04/02033 Technology intervention to improve the energy efficiency and productivity of silk reeling sector

The aim of this study was to determine the energy efficiency indices in the agro-ecosystems of the Guilan province in 2010. One hundred and twenty-seven farmers were interviewed using a particularly designed questionnaire. The inputs in the calculation of energy use in agro-ecosystems embraced labour, machinery, electricity, diesel oil, fertilizers, seeds, while rice and straw yield were included in the output. The results depicted that total input and output energy into these agro-ecosystems were about 47,604 and 90,680.04 MJ/ha, respectively. The highest energy input was related to water (38.84%), electricity (27.87%) and nitrogen fertilizer (17.5%). Energy efficiency and energy productivity in these agro-ecosystems was 2.19 and 0.064 kg/MJ, respectively, and water productivity was 0.11 kg/m<sup>3</sup>. The results also showed that due to application of flood irrigation in these agro-ecosystems and also water elicited from subterranean sources by electrical pump, the inputs had the largest portion among the energy inputs to agro-ecosystems that this matter increased energy use in the unit area and also reduced energy efficiency and productivity.

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Key Figures for Production Control in Non-Ferrous Melting and Die-Casting Plants Based on the Assessment of the Operating State

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.871.176 ◽

2017 ◽

Vol 871 ◽

pp. 176-185 ◽

Cited By ~ 1

Author(s):

Wolfgang Schlüter ◽

Jörg Schmidt ◽

Matthias Henninger ◽

Jakob Krieg

Keyword(s):

Energy Efficiency ◽

Production Process ◽

Die Casting ◽

Liquid Aluminum ◽

Smart Services ◽

Running Time ◽

Supply Situation ◽

The Real ◽

Storage Distribution ◽

Efficiency And Productivity

The study focuses on the examination and development of simulation based measures to increase the energy efficiency and productivity in the non-ferrous melting and die-casting industries. The high energy consumption of gas-fueled melting furnaces is caused by production fluctuations in the foundry. Currently the control of the operating processes is decentralized and based on empirical process experience and inaccurate information of the operating state. The acquisition of the plant wide supply situation of the die casting machines with liquid aluminum is an essential condition for solving the problem of inefficient working melting furnaces. Their representation is grounded on specially defined key figures.In a first step the filling levels of the different liquid aluminum sources (melting furnaces) are considered as one unit as well as the filling levels of the different liquid aluminum sinks (die-casting machines). This assumption leads to the so called storage distribution key figure which describes the current supply situation of the die casting plants with liquid aluminum. This single key figure is able to assess the complex plant wide supply state. This key figure allows the real time evaluation of the operating state (production safety). Another important key figure is the residual running time of the die casting machines. Both key figures can be used for controlling the operating processes, too. A simulation is needed in order to analyze these operating processes because otherwise it would interfere with the real production process. The simulation of the complete material flow of the aluminum starts with its delivery in solid and liquid form, continues with the melting in furnaces and leads to the production process in the die casting machines. Energetic key figures such as the gas consumption and the specific melting rate of the melting operation can be determined by bidirectional coupling with a physically based energy model of the melting furnaces. The simulation model was validated by measured data obtained in an industrial plant.The storage distribution key figure and the residual running time key figure can be used in order to provide Smart Services to increase energy efficiency and productivity in specific operating states. Adjusting the cleaning times of the melting furnaces or controlling the fork lift trucks are potential examples. The results of initial simulations show the effects of different control measures based on these key figures. Smart Services in real operation can be implemented as an assistance system but for the implementation in real operation a central data processing is indispensable prerequisite.

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