Introduction
Laboratory equipment is becoming increasingly connected.
Modern instruments can contain sensors, electronic controllers, embedded software, communication interfaces and data systems.
This creates opportunities to understand equipment not only through physical inspection but also through digital information.
One technology attracting increasing attention is the digital twin.
A digital twin is a digital representation of a physical system that can be connected to information about that system and used for purposes such as monitoring, simulation, analysis, optimization or decision support.
In laboratory environments, digital twins can potentially support:
- Equipment monitoring
- Simulation
- Maintenance
- Fault detection
- Robotics
- Workflow planning
- Training
- Optimization
- Autonomous experimentation
Recent research has specifically explored digital twins in self-driving chemistry laboratories, including their potential to test hypothetical automated workflows digitally before relying on costly physical experiments.
Digital twins therefore connect closely with the broader development of Laboratory 4.0, smart laboratories and self-driving laboratories.
1. What Is a Digital Twin?
A digital twin is a digital representation of a physical object, system or process that uses relevant information about the physical counterpart.
The concept can be applied to:
- Machines
- Buildings
- Industrial processes
- Vehicles
- Manufacturing systems
- Laboratory equipment
- Experimental workflows
A digital twin may use information from sensors, equipment records, operating conditions and other data sources.
It is important to distinguish a digital twin from a simple static digital model.
A digital twin is generally intended to remain connected to information about the physical system and support some form of monitoring, analysis, simulation or decision-making.
2. What Is a Laboratory Digital Twin?
A laboratory digital twin is a digital representation of a laboratory asset, process, workflow or environment.
For example, a laboratory could develop a digital representation of:
- An analytical instrument
- An automated system
- A laboratory process
- A robotic workstation
- An environmental monitoring system
- An entire laboratory workflow
The level of complexity can vary considerably.
A simple implementation may focus on equipment condition.
A more advanced implementation may represent equipment, workflow, data and interactions between multiple systems.
3. Digital Twin vs Simulation
Digital twins and simulations are related but not identical.
A simulation can model how a system behaves under particular conditions.
A digital twin can incorporate information about an actual physical system and may be updated using operational data.
For example:
Simulation:
“What might happen if the equipment operates at this temperature?”
Digital twin:
“What is happening to this particular equipment, and what might happen if its current operating conditions continue?”
The distinction depends on implementation, but connectivity to the physical system is an important part of the digital-twin concept.
4. How Does a Laboratory Digital Twin Work?
A simplified digital-twin architecture may involve:
Physical Laboratory → Sensors/Data → Digital Model → Analysis → Decision → Physical Laboratory
For example:
- Laboratory equipment generates operating data.
- Sensors or systems collect the information.
- Data are transferred to the digital environment.
- The digital representation is updated.
- Software analyses the information.
- The system provides information or recommendations.
- Appropriate action can be taken on the physical equipment.
The digital twin therefore creates a bridge between the physical laboratory and its digital representation.
5. Digital Twins and Laboratory Equipment
Laboratory equipment is a natural application area for digital twins.
A digital twin could potentially represent:
- Equipment condition
- Operating hours
- Temperature
- Vibration
- Error information
- Maintenance history
- Calibration information
- Performance data
For example, the digital representation of a centrifuge could contain information about:
- Operating cycles
- Rotor information
- Maintenance history
- Error events
- Operating conditions
This could help laboratory personnel understand equipment history and current status.
6. Digital Twins and Equipment Monitoring
Equipment monitoring involves observing the condition or performance of equipment.
A digital twin can potentially combine information from several sources.
For example:
Sensor data + Equipment history + Maintenance records + Operating conditions
This can provide a more complete view of equipment condition than a single observation.
Monitoring can help identify unusual behaviour that may require further investigation.
7. Digital Twins and Preventive Maintenance
Traditional preventive maintenance is often based on defined schedules.
For example:
Inspect equipment every six months.
A digital approach can add information about actual equipment usage and condition.
The laboratory could potentially combine:
- Operating hours
- Usage patterns
- Error events
- Sensor readings
- Maintenance history
- Manufacturer recommendations
This information can support maintenance planning.
However, a digital twin does not automatically determine the correct maintenance interval.
Laboratory procedures, manufacturer requirements, equipment history, risk and applicable requirements remain important.
8. Digital Twins and Predictive Maintenance
Predictive maintenance attempts to identify indications that equipment may require attention before failure occurs.
A digital twin may support predictive maintenance by providing a structured representation of equipment condition and historical information.
For example:
Normal operation → Condition monitoring → Unusual pattern → Investigation → Maintenance decision
Machine learning may also be applied to appropriate equipment data.
