Project

DENiM Project - Unlocking the Energy Saving Potential in Manufacturing Systems

Goal: The overarching objective of DENiM project is the development of an interoperable digital intelligence platform to enable a collaborative approach to industrial energy management.

Acknowledgement:
This project has received funding from the European Union’s H2020 research and innovation programme under Grant Agreement N 958339.

Disclaimer:
The information contained in the documents reflects only and exclusively the point of view of the authors. The European Commission is not responsible for any use that may be made of this information.

Date: 1 November 2020

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Project log

Juan Manuel Escaño
added an update
Two special issues of Energies, related to the DENiM research project, have been carried out:
  1. Model Predictive Control System Design and Implementation. This special issue has sought to bring together advances in the design and implementation of predictive controllers in systems where energy plays a key role. From power converters to solar plants, power grids to hydroelectric power generation, there are countless applications where MPC and energy are intertwined, perhaps as part of a larger system. https://www.mdpi.com/journal/energies/special_issues/model_predictive_control_system
  2. Microgrids in Industry, Integration of Renewable Energy in Industry. Conventional power systems are evolving into smart grids which incorporate renewable energy sources and storage units interfaced through power electronics converters. These systems are the important keys to using smart, flexible, techno-economic techniques for the reliable operation and cost-effective utilization of electricity grids. Some of the key concerns with these new systems are the lack of inertia in the grid and the two-way flow of power that require advanced control methods and modern protection strategies to ensure the demand/supply balance and dynamic stability of the grid. This Special Issue focuses on the recent achievements and advancements in overcoming these challenges. https://www.mdpi.com/journal/energies/special_issues/MIIREI
 
Juan Manuel Escaño
added a research item
The paper presents the design of a classifier of variable failures in a wind turbine system. The classifier is based on a structure formed by several TS fuzzy inference systems, with projections of the data onto components of a principal component analysis. The classifier is part of a discrepancy evaluator for triggering the learning mechanism of the digital twin of the wind turbine.
Andrea francesco Barni
added a research item
As a result of the worldwide depletion of natural resources, increased energy use, and environmental, economic, and social imbalance, organizations are working to identify the proper strategies supporting the continuous reduction of their impacts. While this trend is fundamentally agreed upon in the literature, several manufacturing industries still fail to identify which elements most influence their contributions to the impact of sustainability and how to easily manage the calculation of these effects within a manufacturing system. The purpose of this article is to incorporate sustainability practices into manufacturing by developing a set of key performance indicators (KPIs) for assessing and improving environmental and economic management practices at the corporate and production level. The definition of the framework began with in-depth research of the leading indicators and framework types in the literature, integrating the most exploited industrial standards to make them easily acceptable in the industrial domain. Then, to provide a broad view of company behavior, the framework has been designed to take either an inventory and impact point of view, thus providing indicators for the online monitoring of the company operations, or assessing their impacts in an LCA-LCC perspective. In selecting the indicators and the definition of the framework structure, five industrial cases covering different business sectors were involved in identifying the most critical indicators in terms of calculability and defining a structure that would allow for their application in various business situations. Therefore, the defined framework has been validated at a conceptual level, thus laying the basis for future quantitative validation. Twenty key performance indicators (KPIs) for assessing the sustainability of manufacturing firms have been created based on the 163 indicators studied.
Leonardo Moiana
added a project goal
The overarching objective of DENiM project is the development of an interoperable digital intelligence platform to enable a collaborative approach to industrial energy management.
Acknowledgement:
This project has received funding from the European Union’s H2020 research and innovation programme under Grant Agreement N 958339.
Disclaimer:
The information contained in the documents reflects only and exclusively the point of view of the authors. The European Commission is not responsible for any use that may be made of this information.