Two real-life examples of batch analysis in Factry Historian using Grafana dashboards

Jolan De Cock on , updated

Is it the Golden Batch you’re looking for? See how two of our clients use Grafana dashboards in Factry Historian to analyse their production batches, and reproduce perfect results on every run.

real-life examples of batch analysis in Factry Historian

In our blog series dedicated to dashboards, we’re exploring some of the most insightful data visualisations you can build with Factry Historian. In this post, we bring into focus how you can use the historian to detect and analyse batches, leveraging the historian’s event module and Grafana dashboards.

In search of the holy grail of batch manufacturing

A consistent and repeatable production of quality products is the top priority for any global batch manufacturer. To achieve this goal, batch analysis dashboards play a crucial role by offering real-time visibility into the critical process variables that can affect the quality of the end products.

How to do batch analysis 10 times faster and easier

Struggling to analyse production batch data in a user-friendly way? The historian’s event detection and analysis module allows you to add valuable context to raw process data, by automatically creating records in a relational database that meet critical events, such as batches, products, or downtime.

How it works:

  • Ability to create an asset model of your plant’s equipment.
  • Detect any event that has a start and an end time
  • Compare batch data in real time on the shop floor
  • Automatically aggregate the data in a SQL database
  • Use the data hassle-free in the BI tool of your choice

Imagine the world of possibility:

  • Automatically collect data from each batch, order or product
  • Compare real-time data with the perfect batch parameters
  • Optimise energy and utilities consumption within processes
  • Reproduce the golden production batch over and over again

Production batch analysis in Factry Historian: 2 real-life examples

1. Batch analysis of fermentation processes at Lesaffre

In 2022, the global fermentation expert Lesaffre Group started leveraging Factry Historian on the shop floor, to collect and analyse real time data from its fermentation processes.

Since then, they use the built-in Grafana interface to create custom dashboards, follow-up on production parameters in real time, compare them with historical metrics, and drive streamlined product quality.

Previously, data transformation from a time-series to SQL-format was done through custom Excel or Microsoft SQL integrations, which was very complex and hard to maintain in a cost-effective way.

Through Factry Historian, process and batch data is now automatically translated into an SQL database, ready for business analysis in any BI tool.

Let’s take a look at how Lesaffre made the dashboarding tool their own:

real-time production parameters

✔ The discrete chart (the horizontal bar chart) offers an overview of each batch detection on a certain fermenter, including the different production steps within that batch, and the batch ID (e.g. FER1-…).

✔ Just below the discrete chart, end users can find an overview of key statistics regarding the selected batch in the dropdown in the upper left.

✔ The graph in the middle displays data on process parameters, specifically the antifoam volume or the ethanol concentration for different fermentors, extracted from multiple batches and layered on top of each other.

In the graph, each segment of the green curve represents the antifoam volume for one batch, the same is true for the red curve and the ethanol concentration. As there are multiple fermenters operational at the same time, segments of the same curve can overlap in time.

Noticeable is that the trend of the segments remains consistent across different batches over different fermenters. This is to be expected, as the fermentation process over time and/or over different fermenters should be highly reproducible to achieve the highest quality.

✔ The bottom graph presents a comparison of the ethanol concentration over multiple batches in an overlay kind of fashion. Therefore, multiple batches that are naturally spread over time are automatically aligned in time to accurately coincide. The legend of the graph indicates the respective batch IDs displayed in the graph.

Find out more about our solutions for the fermentation industry.

2. Production batch comparison at a specialty chemical company

One of our clients is a global producer of industrial coating resins and additives for architectural, industrial, and protective automotive coatings and inks.

To compare production batches and streamline product quality, they use the historian’s built-in event detection & analysis module.

Here’s how they’ve made the Grafana dashboards their own:

comparing production batches

✔ The end user has the ability to choose the reactor (asset) from the dropdown list displayed at the top. Subsequently, they can select one of the product names that have been identified in one of the batches produced on that reactor. It is important to note that each batch may correspond to a different product or be labelled with a distinct product name.

✔ The visualisation above the table, underneath the dropdown lists, shows the key values of the currently selected batch on the left side. On the right side, some process parameters like the reactor temperature, the reactor pressure and the condenser BTU/min are highlighted by taking the average over the last x batches on the same reactor and product name. The ability to aggregate values over multiple comparable batches opens up opportunities to operators and process engineers.

✔ The table provides a list of detected batches of a particular reactor, serving as a point of reference. Alongside the start and stop times, the duration of the batch is highlighted. Next, the product name is tracked and divided into two parts, as for the end user, the independent parts have more meaning on their own then when combined.

✔ Below are graphs that display an overlay of a specific process parameter, such as the reactor temperature or the reactor pressure, across various batches. The accompanying legend allows for the identification of each batch based on its start time. To monitor this particular process, using the start time as the identifying factor for a batch pictures itself in a highly familiar way to the end user.

As a result, these 2 clients were able to:

  • Improve consistency and repeatability of production quality
  • Find the Golden Batch by comparing real time batch parameters with the optimal conditions
  • Comply with stringent client demands and regulatory standards
  • Implement an improvement programme, and track results
  • Make more informed decisions about suppliers and raw materials

Find out more about our IIoT platform Factry Historian and its event detection and analysis module. Or read more details about our solutions for the fermentation industry.

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