a time can actually make things worse, resulting in more defects overall.

  1. Evaluating the trade-offs involved with changes in production settings

I’ve found that continuous manufacturers often struggle to balance the tradeoffs between different production KPIs.

Here’s an example from Sight Machine’s work with a large paper manufacturer. One of their paper machines runs without cross-direction (CD) moisture controls. Typically, when moisture streaks are measured in the web, the machine’s dryer system will ramp up the individual dryer temperatures to bring the peak moisture back to within quality limits. This single-process enables the production facility to address one KPI: quality.

However, increasing the temperature also negatively impacts a different KPI: it makes energy costs higher. To get a true understanding of optimum machine adjustments and trade-offs, the manufacturer needs to consider machine wet-end data at the same time as data for overall energy use. Reducing additional energy costs could also possibly justify the addition of CD moisture controls. In effect, the manufacturer was looking at only half of the problem. By creating and analyzing integrated end-to-end data, they can optimize the entire process.  

Lesson learned: Creating an end-to-end process model allows manufacturers to evaluate the tradeoffs of proposed optimizations, answering the question, Is it really worth it?

  1.  Combing end-to-end process data with batch data to optimize downstream quality and output

One of our Paper & Packaging customers was interested in reducing web breaks in their paper making process. Web break causes are typically determined using data from break sensors located at different points in the process, and by reviewing visual information captured by a web monitoring system. In this case, the customer had noticed that breaks were occurring at regular time intervals that were not shift-related. The only batch process was in the pulping area where a batch digester was in use. Variables associated with pulping therefore seemed of interest as a possible cause for the web breaks.

Sight Machine was able to create a digital twin of production, integrating equipment sensor inputs with quality data from both batch pulping and the continuous papermaking process. This end-to-end model allowed upstream digester data analysis to be performed for specific batches. The chemistry-based modeling revealed that, among other things, hydrophobic particles in the process water had a correlation to web breaks.

Lesson learned: Integrating process and batch data allow you to optimize the production settings for an upstream process to improve downstream efficiency.

Lesson learned:  Integrating process and batch data allow you to optimize the production settings for an upstream process to improve downstream efficiency.

  1. Using integrated process and batch data to identify upstream causes

With an integrated end-to-end view of all process and batch data, continuous manufacturers can identify upstream root causes of downstream issues.

Here’s an example of how this worked. Like many continuous manufactures, one of our customers was challenged with identifying the root cause of production alarm signals.

The process engineering team was continuously working to address issues on equipment that was generating alarms. But unfortunately, much of this work had limited impact, as the root cause of the alarm was actually associated with upstream production processes. The team was addressing a symptom, not a cause.

By combining an end-to-end model of production with quality data, the manufacturer was able to:

  • Weed out non-relevant alarms to focus their analysis
  • Look for clusters of co-occurring alarms
  • Develop an understanding of cascades of alarms (alarm interdependencies and how one alarm may lead to a sequence of other alarms)

Ultimately this enabled us to determine the root alarm and the corresponding activity that generated that alarm, resolving the issue once and for all.

Lesson learned: An integrated end-to-end view of process and batch data enables you to identify upstream root causes.

The power of a digital twin can transform the way continuous manufacturers operate. It enables process engineers and plant managers to look at old problems in entirely new ways. Check out our use case page to read about some of our recent work. I look forward to hearing from you about the challenges you’re facing in optimizing your environment.

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