ES130V master controller now on Energy Technology List

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ES130V master controller now on Energy Technology ListAtlas Copco reports that the energy-saving features of its ES130V master controller have enabled the unit to qualify for the Enhanced Capital Allowance scheme and be included in the Energy Technology List. The Enhanced Capital Allowance (ECA) scheme, administered in the UK by The Carbon Trust, enables businesses to claim 100 per cent first-year capital allowances on their spending on qualifying plant and machinery that meets published energy-saving criteria. The Energy Technology List (ETL) details the energy-saving technologies and products that qualify for the scheme - and the list now includes Atlas Copco's ES130V master controller.

The ES130 family of central controllers are suitable for use with a range of compressor sizes and compression technologies. The ES130V is specifically designed to intelligently control a mixture of fixed-speed and variable speed drive compressors. With unlimited control capacity for up to 30 machines and artificial intelligence processing, the ES130V anticipates system behaviour to ensure optimum energy efficiency by selecting the most suitable machines to use. Through constant monitoring, the ES130V ensures total system stability and cost savings. With the addition of a range of remote monitoring options, the ES130V can provide accurate and relevant information and trending of a number of system and energy-related measurements.

The ES130V is a wall-mounted system that is independent from the compressor installation and equipment power supply. It features flexible connectivity via a CAN network or digital I/O. Dual remote pressure measurement ensures increased accuracy, better system behaviour anticipation and stability. The large, graphical, colour touchscreen display provides an intuitive user interface for maximum convenience and instant graphical analysis of system behaviour and performance. User-defined priority selection of equipment can also be used to improve the match between expected demand and machine sequence, thereby further reducing energy consumption.

07 October 2010

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