How Data Analytics and Digitalization are Reducing Operational Cost and Improving Production Efficiency
By Rene Akkermans, Regional Account Executive EMEA, Rockwell Automation
Process industries represent a significant share of the European industry in terms of energy consumption and environmental impact. Process optimization can lead to significant savings, both economic and environmental. Predictive modeling can prove effective when applied to the optimization of production processes. Predictive models are built using the data obtained from production processes. The operation data are key to business transformation and enable organizations to become a future-ready. However, the application of these techniques is not straightforward.
Rockwell’s Advanced Analytics and Model Predictive Control is an intelligent software layer on top of basic automation systems. This technology continuously drives your plant to achieve multiple business objectives including cost reductions, decreased emissions, consistent quality and production increase.
The patented parametric hybrid modelling technology combines first principles with empirical modelling techniques to provide the most accurate representation of complex, nonlinear processes. These models can be used to provide real-time and predicted measurements for process variables, reducing dependency on lab samples. The advanced multivariable model predictive control (MPC) algorithms provide excellent closed-loop control performance on even the most complex processes.
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