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Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are extensively utilized across various industries, including water supply, agriculture, and energy, where they consume significant amounts of electricity. As demands for energy efficiency and reduced operating costs increase, enhancing pump efficiency has become crucial. This study focuses on optimizing the pump impeller geometry, which plays a vital role in minimizing energy losses. A hydraulic and hydrodynamic model was developed, alongside a parametric study based on numerical simulations (CFD), to analyze the influence of geometric parameters—specifically the angles and shapes of the blade's inlet and outlet edges—on energy losses and hydraulic efficiency. The study utilized experimental data provided by the manufacturer for model verification. The results revealed that Ivanovsky's method displayed deviations in the blade width at the leading edge and trailing edge of 25% and 43%, respectively, while Spiridonov's method indicated deviations of 13% in the outer diameter D 2 and 27.5% in the blade width at the trailing edge. In contrast, the combined method proposed by the authors achieved high accuracy, with deviations under 9%. Additionally, parametric analysis identified two key parameters affecting the pump efficiency: the angle of the trailing edge and its shape. These findings underscore the necessity of optimizing the blade geometry to enhance the performance and energy efficiency of centrifugal pumps. [ABSTRACT FROM AUTHOR]

The sustainability of the food production achieved with the help of irrigation systems and the sustainability of their energy consumption are major challenges of the current century. Pumping systems currently account for approximately 30% of global electrical energy consumption. As electricity prices rise, there is a growing need for technological advancements to enhance energy efficiency and reduce consumption costs effectively. This study focuses on operating centrifugal pumps at variable frequency as an effective means of achieving this goal. Most centrifugal pump manufacturers/providers traditionally assume that pump efficiency remains constant across various operating frequencies, often equating the efficiency at various frequencies to that at the standard frequency (50/60 Hz). In contrast, this paper introduces a new formula for estimating pump efficiency, crucial to precise power consumption determination, particularly in variable-frequency scenarios. The formula parameters are identified by using experimental data acquired from an existing pumping system. The tests and results presented in this paper demonstrate that the proposed formula outperforms the formulas of the current industry standards in accuracy. Practically, the new relation assures enhanced accuracy in estimating pump efficiency and absorbed power, allowing for the design of a more precise model used for control systems synthesis required for operating centrifugal pumps at variable frequency. This research offers a new way of calculating pump efficiency, which could be very useful for industry practitioners seeking to optimize energy consumption in pumping systems. [ABSTRACT FROM AUTHOR]

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are widely used in various fields and have enormous energy-saving potential. In order to predict efficiency quickly and accurately, firstly, by establishing a hydraulic loss model for centrifugal pump impellers, the functional relationship between impeller hydraulic loss and hydraulic efficiency is constructed to establish the objective function. Secondly, calculate the main dimensions of the impeller using the velocity coefficient method and establish the objective function variables of the number of blades Z and outlet placement angle β2. And the velocity coefficient k0. Then, the method of mathematical statistics, namely response surface analysis, is used to solve the relationship between hydraulic efficiency and dependent variables within the range of variables, which plays the role of predicting hydraulic efficiency. Finally, the accuracy of the predictions is verified by numerical simulation. The results show that the hydraulic efficiency of a high specific speed centrifugal pump reaches its maximum at 1000m³/h operating conditions with a blade number of 3, a speed coefficient of 3.9 and an outlet angle of 30°. The study provides a new direction for the hydraulic design of high specific speed centrifugal pumps to achieve more accurate predictions of high specific speed centrifugal pump efficiency. [ABSTRACT FROM AUTHOR]

