Cleanse

Process Plant Engineering Digitalization

Cleanse for systems integration and futureproofing

Depending upon the level of detail and enforcement of information handover requirement specifications, and the maturity of management of change processes implemented within an organization, process facility engineering data can often be incomplete or inaccurate. Changing strategies, contractors, technologies and conventions over time can also lead to inconsistencies within key data such as tagging systems, breakdown structures and data dictionary setup and application.

A very common example of incomplete data arises when a brownfield project, under strict instructions to minimize costs, limits validation of existing data to elements which will be tied into, replaced or modified by the new project, leaving other data untouched. This can result in the situation where a good quality, information rich digital asset representing the updated part of the facility is available, but the existing plant is only represented in the form of legacy project documentation.

Another common scenario occurs when a digital asset is produced and handed over by a capital project, but the owner/operator does not possess the knowledge or resources to maintain it, and multiple years of changes may only be documented in the form of marked-up drawings.

These gaps and inconsistencies require constant, costly workarounds when attempting to leverage the digital asset, sometimes requiring parallel or alternative work processes to deal with the absence of reliable input data. Instead of being able to focus on more automated and efficient processes, organizations must then fall back on manual methods which almost always require repetitive data assessment and verification activities.

Solving this problem requires evaluating the existing data at a higher level, identifying what gaps and inconsistencies exist, and ensuring that appropriate standards exist to document the expected result. Once these tasks have been completed, the work of data entry, back-modelling, back-drafting and applying standards and rules to existing data sets can be commenced, finally resulting in a complete and internally consistent digital asset which can be relied on.

TecSurge offers the skills and resources needed to cleanse existing data, using inputs such as laser scans, existing drawings, and engineering change documentation to produce a complete and consistent digital asset. Our focus on maintaining digital assets delivers rapid and cost-effective results, interfacing seamlessly with operational, maintenance and capital project data consumers.

The following link is a selected example of our work.