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"Preparing and implementing new computational and measurement-based methods of modeling and machine and process simulation aimed at predicting the technical condition in order to increase availability of critical equipment and improve efficiency of energy generation with the use of advanced Distributed Edge Computing technology and the Hybrid Cloud"


The main objective of the project is to design and develop a universal platform for modeling and simulation of the technical condition of industrial equipment in order to enhance the energy generation efficiency. The proposed integrated system will solve the issues related to the occurrence of significant energy losses, decrease in efficiency and availability of critical machines due to their technical condition and the technical condition of auxiliary machinery used in energy generation processes. The project will have its final result in an Internet platform built in the architecture of the Industrial Internet of Things, integrated directly with advanced technologies of Distributed Edge Computing and Hybrid Cloud within a dedicated measurement and data acquisition system – Elmodis Smart Drive Monitoring System. As a complete solution, the system will constitute a flexible, user-friendly and effective asset management environment. It will serve as a central node of the entire system and will be used to configure the equipment involved in SDMS, data processing, model preparation, implementation and analysis in a real industrial environment. The final functionality of the platform within the project is dedicated in particular for the machines used in energy sector. The architecture and functions of the system will make it possible to transfer the prepared models, data processing schemes (downloading, validation, processing) and machine configurations to the remote modules (Edge Computing) in order to perform a real-time analysis of data stream based on high frequency sampling of the selected signals. A key functionality will consist also in communication and bilateral data exchange with Cloud Computing services such as Microsoft Azure Machine Learning Studio. The environment will be available with the use of an Internet browser without the necessity to install any additional software. Algorithms and computational methods grouped in ready-for-use, proven functional blocks will be available via a clear, intuitive and modern end-user interface. The efficiency of optimization and predictive modeling solutions based on machine learning algorithms will be provided with sufficient amount of data collected from various sources by an interconnected measuring system.

Working in the environment will not require an end user to have wide expert knowledge. The embedded expandable knowledge base and reporting system will support the process of optimization and decisions regarding the maintenance of machine technical condition and availability. The machinery base will include functional blocks containing the information on kinematics/structure, operating specification, input/output parameters and the parameters available for monitoring in the selected types of machines and equipment used in energy sector. The target group includes power plants and plants generating energy for their own needs (sugar refiners, food-processing establishments, chemical, petrochemical and gas processing plants). After a phase of research and achievement of the planned objectives the company plans to use the results of research in order to provide patent protection and implement the results of the project (i.e. services) in its business activity.


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