The present paper presents a multilayer hybrid model for sensorless measurement of pump operating status, with the objective of enabling safe and stable operations while reducing energy losses. The model takes easily measurable variables such as rotational frequency and valve opening as input features to predict the performance parameters of the centrifugal pump. By integrating just-in-time learning (JITL) with Gaussian process regression (GPR) and leveraging the unique probability features of GPR, a just-in-time-learning GPR (JGPR) is developed to extract valuable feature information. The JGPR sequentially predicts the flow rate, coefficient for dynamic head, and shaft power. The predicted values are extended to other input features, which can accurately capture the characteristics of the centrifugal pump and effectively replace the process of acquiring process parameters through sensors. Finally, the mechanism model is integrated into the multilayer JGPR model to calculate the performance parameters of centrifugal pump. The validation results indicate a strong agreement between predicted and experimental results, with predicted meeting performance parameters all engineering requirements. Compared to a single model, the multilayer hybrid model significantly improves the reliability of predictions, demonstrating the feasibility of using this approach to predict performance parameters. This research provides valuable insights into the measurement of sensorless pump operating states, enabling safe and efficient operation in complex conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods. [ABSTRACT FROM AUTHOR]

Turbulent flow, mainly originating from the rotor-stator interaction (RSI), is closely associated with the normal and safe operation of the centrifugal pump. In the current research, to clarify turbulent flow in the centrifugal pump with a vaned diffuser, the non-intrusive LDA (Laser Doppler Anemometry) system is applied to measure velocity pulsation signals at different regions when the pump operates at various flow rates. Time and frequency domain analysis methods are combined to investigate the velocity signals, and the velocity distribution around the volute tongue region is reconstructed from twenty measuring points. Results show that the velocity spectrum is characterized by the discrete components at the blade passing frequency and its higher harmonics, and it is caused by the RSI between the impeller and the diffuser. For the points in the volute spiral and diffusion sections, due to the significantly reduced RSI effect, the velocity spectrum shows an evident difference from comparison with the points between the impeller and diffuser, and the blade passing frequency is not always the dominant frequency. The comparison of velocity amplitudes and RMS* (root mean square of velocity) values at different points proves that the measuring position and flow rate affect velocity pulsations. As observed from velocity distribution reconstructed by LDA signals, high velocity regions are developed downstream of the diffuser channel for all the measured flow rates. [ABSTRACT FROM AUTHOR]

The fault monitoring and diagnosis of industrial machineries are very significant in Industry 4.0 revolution but are often complicated and labour intensive. The application of artificial intelligence (AI) techniques have now been an important part of condition monitoring of the mechanical and electrical machines because of its fast computation, higher accuracy, and robustness in performance, reducing the dependence on experienced personnel with expert knowledge. This paper presents a review of applications of AI-based fault diagnosis techniques that have had demonstrated success when applied to various industrial machineries. The important literature published in the last twenty years (i.e., 2000 to 2020) have been reviewed and added. In this work, first, a brief of various AI techniques such as artificial neural networks (ANN), deep learning (DL), fuzzy logic (FL), and support vector machine (SVM) are added. The literature on AI-based diagnostics used for various industrial machines, such as induction motor, bearing, gear, and centrifugal pump, are added and discussed in detail. The observation, research gap, and new ideas have been discussed, followed by a conclusion. [ABSTRACT FROM AUTHOR]

This paper describes the related research work in the field of fluid-induced vibration of centrifugal pumps conducted by many researchers. In recent years, all walks of life have put forward higher demands for the vibration performance of pumps which drives the investigation on the root cause of pump vibration and the development of guidelines for the design of low-vibration pumps. Fluid-induced excitation is the most important and significant source of pump vibration. Understanding its generation mechanism and dominant characteristics is important for developing low-vibration pump design methodology. This paper starts with the analysis of unsteady flow in the centrifugal pump and summarizes unsteady flow characteristics such as jet–wake structure, secondary flow, and rotational stall in the operating pump. Based on the understanding of the unsteady flow structure in the pump, the fluid-induced excitation mechanism and its characteristics based on the investigation of unsteady pressure pulsation and excitation forces in the pump are summarized. For the pump operating at nominal flow rate, the excitation at blade passing frequency (BPF) dominates and related suppression methods are classified and summarized to provide reference for the design of a low-vibration pump. [ABSTRACT FROM AUTHOR]

