Don Computing develops practical digital-twin solutions for Australian industrial and technology organisations. We combine engineering models, operational data and automation logic to help teams understand assets, test operating scenarios and improve performance.
Applications
- Equipment and process performance monitoring
- Virtual commissioning and control-strategy testing
- Production planning and bottleneck analysis
- Energy-use and emissions optimisation
- Predictive maintenance and anomaly detection concepts
- Operator training and decision-support interfaces
We support manufacturing, energy, hydrogen, logistics, food processing, resources and advanced engineering applications. Work can begin with a focused proof of concept before expanding to a larger operational platform.
Process
- Review the operational goal, asset boundary, users and data availability.
- Define a model and data architecture appropriate to the required accuracy and update frequency.
- Develop a focused prototype and compare its predictions with agreed operating or test data.
- Review outputs with users and document the scope for deployment, monitoring and maintenance.
Model-led industrial improvement
A useful digital twin must reflect the business decision it is intended to support. We begin with the operational objective, available data and required accuracy. The solution may combine CFD, process simulation, reduced-order models, sensor data, dashboards and machine-learning methods.
Integration with automation
Digital twins can be connected with robotics, PLC data, manufacturing systems and remote-operation platforms. Don Computing helps define data structures, model update strategies, validation plans and human-readable outputs so the twin remains useful throughout its lifecycle.
Required inputs
Begin with one asset or process and a specific decision: comparing operating scenarios, detecting a departure from expected behaviour or planning an improvement. Identify available drawings, process models, sensor tags, sampling intervals, historical records and known data gaps. A static simulation and a continuously updated digital twin have different integration and maintenance requirements; these are clarified at the start.
Deliverables
Agreed deliverables may include a model specification, data requirements register, prototype dashboard, scenario comparison, validation report and handover documentation. Access to operational systems, deployment responsibilities and ongoing support are scoped separately.
FAQs
Do we need live sensor data to start?
Not always. Existing drawings and historical data may support a feasibility study or prototype. Live connections are assessed when continuous updating is required.
How is the model validated?
We agree reference data, operating ranges, error measures and acceptance criteria. The report identifies where the model has been checked and where uncertainty remains.
What information is needed for a quotation?
Provide the asset or process description, intended users, decisions to support, available data sources, preferred outputs and desired project stages.
