Energy and eco-efficiency of manufacturing processes

Abstract

The research about energy and eco-efficiency of manufacturing processes is of great interest to all parties including manufacturers, consumers, government and others. This is due to the soaring energy costs and the environmental impacts caused by high energy consumption levels. However, the energy consumption of manufacturing process and the associated environmental impact has been historically overlooked. Unit process is the fundamental, but dynamic element of any manufacturing system. Thus, it requires adequate evaluation of its energy and eco-efficiency in order to derive further improvement measures. This research has developed a reliable methodology for evaluating energy and eco-efficiency of unit manufacturing processes. The Specific Energy Consumption, SEC, has been identified as the key indicator for the energy efficiency of unit processes. An empirical approach has been validated on different machine tools and manufacturing processes to characterize the relationship between process parameters and energy consumptions. All the derived SEC models agree with a universal form, where the Material Removal Rate (MRR) or throughput plays a decisive role in the model. The statistical results and additional validation runs have proved the high accuracy of the derived models which is capable of predicting energy consumption with an accuracy of over 90 per cent. In order to evaluate the eco-efficiency of manufacturing processes, the value and the associated environmental impacts of the processes have also been discussed. Besides the electricity energy consumption, other resource consumptions such as tool and coolant have been taken into account. The interrelationship among process parameters, process value and the associated environmental impact has been integrated in the evaluation of eco-efficiency. The evaluation results have been further investigated to develop strategies for improving the energy and eco-efficiency of manufacturing processes. In addition, the derived SEC models of manufacturing processes have been decomposed in order to explore the mechanism of the model coefficients. A clustered model has been also derived for a rough estimation. Moreover, the derived SEC models can also improve the quality of Life Cycle Inventory (LCI) databases. In addition, the different energy efficiency strategies have been proposed from different perspectives.