Optimizing processes more quickly 

If all available production data are captured, a previously unknown level of transparency can be achieved in industrial processes. Ultimately, this also increases availability, efficiency, and productivity. The cloud-based Control Performance Analytics service enables efficient analysis of control accuracy.  

Increasing productivity is a critical driver for staying competitive, especially for industries that add a great deal of value such as the pulp and paper industry. Production and process data need to be captured, filtered, and structured, allowing intelligent analysis to transform it into specific added value. 

Well-maintained process control systems support analytics of this kind, and with correspondingly configured process control loops contribute to a lower level of variances, optimal plant utilization, and greater process operability. Manual or automatic mechanisms can be used comprehensively for monitoring control accuracy. However, depending on corporate philosophy, pulp and paper companies have differing perceptions of whether it is worth investing in reliable control accuracy monitoring and if so, how much to invest. 

A key to increased productivity

 

In the process industry, the lifecycle of process control loops can be as long as 30 years. Continuous monitoring of control accuracy in ongoing plant operation is crucial in order to keep the performance of all process control loops of a given plant transparent. In this way specific maintenance, service, and adjustment measures can be planned in detail and implemented in a focused manner. In addition to established software tools for monitoring control quality which can be installed locally in a plant, there is also a new cloud-based service available: Control Performance Analytics (CPA).  

Reducing operating costs

 

Thus, assuming that about half of process control loops are not optimally adjusted, or up to 200 process control loops, such a plant would be operating extremely inefficiently, and this would have a direct impact on operating costs.

 

About 30% of all process control loops typically run in manual mode, and about 25% are still operated using the original parameters from commissioning. Each controller requires approximately 10 to 14 expert hours – in total over one and a half person-years – in order to initially identify the appropriate process control loop, investigate it, and then optimize its control performance. 

Automatically increasing transparency

 

CPA eliminates the time-consuming process of manual identification. The automatically calculated optimization recommendations can be used and thus reduce time needed for implementation of these measures alone to only two or four hours. CPA performs these analyses automatically and prioritizes the process control loops requiring assessment by a control technician. CPA calculates the parameters and recommends those requiring optimization. The control technician can then concentrate on the process control loops identified and begin with the most important one. After optimization, control accuracy is continuously captured in order to detect deviations in a timely manner. 

Long-term process optimization

 

Process data is supplemented by CPA. Automatic status detection and a KPI calculation for different control states enable the required continuous transparency to be achieved. CPA makes it possible to optimize processes in the long term and support fine adjustments in a way that makes sense. On a secure web portal the results for all levels – from plantmanager to machine operator – are made available securely over a long period of time. The plant operator does not need to build up any resources or acquire specific expertise, and it is also possible to claim this “managed service” as a business expense. This saves on costs while also creating the basis for decisions based on facts.

 

In the “pay-per-use model,” the operator has a high degree of flexibility regarding the number of controllers to be analyzed over a certain period of time with what are known as “controller months.” For example, different controllers can be controlled in different numbers per month over variable time periods.

 

In fact, small control deviations not only improve the quality of the product but also make it possible to operate closer to production limits. In this way production tonnage can be increased, energy consumption reduced, and savings in raw materials achieved. 

Picture credits: Siemens AG

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