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PIDGraph Module engineering

Use Case: Deep Graph Analysis to optimize engineering


Industry:Oil & Gas


To find a time- and cost-effective way to digitalize engineering documentation for creating e.g. asset hierarchies, and then extract valuable information from the data obtained.


Deep graph analysis can find areas of similar functionality, based on relationships within a P&ID.


  • Identify key equipment items and their most common neighborhood
  • Create an asset hierarchy including component groups, also known as modules, templates, or functional locations
  • Create complex P&ID queries, e.g. to find commonalities or deviations in design


The challenge for plant operators is to find a cost-effective solution for digitalization of engineering documentation and extract additional value from the process – they need a partner to do this.

One approach is set up an asset hierarchy. However, achieving this depends crucially on defining groups of equipment items and their relationships, which is very time consuming. Hence, asset hierarchies are often defined only once, and then poorly maintained over the asset lifecycle.

Nevertheless, if at least the P&IDs are kept as-built, their conversion into intelligent P&IDs using PIDGraph can facilitate automatic detection of groups of equipment items - also known as functional locations, templates or modules - using “Deep Graph Analysis”.

These groups can be used to optimize engineering processes and improve maintenance strategies. The maintenance of the groups is simplified by an easy to read XML/JSON format.

This data structure provides a platform for developing further applications. It acts as a single repository for asset-centric models, hierarchies, objects, and equipment. These files can also be stored in a graph database, which can then be used by data scientists.



The artificial intelligence (AI) applied by PIDGraph significantly enhances P&ID conversion, reducing costs by 50 per cent compared with previous solutions. After migration, deep graph analysis performed by BDN helps to optimize engineering, maintenance, and operations.

In engineering, for example, it can identify patterns in the graph file, and therefore define modules – and facilitate a module engineering approach. Module engineering helps to check component order along a flow stream; it also allows semi-automated identification of key equipment items and their most common neighborhood.

Module-based engineering has become a major topic in engineering applications, as it can help to expedite new engineering projects. For example, it can be used to automate completeness checks on P&IDs received from third parties, and perform automated risk analyses.

For maintenance, it can be used to define ‘Functional Locations’ in a SAP maintenance environment as a group of maintainable equipment items. This helps to better evaluate maintenance costs, compare downtime of similar functional locations, and therefore derive optimized maintenance strategies.

Operations staff can use it to define ‘templates‘ representing multiple related assets, such as gas compressors.


When BDN processes large numbers of P&IDs, or compares them with P&IDs from other plants, much data becomes available. This can be used to identify clusters or patterns which bear certain similarities. Each pattern contains the same community of objects, from which an asset hierarchy can be extracted.


An air cooler will show certain cluster similarities in a particular P&ID. These similarities will be repeated across multiple P&IDs. This can be used to compare the function “cooling” within a P&ID or with other P&IDs. If several thousand P&IDs have been analyzed like this, the knowledge derived can be used to locate a cluster that shows deviations from the norm, indicating possible problems.

If so, the engineering documentation that the P&ID is based upon might include errors that should be checked; this can be done using complex P&ID queries, for example to find commonalities or deviations in design.

Module engineering can be used to provide guidance while setting up greenfield projects. It can use the PIDGraph’s digitized data to boost the entire process.

Industrial Compressor Refrigeration Station

Key Customer Benefit

  • Engineering for optimization: Use P&ID data to check functions and identify problems in operations and maintenance applications
  • Identify key equipment items and their most common neighborhood
  • Profit from data provided by P&IDs. This data can also be made available to other users, such as data scientists, who can then run their own analyses
  • Save time and costs
  • Create guidance to help design greenfield projects