D.11: Hybrid Digital Twin - Revision history

Admin ugqr649i at 09:29, 28 April 2025

2025-04-28T09:29:19Z

← Older revision		Revision as of 09:29, 28 April 2025
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	• '''Asset Type''': As A Service		• '''Asset Type''': As A Service

	• '''AI Breadth''':		• '''AI Breadth''': ML

	• '''Learning Ability''':		• '''Learning Ability''': N.A

	• '''Related technologies''':		• '''Related technologies''': N.A

	• '''Applicable Research Area''':		• '''Applicable Research Area''': Collaborative AI

	• '''Applicable Technical Category''':		• '''Applicable Technical Category''': Other

	• '''License Information''': Copyright		• '''License Information''': Copyright
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	• '''Related to circularity and sustainability''': Yes		• '''Related to circularity and sustainability''': Yes

	• '''Audience''': Petrochemical industry and ~~energy~~ sector		• '''Audience''': Petrochemical industry and Energy sector


	Go back to the '''[[AI portfolio of reference implementations]]'''		Go back to the '''[[AI portfolio of reference implementations]]'''

Admin ugqr649i at 09:06, 28 April 2025

2025-04-28T09:06:39Z

← Older revision		Revision as of 09:06, 28 April 2025
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	• '''Applicable Technical Category''':		• '''Applicable Technical Category''':

	• '''License Information~~: Proprietary license~~''': Copyright		• '''License Information''': Copyright

	• '''Related to circularity and sustainability''': Yes		• '''Related to circularity and sustainability''': Yes

Admin ugqr649i at 08:36, 8 April 2025

2025-04-08T08:36:48Z

← Older revision		Revision as of 08:36, 8 April 2025
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	• Integrating advanced AI and machine learning algorithms for predictive analytics and process optimization.		• Integrating advanced AI and machine learning algorithms for predictive analytics and process optimization.

	• Application Programming Interfaces (APIs) for interoperability, enabling the Hybrid DT to interact with different software systems,Aspen and MATLAB, and platforms for data analysis and process optimization.		• Application Programming Interfaces (APIs) for interoperability, enabling the Hybrid DT to interact with different software systems,Aspen and MATLAB, and platforms for data analysis and process optimization.

	• Utilizing industry-standard communication protocols like OPC UA and Modbus to facilitate data exchange between the Hybrid DT, Aspen models, control systems (DCS and SCADA), and MATLAB.		• Utilizing industry-standard communication protocols like OPC UA and Modbus to facilitate data exchange between the Hybrid DT, Aspen models, control systems (DCS and SCADA), and MATLAB.

	• Incorporating robust security measures to protect sensitive data and system integrity.		• Incorporating robust security measures to protect sensitive data and system integrity.

	• Ensuring compatibility with various industrial data standards for seamless data acquisition and integration.		• Ensuring compatibility with various industrial data standards for seamless data acquisition and integration.

	Finally it supports many relevant standards on ISO/IECD and ISO/IEC.		Finally it supports many relevant standards on ISO/IECD and ISO/IEC.

Admin ugqr649i at 08:36, 8 April 2025

2025-04-08T08:36:31Z

← Older revision		Revision as of 08:36, 8 April 2025
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	Hybrid Digital Twin (Hybrid DT) encompasses the integration of various standards, protocols, and APIs to ensure effective and secure data handling. This integration typically includes:		Hybrid Digital Twin (Hybrid DT) encompasses the integration of various standards, protocols, and APIs to ensure effective and secure data handling. This integration typically includes:

	- Integrating advanced AI and machine learning algorithms for predictive analytics and process optimization.		• Integrating advanced AI and machine learning algorithms for predictive analytics and process optimization.
	- Application Programming Interfaces (APIs) for interoperability, enabling the Hybrid DT to interact with different software systems,Aspen and MATLAB, and platforms for data analysis and process optimization.		• Application Programming Interfaces (APIs) for interoperability, enabling the Hybrid DT to interact with different software systems,Aspen and MATLAB, and platforms for data analysis and process optimization.
	- Utilizing industry-standard communication protocols like OPC UA and Modbus to facilitate data exchange between the Hybrid DT, Aspen models, control systems (DCS and SCADA), and MATLAB.		• Utilizing industry-standard communication protocols like OPC UA and Modbus to facilitate data exchange between the Hybrid DT, Aspen models, control systems (DCS and SCADA), and MATLAB.
	- Incorporating robust security measures to protect sensitive data and system integrity.		• Incorporating robust security measures to protect sensitive data and system integrity.
	- Ensuring compatibility with various industrial data standards for seamless data acquisition and integration.		• Ensuring compatibility with various industrial data standards for seamless data acquisition and integration.
	Finally it supports many relevant standards on ISO/IECD and ISO/IEC.		Finally it supports many relevant standards on ISO/IECD and ISO/IEC.

