# The Synergy of Renewable Energy and Desalination:
## 5 Energy Efficient Operation of Centrifugal Pumping System in Desalination
The prediction of centrifugal pumps' energy performance is crucial for the advancement of pump design and energy efficiency. However, the performance forecast of centrifugal pumps is always impacted by the intricate internal flow, especially when running at low flow rates. In [83], the authors propose a multi-condition performance prediction method for centrifugal pumps with variable speeds based on the data-fitting method. The prediction model is improved by automatically meeting the performance constraints, and the performance relationship is integrated into the particle swarm optimization algorithm. By adding performance restrictions, the performance under various operating situations is accurately predicted in comparison to the experimental data. The results show that the mean absolute relative errors (MAREs) for head, power, and efficiency are 0.85 percent, 1.53 percent, and 1.15 percent, respectively. In [84], the authors suggest a single-objective optimization design approach founded on the non-hierarchical response surface methodology (RSM) and the multi-island genetic algorithm (MIGA) to enhance the operation performance of the multi-stage double-suction centrifugal pump and lower the internal energy loss of the pump. Efficiency under design conditions was the optimization goal, and nine parameters, including the blade outlet width and blade wrap angle, were used as design variables. Using the Latin hypercube sampling method (LHS), 99 sets of valid data were retrieved out of 149 pieces of data, with the associated efficiency and head criteria set. Based on non-hierarchical RSM, a cross-validation study of the sieved data was conducted, and the efficiency was globally optimized. For the modeling of hydraulic transient phenomena that occur in pipe systems, the full set of centrifugal pump characteristics is essential. However, pump manufacturers usually only provide minimal information because of the work necessary to obtain these curves, especially when the pump is operating normally. In [85], the authors propose a machine learning model to predict full, complete Suter curves using a pump's particular speed with the known parts of the Suter curve in order to obtain the entirety of the characteristic curves based on the manufacturer's general performance curve. The Suter curves that are available from lab tests serve as the model's training data. In order to determine the most accurate forecast in terms of the root mean, the suggested machine learning model then integrates many regression model types. Even though modern pumping systems are dependable, unplanned malfunctions could endanger public safety or result in large financial losses. As a result, early defect diagnosis, detection, and predictive monitoring systems are highly sought after. The majority of earlier research on machine learning-based centrifugal pump failure detection relies on either simulations, synthetic data, or data from test rigs operating under carefully monitored laboratory circumstances. In [86], the authors worked with a specialized pump-engineering business to gather data from actual functioning pumps placed in several locations in an effort to identify centrifugal pump defects with the help of convolutional neural network (CNN) transfer learning. In [87], to forecast the outcomes of a reverse osmosis experimental setup using a FilmTec SW30 membrane, a feedforward ANN model was created. To train and evaluate the network, sixty-three experimental data sets were created. To forecast the water permeate rate, the ANN was fed three inputs: temperature, feed pressure, and salt content. The neural network was trained using the rapid Levenberg-Marquardt optimization approach. Neural networks for scheduling and forecasting wind speed were applied by the authors in [88]. To operate a three-unit RO system, a modular operation technique was proposed.