Background
In our customer production activities, energy and raw materials are used to manufacture products at a specified level of quality. Also, within these activities, various energy-saving efforts have been carried out and highly efficient equipment have been created because energy consumption and yield are directly linked to profits.
However, with the development of mass data processing technology and industrial IoT, the effective use of operation data that had been dispersed until now, coupled with the ability to link between production processes, has paved the way for optimizing entire factories and regions.
Technology
To realize optimal operation that includes not only manufacturing costs but also quality and environmental standards, Yokogawa has developed two technologies—data-driven modeling for optimization and high-speed multi period optimizer—and offers the sale of systems and consulting services that seamlessly put the PDCA cycle into action from diagnosing the potential for performance improvement in a customer’s operational improvements, implanting actual measures, to verification of performance improvements. These are used as communication tools with our customers with the aim of discovering problems in operations and creating value.
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Swift and accurate modeling
To optimize a factory as a whole, Yokogawa has developed data-driven modeling for optimization technology because of the necessity of having to model a diverse number of production equipment. In the case of an energy supply plant, Yokogawa was able to produce a plant model in one fifth of the time, while maintaining the same level of precision compared with manual modeling.
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Field tests
Yokogawa is currently demonstrating proof of concept by carrying out actual operations at a customer's plant and verifying the performance improvement effect at multiple sites.
Vision for the Future
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Expanding the range of target for optimization
Energy supply plants, also called BTG plants, are relatively limited in the major equipment they have such as boilers, turbines, and generators; however, because production plants consume energy with their diverse equipment, their modeling has not progressed. By taking advantage of the feature of data-driven modeling technology that can automatically model the characteristics of production equipment, Yokogawa is verifying the feasibility of modeling production equipment at an actual site, aiming to create value through the optimal operation across the factory.
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Sustaining the optimization effect
The plant model becomes deviated after it is created as the aging of equipment and fluctuations in the external environment cause deviations at the actual plant. By implementing the PDCA cycle with customers, including validating the optimization effect and re-tuning the model, Yokogawa aims to provide services that will sustain the optimization effect.
