Energy characterisation and benchmarking of factories
Introduction
Energy and the associated emissions are of great concerns in today's world. The manufacturing industry, in particular, is affected from this since manufacturing sector consumes nearly one-third of the global energy generated [1]. Improving energy efficiency in manufacturing can be considered as a pragmatic and an attractive solution, because it assists manufacturers to address the mentioned concerns as well as reducing their production cost, ultimately enhancing their competitiveness in the market.
In order to systematically improve the energy efficiency, it is essential to identify improvement potentials and to monitor the progress at the factory level. One approach is to derive references or targets through benchmarking which is a well-established management tool [2], [3]. The present development in energy benchmarking for factories is mainly based on industrial surveys for a specific sector. For example, the Energy Star® industry programme uses statistical analysis to determine a probabilistic frontier for automotive industries [4]. The BEST (benchmarking and energy savings tool) uses a bottom-up approach to compare each unit process with a hypothetical best process from a sector-specific survey (e.g. iron and steel industry) [5]. However, those methods are limited to available industry surveys which require great efforts and need to be updated regularly. In addition, it is often unfair to compare with an external practice due to the variety of products, processes and factories.
Alternatively, benchmarking can be performed through the comparison with a theoretical limit [2]. In the context of energy benchmarking, the concept of minimal/theoretical energy requirements can serve as an unbiased reference for a given manufacturing system. Therefore, this paper aims to develop a generic methodology to derive such reference points for a given factory.
Section snippets
Analytic approach and its limitation
In order to provide the theoretical background, the analytic approach from a thermodynamic perspective is first discussed in this section. This approach is based on ‘exergy’ that has been defined by Sciubba and Wall [6] as ‘the maximum theoretical useful work obtained if a system S is brought into thermodynamic equilibrium with the environment by means of processes in which the S interacts only with this environment’.
Gutowski et al. [7] introduced an exergy framework for manufacturing systems.
Alternative approach: empirical characterisation
Unlike analytic approaches, empirical modelling uses observations and statistical analysis to characterise the relationship between cause (i.e. variables) and effect (i.e. responses). The derived relationship can be potentially used to estimate the theoretical limit [8]. It is often used in conjunction with Design of Experiments (DOE), and has been successfully adapted to characterise the energy efficiency of unit processes [9]. However, it is not directly applicable at a factory level. The
Energy benchmarking
According to the efficiency definition, the energy performance of a system can be measured by the ratio of minimal/theoretical energy requirement against the actual energy input. Two indicators are proposed by using the results from the above empirical characterisation.
Firstly, the observed specific exergy consumption at ith observation, SexCi, is compared with the minimal/theoretical exergy requirement, C0, namely the absolute energy efficiency (Abs. ŋ), as shown in Eq. (10).
Conclusion and outlook
Instead of using rigorous industrial surveys or exergy analysis, the energy benchmarking of manufacturing factories can be simplified with an empirical approach. Fig. 5 summarises the steps of the proposed energy benchmarking methodology. There are a number of unique benefits of this proposed methodology:
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Low efforts for data collection: As indicated in Stage 1, Fig. 5, the studied manufacturing system can be treated as a black-box, and only the information about input energy and output product
Acknowledgement
The authors would like to acknowledge our industrial partners to support this research project.
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