The simulation of cavitation flow is sensitive to the turbulence model and cavitation model. To improve the accuracy of numerical simulation, a centrifugal pump with specific speed of 56.5 is taken as the research object, and the Filter-Based Model (FBM) was used to correct the RNG turbulence model. Zwart-Gerber-Belamri (ZBG) cavitation model, Kunz model and Schnerr-Sauer model were employed for the simulation of cavitation flow in a centrifugal pump. According to the comparison, the results calculated by the ZGB model were 8% closer to the experimental results in cavitation predictions. The cavitation occurs around the impeller ring and balance holes firstly, and then the vapor develops and cover 80% of the blade suction side. By using Q-criterion, the distribution of the vortex core region under different cavitation conditions were investigated. The volume of high intensity vortex under severe cavitation condition is 30%∼50% higher than that under other conditions. Furthermore, the distribution of the radial force of the impeller corresponds to the number of blades, and the cavitation affects the resultant force direction of the impeller. With the intensification of cavitation, the radial force of impeller increases gradually, and the regularity of radial force direction will be disorder. [ABSTRACT FROM AUTHOR]

During the global energy crisis, it is essential to improve the energy efficiency of pumps by adjusting the pump's control strategy according to the operational states. However, monitoring the pump's operational states with the help of external sensors brings both additional costs and risks of failure. This study proposed an interpolation method based on PN curves (power–speed curves) containing information regarding motor shaft power, speed, and flow rate to achieve high accuracy in predicting the pump's flow rates without flow sensors. The impact factors on the accuracy of the estimation method were analyzed. Measurements were performed to validate the feasibility and robustness of the PN curve interpolation method and compared with the QP and back-propagation neural network (BPNN) methods. The results indicated that the PN curve interpolation method has lower errors than the other two prediction models. Moreover, the average absolute errors of the PN curve interpolation method in the project applications at 47.5 Hz, 42.5 Hz, 37.5 Hz, and 32.5 Hz are 0.1442 m3/h, 0.2047 m3/h, 0.2197 m3/h, and 0.1979 m3/h. Additionally, the average relative errors are 2.0816%, 3.2875%, 3.6981%, and 2.9419%. Hence, this method fully meets the needs of centrifugal pump monitoring and control. [ABSTRACT FROM AUTHOR]

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods. [ABSTRACT FROM AUTHOR]

Featured Application: The presented results can be used to assess the energy-saving potential of various topologies of multi-pump pumping stations. The energy efficiency of a multi-pump system consisting of two low-power (0.75 kW) pumps operating in parallel mode and a single-pump mechanism (1.5 kW) is compared in this study. For this purpose, mathematical models, experimental data, and data retrieved from the manuals provided by the pump manufacturers are used. The single-pump system is fed by a single variable speed drive. A multi-pump system running in parallel mode consists of two pumps. One of them is driven by an induction motor connected directly to the electrical grid and equipped with a throttle. Another pump is actuated by an induction motor fed by a variable speed drive. The flowrate of the liquid in the multi-pump is controlled with the help of speed variation and throttling. In the case of the single-pump system the conventional speed control method is applied during the analysis. For both pump system topologies, the daily and annual energy consumption is obtained. As a result of conducted calculations, it was shown that the multi-pump provides 29.8% savings in comparison to the single-pump system in the case of a typical flowrate profile. [ABSTRACT FROM AUTHOR]

Featured Application: The presented results can be used to evaluate the energy-saving potential and ecological impact of electric motors of various energy efficiency classes in various applications of electric drive. The high energy intensity of the modern industry and the threat of climate change determine the high urgency of increasing the energy efficiency of electric motors. In this paper, energy consumption, energy costs, payback periods, and CO2 emissions of 75 kW, 4 pole induction motors with direct grid supply in a fixed-speed pump unit are evaluated. Motors of the IE2, IE3, and IE4 efficiency classes according to IEC 60034-30-1 standard are compared in terms of life-time energy savings, payback period, and CO2 emissions. To carry out the analysis, polynomial interpolation of the data from the available manufacturer datasheets of the motors is used. It concluded that even though the initial investment cost of the IE4-motor is higher than that of IE3-motor, the IE4-motor is more profitable if more than 3 years of operation are considered and also provides significant reductions of CO2 emissions. The paper presents a calculation method of the aforementioned indicators which can be useful for companies, researchers, and engineers for quick assessment and selection of technical solutions. [ABSTRACT FROM AUTHOR]

