A carregar...
Publicação
Context-based identification of energy consumption in industrial plants
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 11.16 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Nowadays, reducing energy consumption is one of the highest priorities and biggest
challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers.
Particularly in industrial environments, energy consumption is affected by several
factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy
consumption patterns due to the lack of structure to incorporate context influence,
hence are not able to track down the causes of consumption to a process level, where
optimization measures can actually take place.
This dissertation proposes a new approach to tackle this issue, by on-line estimation
of context-based energy consumption models, which are able to map operating context to
consumption patterns. Context identification is performed by regression tree algorithms.
Energy consumption estimation is achieved by means of a multi-model architecture using
multiple RLS algorithms, locally estimated for each operating context.
Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic
context identification and energy consumption estimation.
Descrição
Palavras-chave
Energy consumption Context awareness Regression tree Multi-models Recursive least-squares