However, predictive models must be evaluated carefully.
A prediction is not the same thing as a confirmed equipment fault.
9. Digital Twins and Laboratory Troubleshooting
Digital twins may also support troubleshooting.
Suppose an instrument begins producing unusual results.
A digital representation could provide access to information such as:
- Recent operating conditions
- Error history
- Maintenance history
- Previous performance
- Sensor information
- Equipment configuration
This information could help a qualified professional investigate the problem.
The digital twin should therefore be considered a decision-support tool, not an automatic replacement for technical diagnosis.
10. Digital Twins and Calibration
Calibration remains important for laboratory measurement systems.
A digital twin may store or display information related to:
- Calibration status
- Calibration history
- Verification results
- Equipment configuration
- Measurement performance
However, a digital twin does not itself constitute calibration.
The physical instrument still needs appropriate calibration or verification according to applicable procedures.
This distinction is important.
Digital representation ≠ physical calibration.
11. Digital Twins and Laboratory Automation
Digital twins can work alongside laboratory automation.
An automated laboratory may contain:
- Robots
- Automated instruments
- Sensors
- Conveyors
- Controllers
- Software
A digital representation of these systems can potentially support:
- Workflow simulation
- Monitoring
- Optimization
- Fault detection
- System testing
- Planning
This can be especially useful where automated workflows are complex.
12. Digital Twins and Robotics
Robotic laboratory systems must operate accurately within their physical environment.
Digital models can potentially help engineers and laboratory professionals study:
- Robot movement
- Workstation layout
- Equipment interaction
- Collision risks
- Workflow timing
- Task sequences
Digital-twin approaches are also being discussed in research on autonomous laboratory robotics.
This can allow some problems to be identified in a digital environment before changes are made to physical equipment.
13. Digital Twins and Self-Driving Laboratories
Digital twins have an especially interesting relationship with self-driving laboratories.
A self-driving laboratory combines:
AI + Robotics + Automated Experimentation + Data
A digital twin can add:
Digital Representation + Simulation + Physical-System Feedback
This creates a potentially powerful architecture:
Physical Laboratory ↔ Digital Twin ↔ AI/Decision System ↔ Robotics
Research published in 2026 has explored digital twins specifically in self-driving chemistry laboratories, including their potential for testing automated workflows in silico.
14. Digital Twins and Artificial Intelligence
AI can analyse information generated by a digital twin.
Potential applications include:
- Pattern detection
- Prediction
- Optimization
- Fault detection
- Maintenance support
- Workflow optimization
For example, an AI model could analyse equipment data and identify patterns associated with unusual operating conditions.
The combination can therefore be represented as:
Digital Twin → Data → AI Analysis → Insight → Decision
However, AI predictions need appropriate validation and human oversight.
15. Digital Twins and LIMS
LIMS manages laboratory information and workflows.
Digital twins focus on representing physical systems or processes and their associated information.
The two technologies can therefore complement each other.
For example:
LIMS:
Sample, result and workflow information.
Digital Twin:
Equipment condition, operational state and simulated behaviour.
Integration could potentially provide a more complete digital representation of laboratory operations.
16. Digital Twins and Laboratory 4.0
Digital twins fit naturally into the Laboratory 4.0 concept.
Laboratory 4.0 involves increasing use of:
- Automation
- Connectivity
- AI
- IoT
- Data analytics
- Cloud systems
- Smart equipment
- Digital workflows
Digital twins can provide another layer by connecting physical laboratory assets with digital representations.
The result can be a laboratory that is increasingly:
Connected + Observable + Data-driven + Digitally managed
17. Digital Twins for Laboratory Training
Digital models can potentially support training.
For example, a trainee could interact with a simulated representation of laboratory equipment before operating the physical instrument.
Training applications may include:
- Equipment familiarization
- Workflow simulation
- Troubleshooting exercises
- Maintenance training
- Safety scenarios
- Automation training
This does not necessarily replace practical training.
Instead, digital simulation can complement supervised hands-on learning.
18. Digital Twins for Workflow Planning
Laboratory workflows can become complicated.
A digital representation can help planners examine:
- Equipment placement
- Sample movement
- Robot paths
- Process sequences
- Bottlenecks
- Workflow timing
This can be particularly useful before introducing new automation.
Instead of changing the physical laboratory immediately, some options can first be evaluated digitally.
19. Digital Twins and Virtual Commissioning
Virtual commissioning involves testing or evaluating systems in a virtual environment before physical implementation.
