Increasing energy efficiency by means of adaptive operational management

Adaptive pump controls are suitable for cases where several degrees of freedom exist in the operating possibilities. This is primarily the case for pumps arranged in parallel or in series or for filling processes of fluid storage tanks or pressure containers. In turn, combinations of these system topologies have a further degree of freedom for the operation of pumps. Additional degrees of freedom in optimization include time specifications and changing fluid properties. If self-tuning control systems are now used for automation, online optimization during operation allows the control parameters to be adapted directly to the real plant behaviour, thus generating truly optimal operating modes.

Figure 1: Filling possibilities of fluid storage tanks

Energy-saving potential

In order to show the energy-saving potential of centrifugal pumps by means of improved operational management, different operation modes for filling processes can be considered. Filling processes for storage tanks are used, for example, in water supply systems, in the petrochemical industry and also in the food industry. In principle, a distinction can be made between three different possibilities (Figure 1): filling at constant speed (NC), with constant volume flow (QC), or with a speed adapted to the filling level (LC).

If different system parameters such as dynamic pressure loss, the static delivery head or the filling volume are considered, energy savings of up to 70% per filling process can be achieved. Figure 2 gives an overview of the savings potential of various storage systems.

Figure 2: Overview of theoretical savings potential thanks to optimized operational management

Tuning of a pump system during operation

One possibility for tuning pumps during operation is dynamic optimization. An algorithm specially developed for pumps can coordinate several pumps in a system, whereas system parameters are subject to constant changes. Figure 3 shows the tuning process of two pumps operating in parallel. This tuning process was carried out on the pump test bench at the NCT. If the setpoint value (volume flow) is maintained at the same time, the PLC adapts the speed settings to the energetic optimum.

This can also be used to detect the switching on and off of pumps (Figure 4). Here, the varying setpoint value is assumed for the reference value, and the speed specifications can be set as a function of this. The dynamic optimization algorithm refines itself and adapts the speeds (red and blue) to the optimum performance (comparison: grey speed curve is optimal).

Figure 3: Tuning course of two pumps to minimize the absorbed power
Figure 4: Learned speed course with switching point for the pumps

To design tuning processes in a useful way, pumps must be located in a suitable working area during operation, since otherwise seals and bearings may be damaged or cavitation may occur. Figure 5 shows the recommended range for continuous operation in order to avoid potential damage to the pump. This must be taken into account during the tuning process.

Figure 5: Recommended working range of a centrifugal pump


Modelling of pump systems

Simulation programs are suitable for enabling observation of a large number of system topologies. To verify the modelled systems, as shown in Figure 6, experiments must be run on pump test benches and compared with simulation results. The modelling within the development work takes place using MATLAB/Simulink, since a one-dimensional flow model is sufficiently accurate and regulatory structures and optimisation algorithms can easily be implemented in the program.

Figure 6: Simulink model of a pump system for a storage facility

This acceleration behaviour of a one-dimensional fluid column is shown in Figure 7. A water column approx. 75 m in length was accelerated by a constant pump speed. In the upper speed range, the model to be verified in Simulink corresponds very well to the real behaviour. If the operating point of the system moves further away from the nominal operating range, deviations of the affinity laws occur. This can also be seen in the acceleration behaviour in the lower speed range. This effect can be taken into account by an adjusted modelling. At the start of the measurement recordings, strong fluctuations can be observed. The reason for this is the installed measurement technology (determination of volume flow by means of differential pressure). The peaks that occur depict the pressure propagation in the system, along with the flow speed. Using this mathematical model, different systems can be simulated on the computer and optimal operating points can be found. With the aid of these optimal operating points, self-optimizing control algorithms can thus be drawn up for different applications.

Figure 7: Acceleration behaviour of a liquid column at different speeds; experiment and models



Thomas Hieninger, Ronald Schmidt-Vollus. Energy management system for the efficient operation of water storage tanks with centrifugal pumps. ProcessNet annual conference 2016. Article ID: 5150.

Thomas Hieninger, Ronald Schmidt-Vollus. Markus Norden. Process optimisation through auto-tuning of pump systems – an efficiency boosting opportunity. Process Technology and Components 2017.

Thomas Hieninger, Ronald Schmidt-Vollus. Markus Norden. Process optimisation through auto-tuning of pump systems – an efficiency boosting opportunity. Process Technology and Components 2017.

Thomas Hieninger, Ronald Schmidt-Vollus. Auto-tuning pump operation mode for fluid storages to increase energy efficiency. CCWI 2017.

Thomas Hieninger, Ronald Schmidt-Vollus. Energy-efficient operation of water storage tanks by means of online optimisation. Industriewassertage 2017.

Thomas Hieninger. Energetic evaluation for different control strategies in centrifugal pump-driven storages, Achema 2018

Thomas Hieninger, Ronald Schmidt-Vollus. On-line self-tuning for centrifugal pumps driven in parallel mode using dynamic optimization. Mechatronika 2018 (pending)