Energy efficiency optimisation in industrial processes: Integral decision support tool

Assessing and improving energy efficiency in manufacturing processes is a crucial issue for companies who wish to become competitive. In addition, there are high margins of technical energy efficiency improvement for practically each sector and kind of industry. There are many Energy Efficiency Meas...

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Veröffentlicht in:Energy (Oxford) 2020-01, Vol.191, p.116480, Article 116480
Hauptverfasser: Bonilla-Campos, Iñigo, Nieto, Nerea, del Portillo-Valdes, Luis, Manzanedo, Jaio, Gaztañaga, Haizea
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container_start_page 116480
container_title Energy (Oxford)
container_volume 191
creator Bonilla-Campos, Iñigo
Nieto, Nerea
del Portillo-Valdes, Luis
Manzanedo, Jaio
Gaztañaga, Haizea
description Assessing and improving energy efficiency in manufacturing processes is a crucial issue for companies who wish to become competitive. In addition, there are high margins of technical energy efficiency improvement for practically each sector and kind of industry. There are many Energy Efficiency Measures (EEM)s and tools in the literature to assess energy efficiency. To fit an EEM to a specific process is a complicated task, due to the different characteristic and restrictions of each specific manufacturing process. Owing to the complexity of the non-continuous processes, each measure has to be deeply analysed. Consequently, this work presents an integral analysis, process optimisation and decision support tool to assess the implementation of EEMs in these processes. This tool allows us to analyse both the possible synergies that may be created among the EEMs and their impact on other parts of the process line. The optimum solution or combination of EEMs, and their sizing, are determined to reach the highest levels of energy efficiency. The impact on the overall energy efficiency and the synergies among EEMs is shown. Several packages of EEMs are analysed, with energy consumption reductions ranging from 21% to 50%. •This tool deeply assess the process and any proposed energy efficiency measure.•The proposed sets of measures reduce the energy consumption up to 50%.•The dynamical interactions and the energy behaviour of the measures is analysed.•The paybacks of the combinatorial packages range from 13 to 28 months.•The scenarios are evaluated in energy, production, ecological and economic terms.
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subjects Decision analysis
Decision support systems
Decision-making-process
Energy & Fuels
Energy consumption
Energy efficiency
energy efficiency measures
Integrals
Manufacturing industry
Optimisation tool
Optimization
Physical Sciences
Power efficiency
Process manufacturing
Science & Technology
Technology
Thermodynamics
title Energy efficiency optimisation in industrial processes: Integral decision support tool
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