For laboratory automation, this could potentially help evaluate:
- Equipment integration
- Robot movement
- Workflow sequences
- Control logic
- System behaviour
The objective is to identify problems before they become expensive physical problems.
20. Digital Twins and Equipment Lifecycle Management
Laboratory equipment has a lifecycle:
Selection → Procurement → Installation → Commissioning → Operation → Maintenance → Calibration/Verification → Repair → Decommissioning
A digital twin can potentially support information throughout parts of this lifecycle.
It may connect:
- Equipment identification
- Technical specifications
- Operating information
- Maintenance
- Calibration
- Fault history
- Performance information
This can complement the equipment records discussed in earlier KTH articles.
21. Data Requirements for Digital Twins
A digital twin is only as useful as the information supporting it.
Important data considerations include:
- Accuracy
- Completeness
- Timeliness
- Consistency
- Traceability
- Metadata
- Security
Poor-quality data can produce misleading digital representations.
Therefore, laboratories should strengthen their documentation and data-management practices before attempting sophisticated digital-twin projects.
22. Cybersecurity and Digital Twins
Digital twins can involve substantial amounts of operational data.
If connected to laboratory networks, they may also become part of the organization’s digital infrastructure.
Security considerations include:
- Authentication
- Authorization
- Network security
- Data protection
- Access control
- Backup
- Monitoring
- Software updates
A digital twin should therefore be designed with cybersecurity in mind.
23. Challenges of Digital Twins in Laboratories
Digital twins have potential benefits, but they also present challenges.
Cost
Developing sensors, connectivity, software and digital models can require investment.
Data Quality
Poor information can reduce the value of the digital representation.
Integration
Equipment from different manufacturers may use different interfaces.
Skills
Laboratories may need expertise in equipment, software, data and systems integration.
Maintenance
The digital model itself may need to be maintained as the physical system changes.
Cybersecurity
Connected systems require appropriate security controls.
Complexity
A highly detailed digital twin can become difficult and expensive to maintain.
24. Do All Laboratories Need Digital Twins?
No.
A digital twin should solve a genuine laboratory problem.
A laboratory may benefit more from basic improvements such as:
- Better equipment records
- Preventive maintenance
- Calibration management
- Digital documentation
- Environmental monitoring
- LIMS
- Basic automation
before implementing a sophisticated digital twin.
The appropriate question is not:
“Do we need a digital twin because it is modern?”
Instead:
“What laboratory problem would a digital twin help us solve?”
25. How Laboratories Can Begin
A practical implementation pathway could be:
Step 1 — Identify a Problem
Choose a specific equipment or workflow problem.
Step 2 — Improve Data
Ensure relevant equipment and operational records are reliable.
Step 3 — Connect Appropriate Sensors
Collect useful information from the physical system.
Step 4 — Establish Digital Representation
Create an appropriate digital model.
Step 5 — Connect Relevant Systems
Integrate equipment, data and workflow systems where practical.
Step 6 — Add Analytics
Use data analysis to identify patterns.
Step 7 — Explore AI
Apply AI where it provides a clearly defined benefit.
Step 8 — Evaluate Results
Monitor whether the system actually improves the laboratory process.
26. Digital Twins and Africa
Digital-twin adoption in Africa may need to account for local operating conditions.
These may include:
- Infrastructure
- Electricity reliability
- Internet connectivity
- Equipment availability
- Technical support
- Cost
- Staff training
- Data-management capability
This does not mean that digital-twin technology is unsuitable for African laboratories.
It means implementation should be adapted to the actual environment.
A practical starting point may be a focused digital representation of one important equipment asset or workflow rather than an attempt to digitize an entire laboratory immediately.
27. Skills Needed for Digital-Twin Technologies
Professionals working with laboratory digital twins may benefit from a combination of skills.
Laboratory Knowledge
- Laboratory procedures
- Safety
- Quality
- Workflow
Equipment Knowledge
- Instrumentation
- Electronics
- Sensors
- Maintenance
- Troubleshooting
Digital Skills
- Data management
- Databases
- Networking
- Software
Advanced Technology
- IoT
- Cloud computing
- AI
- Data analytics
- Simulation
Professional Skills
- Systems thinking
- Problem solving
- Communication
- Continuous learning
This reinforces an important theme throughout the KTH Laboratory Technology series:
The future laboratory professional will increasingly need interdisciplinary awareness.
28. Practical Example: Digital Twin for a Laboratory Incubator
Consider a laboratory incubator.
The physical incubator may have:
- Temperature sensors
- Controllers
- Operating records
- Maintenance records
- Calibration information
A digital twin could combine relevant information into a digital representation.
