A methodology for determining auxiliary and value-added electricity in manufacturing machines

A methodology was developed that accurately and flexibly determines the auxiliary (AU) and value-added electricity in manufacturing operations. A tool was developed for production engineers which allows for the verification of machine efficiency in relation to their energy consumption. Historical production and electricity consumption data were collected for a period of three months from four different machines in a value stream at a manufacturing facility. The data were examined using a methodology based on statistical analysis of the historical data collected and were verified using heuristic machines profiles. Results showed AU electricity consumption varied between 10 and 26% per machine. When weekend data (non-productive periods) were excluded from calculations, AU electricity consumption reduced. Past work focuses on optimising single machine, and the quantification of wasted electricity is not always clear. This research work can be applied to one or more machines, and to single or multiple products passing through the same machine. It places particular attention to AU electricity since potential energy and cost reduction of up to 20% could be achieved. Hence, this work can aid in developing key performance indicators to measure energy usage in manufacturing operations, particularly focused towards reducing AU electricity consumption.

[1]  T. Gutowski,et al.  Electrical Energy Requirements for Manufacturing Processes , 2006 .

[2]  Christoph Herrmann,et al.  Energy oriented simulation of manufacturing systems - Concept and application , 2011 .

[3]  John W. Sutherland,et al.  A New Shop Scheduling Approach in Support of Sustainable Manufacturing , 2011 .

[4]  Mehmet Bayram Yildirim,et al.  A framework to minimise total energy consumption and total tardiness on a single machine , 2008 .

[5]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[6]  Yingying Seow,et al.  A framework for modelling embodied product energy to support energy efficient manufacturing , 2011 .

[7]  Jiří Zelinka,et al.  Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing , 2012 .

[8]  Alexander Verl,et al.  A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing , 2009 .

[9]  Björn Johansson,et al.  Environmental aspects in manufacturing system modelling and simulation—State of the art and research perspectives , 2013 .

[10]  Corinne Reich-Weiser,et al.  Metrics for Sustainable Manufacturing , 2008 .

[11]  Pierre Baptiste,et al.  Scheduling issues for environmentally responsible manufacturing: The case of hoist scheduling in an electroplating line , 2006 .

[12]  Janet M. Twomey,et al.  Operational methods for minimization of energy consumption of manufacturing equipment , 2007 .

[13]  Saad Mekhilef,et al.  A review on energy saving strategies in industrial sector , 2011 .

[14]  Shahin Rahimifard,et al.  Minimising Embodied Product Energy to support energy efficient manufacturing , 2010 .

[15]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[16]  John W. Sutherland,et al.  A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction , 2011 .

[17]  Timothy G. Gutowski,et al.  An Environmental Analysis of Machining , 2004 .

[18]  Ningjian Huang,et al.  Optimal Scheduling to Achieve Energy Reduction in Automotive Paint Shops , 2009 .

[19]  Zhuming M. Bi,et al.  Revisiting System Paradigms from the Viewpoint of Manufacturing Sustainability , 2011 .