Featured Application: The presented results can be used to assess the energy-saving potential of general-purpose and industrial electric motors of various types in various applications of electric drives. The high energy intensity of the modern industry determines the high urgency of increasing the energy efficiency of production processes. However, a big number of motor types of enhanced efficiency classes are available on the market. The motor users can be confused about the choice of the right motor solution for a certain application. In this paper, to help with this choice the energy efficiency indicators of various types of electric motors in a low-power pump unit with a constant rotation speed are studied. Moreover, not only power losses in the motor are considered, but also power losses in the cable and transformer, which are influenced by the power factor of the motor. Induction motors (IMs) and synchronous motors powered directly from the grid (direct-on-line synchronous motor with permanent magnet in the rotor, DOL PMSM; direct-on-line synchronous reluctance motor without permanent magnet, DOL SynRM) of IE2, IE3, and IE4 energy efficiency classes are compared. To carry out the analysis, polynomial interpolation of the available catalogue data and experimental data of the motors are used. The main criteria for comparing different motors in this work are the energy savings over the pump's life cycle and the payback period when replacing an IE2 motor with a motor of a higher energy efficiency class. The article shows that although the DOL PMSM has a lower motor efficiency than the DOL SynRM, it saves more energy due to its higher power factor, which reduces cable and transformer losses. It is also shown that, despite the highest initial cost, when taking into account cable and transformer losses, the payback period of DOL PMSM can be shorter than that of IE3 and IE4 induction motors. DOL SynRM has the shortest payback period in all considered cases, has no troublesome rare-earth permanent magnets, and can also be a valuable solution. [ABSTRACT FROM AUTHOR]

In this study, energy loss within a centrifugal pump is investigated by post-processing three-dimensional unsteady flow field through kinetic energy dissipation theory. The three-dimensional unsteady flow field is predicted by solving unsteady Reynolds-averaged Navier–Stokes equations. The kinetic energy dissipation consists of three parts: averaged kinetic energy dissipation, turbulent kinetic energy dissipation, and near-wall revised kinetic energy dissipation. The total value variations of three kinetic energy dissipations in the centrifugal pump with flowrate are investigated and compared. Results show that with the increase in flowrate, the total near-wall revised kinetic energy dissipation gradually increases, the total turbulent kinetic energy dissipation first gradually decreases and then gradually increases, and reaches the minimum value at the design flowrate. The total averaged kinetic energy dissipation is less than the total turbulent and the total near-wall revised kinetic energy dissipations, and the total near-wall revised kinetic energy dissipation is larger than the total turbulent kinetic energy dissipation when the flowrate is larger than 0.75 Qdes. The space variation of the near-wall revised kinetic energy dissipation with flowrate shows that large near-wall revised kinetic energy dissipation mainly occurs at the volute and transfers from the small cross-section casing to large cross-section casing and discharge pipe with the increase in flowrate. The space variations of the turbulent kinetic energy dissipation with time for three flowrates are also discussed. Results indicate that large turbulent kinetic energy dissipation near the volute tongue evidently changes with the rotation of the impeller, particularly in 0.5 Qdes. The large turbulent kinetic energy dissipation gradually expands to the pressure side of the blade when the volute tongue gradually approaches the middle of the impeller blade passage. The large turbulent kinetic energy dissipation transfers from the impeller inlet and outlet to the volute tongue and discharge pipe with the increase in flowrate. The findings of this study can serve as guide to improve the design of centrifugal pumps. [ABSTRACT FROM AUTHOR]

Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size. [ABSTRACT FROM AUTHOR]

