Product News: Hamilton Expands Products and Applications for Process Measurement Analytics

22 Oct 2013

Hamilton Company announces the availability of its new process analytics catalog offering the latest in intelligent and robust sensor technologies. For more than 50 years Hamilton has been a leader in monitoring progressive solutions. Manufacturing sensors that are used whenever accurate process analytics are required to ensure high-quality results including pharmaceutical and biotherapeutic production, water treatment, food processing, refineries, breweries and wineries.

During the past three years, Hamilton has added more than 50 new products to its portfolio, introducing innovative sensor technologies suitable for a wide range of applications. New product highlights include:
 • Smart Arc™ sensors with built-in 4-20 mA and digital data transmission
 • A complete line of Redox/ORP electrodes
 • Robust, time- and cost-saving optical DO electrodes
 • Built-for-purpose Beverly portable dissolved oxygen (DO) system for the beverage industries
 • Wireless pH, DO and conductivity transmitters
 • Hygienic, regulatory-compliant Retractex® retractable housings

One of the newest products, the Beverly, is a portable system for measuring dissolved oxygen during beer fermentation, filtration and filling to ensure purity and superior taste. The Beverly was designed by Hamilton engineers in collaboration with master brewers and utilizes the Arc microprocessor and Visiferm™DO B sensor for highly accurate, rugged and affordable at-line and laboratory measurements. With its ability to reduce downtime by eliminating offline calibration and by using the intuitive user interface, the Beverly system is one of the highest performing and least expensive portable DO unit on the market.

“Hamilton has added many new Arc intelligent sensors with built-in microprocessors that bypass the older hard-wired transmitter systems and provide higher quality data reportable in real time,” said Jahir Kololli, Arc sensor product manager. “In the past, data was monitored only off-line, so optimization required iterative cycles which could put operations at risk.”