Admin ugqr649i at 08:34, 8 April 2025

2025-04-08T08:34:49Z

← Older revision		Revision as of 08:34, 8 April 2025
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	• '''Short Description''':		• '''Short Description''': Hybrid Digital Twin (DT) is an advanced tool designed for the process industry, particularly in sectors like petrochemicals and energy. Its primary utility lies in combining a data-driven Digital Twin model with physical process models. This integration enables it to leverage data from various sources, including control systems like DCS and SCADA, and apply AI analytics for predictive capabilities and system monitoring. The Hybrid DT is crucial for understanding and optimizing complex industrial processes, enhancing decision-making, and predicting system performance. It assists in monitoring usage, energy consumption, and emissions, and plays a key role in predictive maintenance and process optimization. This technology is particularly valuable in managing and optimizing the lifecycle of process plants and in aiding the digital transformation of factories.
			Hybrid Digital Twin (Hybrid DT) encompasses the integration of various standards, protocols, and APIs to ensure effective and secure data handling. This integration typically includes:

	~~• '''Reference~~, ~~URL''':~~		- Integrating advanced AI and machine learning algorithms for predictive analytics and process optimization.
			- Application Programming Interfaces (APIs) for interoperability, enabling the Hybrid DT to interact with different software systems,Aspen and MATLAB, and platforms for data analysis and process optimization.
			- Utilizing industry-standard communication protocols like OPC UA and Modbus to facilitate data exchange between the Hybrid DT, Aspen models, control systems (DCS and SCADA), and MATLAB.
			- Incorporating robust security measures to protect sensitive data and system integrity.
			- Ensuring compatibility with various industrial data standards for seamless data acquisition and integration.
			Finally it supports many relevant standards on ISO/IECD and ISO/IEC.

	• '''~~Applicable Business Category~~''':		• '''Reference, URL''': https://www.teknopar.com.tr/

	• '''~~Application in relevant Projects/Initiatives~~''':		• '''Applicable Business Category''': Manufacturing

	• '''Asset Type''':		• '''Application in relevant Projects/Initiatives''': Yes, Circular TwAIn

			• '''Asset Type''': As A Service

	• '''AI Breadth''':		• '''AI Breadth''':
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	• '''Applicable Technical Category''':		• '''Applicable Technical Category''':

	• '''License Information: Proprietary license''':		• '''License Information: Proprietary license''': Copyright

	• '''Related to circularity and sustainability''':		• '''Related to circularity and sustainability''': Yes

	• '''Audience''':		• '''Audience''': Petrochemical industry and energy sector


	Go back to the '''[[AI portfolio of reference implementations]]'''		Go back to the '''[[AI portfolio of reference implementations]]'''

Admin ugqr649i: Created page with "• '''Short Description''': • '''Reference, URL''': • '''Applicable Business Category''': • '''Application in relevant Projects/Initiatives''': • '''Asset Type'..."

2025-03-11T08:06:44Z

Created page with "• '''Short Description''': • '''Reference, URL''': • '''Applicable Business Category''': • '''Application in relevant Projects/Initiatives''': • '''Asset Type'..."

New page

• '''Short Description''':

• '''Reference, URL''':

• '''Applicable Business Category''':

• '''Application in relevant Projects/Initiatives''':

• '''Asset Type''':

• '''AI Breadth''':

• '''Learning Ability''':

• '''Related technologies''':

• '''Applicable Research Area''':

• '''Applicable Technical Category''':

• '''License Information: Proprietary license''':

• '''Related to circularity and sustainability''':

• '''Audience''':

Go back to the '''[[AI portfolio of reference implementations]]'''