Copyright of Electrical Engineering & Electromechanics is the property of National Technical University, Kharkiv Polytechnic Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The presented results can be used to assess the energy-saving potential of general purpose and industrial electric motors of various types in various applications of electric drives. The paper presents a comparative analysis of energy consumption by 2.2 kW electric motors of various types and energy efficiency classes in the electric drive of a pump unit with throttle control in a water supply system. Line-start permanent-magnet synchronous motors of the IE4 energy efficiency class and induction motors of the IE4 and IE3 energy efficiency classes of various manufacturers were considered (IE4 and IE3 are labels of energy efficiency classes of electric motors according to IEC 60034-30-1 standard). Energy consumption at a hydraulic load changing under a typical duty cycle was calculated based on the nameplate data of the pump and electric motors. The developed method shows that selecting an electric motor based on the IE energy efficiency class under the IEC 60034-30-1 standard (i.e., based on efficiency at a rated load) may not provide the minimum energy consumption of a variable flow pump unit over a typical duty cycle. In particular, the considered IE4 class line-start permanent-magnet synchronous motors do not provide significant advantages over IE4 class induction motors, and sometimes even over IE3 class induction motors when they are used in variable flow pump units. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work focuses on the modeling and simulation of constant pressure systems based on variable speed pumps, with the aim of studying and evaluating their performance from a multidisciplinary approach. Using the physical models of the Simscape library, from MATLAB/Simulink R2019b, two study cases are assembled consisting of: piping system, a hydropneumatic tank, centrifugal pumps with an induction motor, variable speed drives, and a control system. Case one is comprised of one pump at a fixed speed and another at variable speed, and case two with both pumps at variable speed. For the parameterization of the models, data from manufacturers and process requirements are used. The different stages of the control system are integrated and configured; these are constant V/f control, slip compensation, space vector modulation (SVM,) and pressure controller. The dynamic response of the system, power saving, transient current at startup, and harmonic distortion are evaluated. The results showed that both cases kept the pressure constant in the face of variable flow demand and smoothed out the current during startup. Case two saved more energy (between 28 and 49%) but generated more harmonic distortion. In addition, both cases have better performance compared with traditional fixed-speed technologies. [ABSTRACT FROM AUTHOR]

The paper discusses the use of the combined control for a system of two parallel pumps to increase its service life. Using the combined control, the pumping system is controlled together by change the speed, throttling, and bypass. The power consumption of the pumping system is calculated for three methods of flow control: with minimum energy consumption, with maximum reliability, and control with a trade-off between efficiency and reliability. In the case of control with maximum reliability, the energy consumption of the pumping system is higher than in the case of control with minimum energy consumption by 29.2%. In the case of the proposed trade-off control, which provides acceptable reliability, the power consumption is higher than with the minimum energy consumption control by only 7.3%. [ABSTRACT FROM AUTHOR]

Reliability, along with energy efficiency, is an important characteristic of pump units in various applications. In practical pump applications, it is important to strike a balance between reliability and energy efficiency. These indicators strongly depend on the applied control method of the pump unit. This study analyzes a trade-off method for regulating a system with three parallel pumps equipped with only one frequency converter (multi-pump single-drive system). A typical operating cycle of a pumping system with variable flow rate requirements is considered. The proposed trade-off method is compared with the traditional regulation, when a change in the operating point of the pump is achieved only by changing the rotation speed, and with the method for maximum reliability. It is shown that the proposed trade-off method makes it possible to ensure sufficient reliability of the multi-pump system operation without a significant increase in energy consumption. [ABSTRACT FROM AUTHOR]

The paper presents a comparative analysis of life-cycle energy consumption for three different types of 4 kW line-start motors used in a pump unit with throttling: the most widely used induction motor with IE3 efficiency class, line start permanent magnet synchronous motor with IE4 efficiency class and line start synchronous reluctance motor with IE4 efficiency class. The operating cycle for pump units with constant flow is considered for the above-mentioned types of motors taking into account not only the losses in the pump and motor, but also in the power supply cable. It is shown that the line start synchronous reluctance motor without magnets has the highest efficiency over the entire considered loading range. However, its power factor is lower than that of the synchronous motor with magnets and therefore it has more significant losses in power supply cable. Despite this disadvantage, the line-start reluctance motor is a good alternative to widespread induction motor since it allows saving of approximately 4000 euro more than the latter during the 20 years life cycle. It also provides similar savings in comparison to the permanent magnet synchronous motor, but unlike it, it does not have costly rare-earth materials in the rotor. [ABSTRACT FROM AUTHOR]