Improving energy efficiency in manufacturing using peer benchmarking to influence machine design innovation

Phil Sheppard,Shahin Rahimifard

doi:10.1007/s10098-019-01701-4

Phil Sheppard, Shahin Rahimifard

Open Access

https://doi.org/10.1007/s10098-019-01701-4

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Abstract

Energy efficiency in manufacturing is important for overall sustainability of society. This paper combines three observations to improve an overlooked part of the energy efficiency support infrastructure in food and drink manufacturing: innovation capability. First, variations in machine and process design produce significant differences in energy efficiency; second, these differences are not widely known or valued because comparable machine energy data are not gathered for the vast majority of products, so machine and process design is under-used as a route to efficiency improvement; third, peer benchmarking has proved to be an effective tool for stimulating change in other contexts, but has not been used at machine level in manufacturing. This paper describes and makes the case for a self-sustaining system in which machines would be validly compared on energy consumption and peer benchmarking would stimulate innovation in machine and process design for food manufacturing. The system, to be tested in a feasibility study, would benefit both food manufacturers and stakeholders. It would avoid dependence on public funding and enable stakeholders to provide value from the data. The paper contains the academic underpinning for the system and sets out an effective means of using it to achieve practical change.Graphical abstract

Highlights

Driven by the political drivers of global warming, energy security and energy affordability, energy efficiency in food and drink manufacturing and other industrial sectors in many countries has received significant attention in industry and government reports, in addition to the academic literature
This paper describes a proposal at this stage, so details of the experimental setup will be left for the report on the experiment, but the main variables on peer benchmarking will be as follows: Dependent variable: Independent variables: Energy consumption differences by machine brand for a defined intermediate or final food product
This paper has described the emergence of a system of influence and leverage for energy efficiency in food manufacturing from a train of logic which began with the observation that little was known about energy consumption at the machine level

Summary

Introduction

Driven by the political drivers of global warming, energy security and energy affordability, energy efficiency in food and drink manufacturing (shortened to ‘food manufacturing’ throughout this paper) and other industrial sectors in many countries has received significant attention in industry and government reports, in addition to the academic literature. ISO 50006:2014 (BSI 2015) provides guidance on how to establish, use and maintain energy performance indicators (EnPIs) and energy baselines (EnBs) in measuring energy performance and energy performance changes For food products, this data acquisition step can initially often take the form of eliciting and assembling the knowledge of long-standing personnel or experienced consultants in order to identify the processes using the most energy and with the greatest scope for reduction. The remainder of this paper describes a novel system for accelerating and enriching innovation in the design of food manufacturing machines for energy efficiency purposes, utilising aspects of data science and behavioural science to stimulate and inform engineering science. Material density of moving parts, other materials properties, impeller geometry and pipe layout (see Table 3)

Design aspects

Conclusion and next steps

Findings

Compliance with ethical standards