The system could potentially display:
- Current temperature
- Historical temperature
- Operating hours
- Maintenance status
- Calibration status
- Alerts
- Previous faults
If unusual temperature behaviour occurs, the system could alert laboratory personnel.
The professional would then investigate the physical equipment and determine the appropriate action.
The digital twin supports the decision; it does not replace technical responsibility.
29. The Future of Digital Twins in Laboratory Technology
As laboratories become more connected, digital twins may increasingly interact with:
- AI
- Robotics
- IoT
- LIMS
- Cloud computing
- Laboratory automation
- Predictive maintenance
- Self-driving laboratories
This could eventually produce digital representations of increasingly complex laboratory workflows rather than individual instruments.
However, the pace of adoption will vary.
Technological capability alone does not determine whether a digital twin is useful.
Laboratory objectives, infrastructure, data quality, cost, skills, safety and governance all matter.
30. Frequently Asked Questions
What is a digital twin in laboratory technology?
It is a digital representation of a physical laboratory asset, process, workflow or environment that can use relevant data to support monitoring, analysis, simulation or decision-making.
Is a digital twin the same as a simulation?
Not necessarily. Simulation can model a system, while a digital twin generally maintains a relationship with a specific physical system through relevant data.
Can digital twins help with laboratory equipment maintenance?
They can potentially support condition monitoring, maintenance planning, fault investigation and predictive-maintenance applications.
Can a digital twin replace laboratory equipment?
No. A digital twin represents or models a physical system; it does not automatically replace the physical equipment.
Can digital twins work with AI?
Yes. AI can analyse information associated with a digital twin for applications such as prediction, optimization and fault detection.
Can digital twins be used with LIMS?
They can potentially be integrated with LIMS and other laboratory information systems, depending on the implementation and interoperability of the systems.
Are digital twins useful for Laboratory 4.0?
They can support Laboratory 4.0 by connecting physical laboratory systems with digital models, data, analytics and automation.
Does every laboratory need a digital twin?
No. Laboratories should first identify whether a digital twin addresses a genuine operational, equipment or workflow problem.
Conclusion
Digital twins represent an emerging approach to connecting physical laboratory systems with digital information, simulation and analysis.
They can potentially support equipment monitoring, maintenance, troubleshooting, automation, robotics, training, workflow planning and autonomous experimentation.
Their importance is likely to increase as laboratories become more connected.
However, digital twins should not be viewed as a technology that automatically improves laboratory performance.
Their value depends on:
- Good data
- Reliable equipment
- Appropriate sensors
- Effective integration
- Skilled personnel
- Cybersecurity
- Quality systems
- Clearly defined objectives
The most practical approach is therefore to begin with a genuine laboratory problem and determine whether a digital-twin solution can address it effectively.
Knowledge Tech Hub Practical Takeaway
Digital twins can be understood as a bridge between:
Physical Laboratory + Sensors + Data + Digital Model + Analytics + Decision Support
They represent another step in the evolution from traditional laboratory equipment toward smart, connected and digitally managed laboratory environments.
For laboratory professionals, understanding digital twins does not necessarily mean becoming a software engineer.
It means understanding how physical equipment, data and digital technologies can work together.
Suggested Internal Links
This article should link to:
- Laboratory Equipment: Names, Functions and Uses
- How to Use Laboratory Equipment Correctly
- Laboratory Equipment Maintenance
- Preventive Maintenance for Laboratory Equipment
- Laboratory Equipment Troubleshooting
- Laboratory Equipment Calibration
- Laboratory Documentation and Record Keeping
- Laboratory Automation
- Laboratory Information Management Systems (LIMS)
- Laboratory 4.0
- Artificial Intelligence in Laboratory Technology
- The Future of Laboratory Technology
- Self-Driving Laboratories
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References and Further Reading
- Zhao, T., & Zeng, Y. (2026). Digital twins for self-driving chemistry laboratories. Nature Computational Science.
- Salazar-Villacis, P., & Benyahia, B. (2026). The ADePT framework for assessing autonomous laboratory robotics. Communications Chemistry.
- Canty, R. B., & Abolhasani, M. (2026). The past, present and future of self-driving laboratories. Nature Reviews Chemistry.
- Relevant laboratory automation, instrumentation, digital-twin, quality-management and equipment-manufacturer documentation.
- Applicable laboratory data, cybersecurity and quality-management requirements.
Editorial note: Digital-twin applications should be evaluated according to laboratory objectives, infrastructure, equipment capability, data quality, cybersecurity, available expertise and applicable requirements. A digital twin should solve a defined laboratory problem rather than be adopted simply because it is a modern technology.
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