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For billing purposes, heavy-haul locomotives in Sweden are equipped with on-board energy meters, which can record several parameters, e.g., used energy, regenerated energy, speed and position. Since there is a strong demand for improving energy efficiency in Sweden, data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.

To monitor energy efficiency, the present study, therefore, develops key performance indicators (KPIs), which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation. Energy meter data of IORE class locomotives, hauling highly uniform 30-tonne axle load trains with 68 wagons, together with additional data sources, are analysed to identify significant parameters for describing driver influence on energy usage.

Results show that driver behaviour varies significantly and has the single largest influence on energy usage. Furthermore, parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions, e.g., axle loads and number of wagons, on energy usage.

Based on the parametric studies, some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived. In the end, some possible measures for improving energy performance in heavy-haul operations are given.

Values of parameters such as temperature, humidity, number of plastic products and the location of plastic injection moulds are required to determine the efficiency of plastic injection moulds with a view to improving the quality of the outputs. This article determined the appropriate sensors for the measurement of these essential parameters in the most suitable form of representation of the data to aid a proficient analysis of the data.

The outputs of these sensors were obtained by connecting the sensors to the general-purpose input/output (GPIO) pins of a Raspberry Pi and writing a Python programme for the connected GPIO pins. The values of the outputs of these sensors were represented in a graphical form. The connection of the Raspberry Pi and the sensors were done with a full-sized breadboard and jumper wires. A computer-aided design (CAD) of the connections was produced using Fritzing software.

The appropriate sensors determined are MLX90614 infrared thermometer sensor, DHT11 humidity sensor, pixy2 vision sensor and Neo-6m GPS sensor. This study proposed that the sensors analytic system be applied on an industrial plastic injection mould to measure and display the various parameters of the injection moulds for the purpose of understanding and improving the performance of the injection mould

An electronic system that provides the continuous values of essential parameters of a plastic injection mould in operation.