Abstract
Due to their lower energy consumption, permanent magnet synchronous motor (PMSM) submersible pumps have been introduced in ground water well field operation. Besides direct savings of energy, the introduction of PMSM pumps together with the required variable frequency converters offers new operational scenarios to meet the seasonally varying water demand. In this work, potential energy savings of variable speed submersible pumps were investigated. A ground water well field consisting of 13 wells and their transport pipes was modeled in the hydraulic modeling software EPANET 2.0. Using MS visual basic for applications, EPANET was coupled with MS Excel and a genetic algorithm to identify the most energy efficient combination of pump speeds. For the simulated well field, the total specific energy demand required for pumping was significantly lower in partial load operation as compared to nominal pump speed operation. For low and moderate flow scenarios, energy savings in the range of 20% compared to nominal speed operation can be achieved. These findings were confirmed by a monitoring campaign in the well field. Combining hydraulic simulation and optimization using genetic algorithms, the best efficiency scenario for operation of ground water well fields can be found.
Highlights
In their efforts to cut greenhouse gas emissions and reduce their fossil energy demand, municipalities often focus on public drinking and wastewater utilities which make up a significant share of municipal energy usage
Whereas efficiency gains by recent permanent magnet synchronous motor (PMSM) technology in individual pumps were investigated in another study [6], this study focused on the optimal operation of PMSM pumps in a well field and its benefits
Simulation of Different Operational Scenarios Using Variable Speed Submersible Pumps in a Well Field In Figure 4, the resulting total well field flow which can be realized by the 13 pumps in the well the resulting total many well field floware which can be on realized by the 13speed
Summary
In their efforts to cut greenhouse gas emissions and reduce their fossil energy demand, municipalities often focus on public drinking and wastewater utilities which make up a significant share of municipal energy usage. Genetics algorithms [10] can be considered as computer programs that mimic the process of biological evolution to solve optimization problems They contain potential solutions in a chromosome-like data structure and apply recombination and mutation operators to preserve critical information. They are considered as function optimizers, the range of problems in which they can be implemented is very broad (e.g., optimization of routes/distribution, resources and processes).
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