Digital Twins for Process Optimization
Detailed overview of innovation with sample startups and prominent university research
What it is
A digital twin is a virtual representation of a physical asset or process that mirrors its real-world counterpart in near real-time. In industrial process optimization, digital twins are used to simulate and analyze process behavior, allowing engineers to identify inefficiencies, test different scenarios, and optimize process parameters without disrupting actual operations.
Impact on climate action
Digital Twins for Process Optimization within Industrial Resource Efficiency drive climate action by simulating and optimizing industrial processes. By minimizing energy consumption, reducing emissions, and improving resource efficiency, these innovations enhance sustainability, mitigate environmental impact, and contribute to a more efficient and eco-friendly industrial ecosystem.
Underlying
Technology
- Process Modeling and Simulation: Digital twins are built on sophisticated process models that capture the complex relationships between different process parameters. These models are used to simulate process behavior under various conditions.
- Data Integration and Analytics: Digital twins integrate data from various sources, such as sensors, process historians, and enterprise systems. This data is used to update the digital twin model and provide real-time insights into process performance.
- Artificial Intelligence (AI) and Machine Learning (ML): AI and ML algorithms can be used to analyze process data, identify patterns and anomalies, and recommend process optimization strategies.
- Industrial Internet of Things (IIoT): IIoT sensors and devices provide real-time data on process parameters, enabling the digital twin to accurately reflect the current state of the physical process.
TRL : 6-7
Prominent Innovation themes
- High-Fidelity Process Models: Innovations in process modeling and simulation are creating more accurate and detailed digital twins that can better predict real-world process behavior.
- AI-Powered Optimization Algorithms: Advanced AI algorithms are being developed to analyze process data and recommend optimization strategies, improving the efficiency and effectiveness of process optimization.
- Cloud-Based Digital Twin Platforms: Cloud-based platforms are making it easier to develop, deploy, and manage digital twins, making the technology more accessible to businesses.
- Integration with Automation Systems: Digital twins are being integrated with automation systems to enable real-time process control and optimization.
Sample Global Startups and Companies
- Siemens:
- Technology Enhancement: Siemens develops digital twin solutions for process optimization across various industries, including manufacturing, energy, and transportation. Their digital twin technology creates virtual replicas of physical assets, systems, and processes, allowing for real-time monitoring, analysis, and optimization.
- Uniqueness of the Startup: Siemens’ digital twin technology integrates data analytics, simulation, and visualization to provide insights into process performance and efficiency. Their solutions enable predictive maintenance, scenario modeling, and continuous improvement, helping companies optimize operations and reduce downtime.
- End-User Segments Addressing: Siemens serves industries with complex and interconnected processes, including automotive, aerospace, chemical, and power generation sectors. Their digital twin solutions cater to manufacturers, utilities, and infrastructure operators seeking to enhance productivity, reliability, and sustainability.
- GE Digital:
- Technology Enhancement: GE Digital offers digital twin solutions for process optimization and predictive maintenance in industrial settings. Their Predix platform enables the creation of digital twins for equipment, production lines, and entire facilities, allowing for data-driven decision-making and performance optimization.
- Uniqueness of the Startup: GE Digital’s digital twin technology leverages industrial internet of things (IIoT) sensors, data analytics, and machine learning to monitor and analyze process data in real time. Their solutions enable proactive maintenance, predictive analytics, and remote monitoring, improving asset reliability and efficiency.
- End-User Segments Addressing: GE Digital serves industries with complex and critical assets, including manufacturing, energy, healthcare, and aviation sectors. Their digital twin solutions help companies optimize asset performance, reduce maintenance costs, and maximize uptime across various industrial applications.
- AVEVA:
- Technology Enhancement: AVEVA specializes in digital twin solutions for process industries, including oil and gas, chemicals, and utilities. Their software platform integrates engineering, operations, and maintenance data to create holistic digital representations of industrial assets and processes.
- Uniqueness of the Startup: AVEVA’s digital twin technology enables companies to simulate, analyze, and optimize process operations in real time. Their solutions support asset performance management, predictive maintenance, and operational excellence initiatives, driving efficiency and profitability.
- End-User Segments Addressing: AVEVA serves process industries seeking to improve operational efficiency, safety, and sustainability. Their digital twin solutions are used by operators, engineers, and managers in oil refineries, chemical plants, power plants, and other process facilities to optimize production and mitigate risks.
Sample Research At Top-Tier Universities
- University of California, Berkeley:
- Research Focus: UC Berkeley is actively involved in research on Digital Twins for Process Optimization, focusing on developing advanced modeling and simulation techniques to create virtual replicas of industrial processes for optimization and control.
- Uniqueness: Their research involves integrating machine learning, physics-based modeling, and real-time data analytics to create dynamic digital twins that accurately represent the behavior of complex industrial systems and enable predictive optimization and decision-making.
- End-use Applications: UC Berkeley’s work has applications in manufacturing, energy, and chemical processing. For example, they’re developing digital twins for optimizing energy-intensive processes such as steelmaking, chemical synthesis, and semiconductor manufacturing, leading to improved energy efficiency and reduced environmental impact.
- Stanford University:
- Research Focus: Stanford University conducts cutting-edge research on Digital Twins for Process Optimization, exploring innovative approaches for creating and deploying digital twins to improve the efficiency, reliability, and sustainability of industrial operations.
- Uniqueness: Their research involves developing scalable and adaptable digital twin frameworks that leverage sensor data, machine learning algorithms, and optimization techniques to optimize production processes, reduce downtime, and minimize resource consumption.
- End-use Applications: Their work finds applications in oil and gas, healthcare, and transportation. For instance, they’re researching digital twins for optimizing oil refinery operations, predicting equipment failures, and scheduling maintenance activities to enhance operational reliability and safety.
- Massachusetts Institute of Technology (MIT):
- Research Focus: MIT is a leader in research on Digital Twins for Process Optimization, focusing on developing advanced computational tools and control strategies to enable real-time optimization and decision support in complex industrial systems.
- Uniqueness: Their research involves combining data-driven modeling techniques, optimization algorithms, and human-machine interfaces to create interactive digital twins that empower operators and engineers to visualize, analyze, and optimize industrial processes in real time.
- End-use Applications: MIT’s work has applications in aerospace, automotive, and pharmaceuticals. For example, they’re researching digital twins for optimizing aircraft assembly processes, predicting product quality variations, and minimizing production costs while meeting quality and safety requirements.
Commercial Implementation
Digital twins are being implemented in various industries, including manufacturing, chemical processing, and energy. For example, Shell uses digital twins to optimize the performance of its oil and gas production facilities, while pharmaceutical companies use them to improve the efficiency and quality of drug manufacturing processes.
