Bersey.ai is our vision for bringing accumulated boiler and plant engineering knowledge into digital ways of working. We are developing two connected goals: faster, more consistent engineering within Bersey, and digital twin solutions developed together with our customers from our automation experience.
When drawings, calculations, equipment information and operating knowledge share the same engineering context, alternatives and the reasons behind decisions become clearer. We are developing Bersey.ai to build these connections. The model scope, available data and validation method are agreed together for each project.
Digital twin approachOur first goal is to make engineering knowledge from design and manufacturing reusable. With parametric models, we aim to carry changes consistently through calculations, geometry and documentation.
We aim to build on our PLC and automation experience with an approach that evaluates plant behaviour alongside an engineering model. The goal is to provide understandable evidence for maintenance, process and energy decisions, using existing operating knowledge.
Explore the technical framework of our digital twin approach in Digital Twin & AI Platform (EN).
Take the first step together: define the need, the pilot scope and the criteria for success.
Four examples from our modelling and visualisation work.
A look at modelling work in Autodesk Inventor.
Explore plant layout and equipment relationships in a digital environment.
An example of bringing digital design into visualisation and presentation.
A study that brings a digital model and site footage into one story.
“How can I assess my existing energy plant as production increases?” The story starts here: understand the current plant, examine options on a consistent basis and record the reasons behind the decision.
These two goals meet in the four engineering steps below. First understand the plant, then bring scenarios, equipment relationships and decision knowledge into the same framework.
Plant modelBoilers, fuel handling, fans, pumps, piping and process consumers are connected. Bringing drawings, equipment lists and available operating data together makes those relationships visible.
Example questionWhere would new equipment fit, and which lines would it connect to?
Study outputPlant model, equipment list and data requirements.
Operating scenariosChanges in capacity, fuel properties and operating conditions call for a fresh assessment of heat and mass balances. Comparing alternatives against the same design basis helps identify equipment and process connections that need closer review.
Example questionHow would fuel moisture or steam demand affect the system?
Study outputScenario comparison with stated assumptions and limits.
Equipment interactionsSelecting a fan, pump or heat exchanger can affect the whole system. A digital model provides a framework for examining flow, pressure and temperature relationships alongside engineering calculations. Where suitable measurements exist, calculated and observed conditions can be assessed together.
Example questionWhere is the bottleneck, and what should be measured before deciding?
Study outputEquipment assessment and a measurement/review plan.
Engineering knowledgeTechnical documents, revisions and calculation results are organised in their project context. AI-assisted work can help locate relevant information, structure questions and prepare technical summaries. Each assessment is considered alongside its source data and engineering review.
Example questionWhich data and assumptions supported this choice?
Study outputTechnical summary, decision rationale and engineering documentation.
Explore our approach to predictive maintenance, energy management and process optimisation in Industrial Intelligence Platform (TR).
Identify the decision, existing equipment and available data together.
Agree on design assumptions, data connections and scenarios to compare.
Review results within a limited scope against agreed acceptance criteria.
Record findings and limits, then decide together whether to expand.
Start with your priority engineering question. Together, we can assess the current situation and available data, then define a project-specific scope for development and validation.