Real Pump Research Articles

Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

Investigation of two-phase flow distribution in the vertical annular distribution header of a variable refrigerant flow heat pump system

The characteristics of two-phase flow distribution from a vertically installed annular refrigerant distribution header to multiple branch ports are investigated, with a specific focus on simulating a real variable refrigerant flow heat pump (VRF HP) system. A test section is fabricated to replicate an actual distribution header for empirical investigation, utilizing an air–water mixture as the working fluid. A series of experiments are conducted to examine distribution characteristics, involving variations in the location of the main inlet port, operational mode, total mass flow rate, and void fraction. The experimental results indicate that the two-phase mixture exhibits a relatively uniform liquid phase distribution to the branch ports when the main inlet port is positioned at the bottom side. However, maldistribution to branch ports is intensified as the inlet void fraction increases and the flow rate of the two-phase mixture decreases. Furthermore, a series of computational fluid dynamics analyses is performed to investigate the distribution characteristics of R410A, the actual working fluid utilized in VRF HP systems. These analyses are conducted under the operational conditions typical of VRF HP systems. Despite identical Reynolds numbers for the two-phase R410A flow and air–water mixture flow, the former exhibits a notably uneven distribution to branch ports owing to the distinct thermophysical properties, particularly density and viscosity. The findings from this investigation enhance the comprehension of two-phase flow distribution characteristics in a vertically installed refrigerant distribution header under real VRF HP system conditions, offering valuable insights into mitigating the maldistribution of refrigerant flow to branch ports.

Model predictive controls for residential buildings with heat pumps: Experimentally validated archetypes to simplify the large-scale application

Exploiting the energy flexibility resulting from thermal loads management in buildings is one of the most promising solutions to contribute to the energy transition targets. To offer energy flexibility to the grid, the building should meet the requirements: (i) it needs electrically powered generation (e.g., through Heat Pumps) and (ii) advanced control techniques (e.g., Model Predictive Controls) must be implemented. In recent years, the scientific community has produced many studies demonstrating the potential of Model Predictive Controls combined with Heat Pumps to exploit energy flexibility in buildings. However, a large-scale deployment of such control techniques is still far off, both because of the not yet widespread use of Heat Pumps and the computational challenges involved in implementing them. The aim of this study is to contribute to the deployment of advanced control techniques for Heat Pumps systems in buildings by simplifying their implementation. At this aim, validated archetypes of Model Predictive Controls for Heat Pumps in residential buildings are proposed. The availability of archetypes can greatly facilitate the practical application of Model Predictive Control. In fact, they are adaptable to the characteristics of different buildings and Heat Pump installations and their structure is designed to have an acceptable trade-off between calculation time and prediction reliability. The archetypes cover three different types of heating systems: low temperature radiators without (i) and with (ii) integrated heat storage devices and (iii) underfloor heating system. All archetypes are applied to a real Heat Pump and validated through experimental campaigns. During the experimental test all the archetypes proved to be effective in controlling the real system and the results showed good reliability of the prediction model in the control (for all archetypes a Root Mean Square Error lower than 0.44 °C was obtained) and an optimizer success rate in Model Predictive Controls greater than 91 %. The archetypes proposed are provided as open-source tools that can be reused for similar cases to facilitate Model Predictive Controls implementation in heat pump systems.

A hybrid deep learning model towards fault diagnosis of drilling pump

This paper proposes a novel method namely WaveletKernelNet-Convolutional Block Attention Module-BiLSTM for intelligent fault diagnosis of drilling pumps. Initially, the random forest method is applied to determine the target signals that can reflect the fault characteristics of drilling pumps. Accordingly, the WaveletKernelNet-Convolutional Block Attention Module Net is constructed for noise reduction and fault feature extraction based on signals. The Convolutional Block Attention Module embedded in WaveletKernelNet-CBAM adjusts the weight and enhances the feature representation of channel and spatial dimension. Finally, the Bidirectional Long-Short Term Memory concept is introduced to enhance the ability of the model to process time series data. Upon constructing the network, a Bayesian optimization algorithm is utilized to ascertain and fine-tune the ideal hyperparameters, thereby ensuring the network reaches its optimal performance level. With the hybrid deep learning model presented, an accurate fault diagnosis of a real five-cylinder drilling pump is carried out and the results confirmed its applicability and reliability. Two sets of comparative experiments validated the superiority of the proposed method. Additionally, the generalizability of the model is verified through domain adaptation experiments. The proposed method contributes to the safe production of the oil and gas sector by providing accurate and robust fault diagnosis of industrial equipment.

Optimizing Method for Photovoltaic Water-Pumping Systems under Partial Shading and Changing Pump Head

Photovoltaic systems for pumping water, based on direct current powered motor pumps, have great application in small rural regions without electrical networks. In addition, these systems provide environmental benefits by replacing fossil fuels. However, these systems reduce their performance due to partial shading, which is magnified by the internal mismatch of the PV modules. This work proposes an intelligent, low-cost, and automatic method to mitigate these effects through the electrical reconfiguration of the PV array. Unlike other reported techniques, this method considers the pump head variations. For that, the global voltage and current supplied by the PV array to the motor pump subsystem are introduced to an artificial neural network and to a third-order equation, which locates the shaded PV module and detects the pump head, respectively. A connection control implements the optimal electrical rearrangement. The selection is based on the identified partial shading pattern and pump head. Finally, the switching matrix modifies the electrical connections between the PV modules on the PV array without changing the interconnection scheme, PV array dimension, or physical location of the PVMs. The proposed approach was implemented in a real PV water pumping system. Low-cost and commercial electronic devices were used. The experimental results show that the output power of the PV array increased by 8.43%, which maintains a more stable level of water extraction and, therefore, a constant flow level.

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.

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.

Improving and evaluating the performance of a real photovoltaic pumping system for agricultural irrigation purposes in a desert environment at Ghardaia, Algeria

Algeria has one of the highest solar resources in the world. The availability of this significant solar energy can make photovoltaic (PV) water pumping applications a very attractive solution for many uses, including irrigation of agricultural areas, village water supply and domestic uses. The cost of water pumped by a PV pumping system is directly linked to the efficiency and reliability of the different elements constituting the system and also to the solar irradiation available at the installation site. Consequently, it is necessary to improve the reliability and efficiency of the PV generator in order to extract the maximum possible power at all times in order to achieve the most reliable and economic operation. On a real well of 25 m height in desert and semi-arid climate at the Sebseb site, Ghardaïa, Algeria, a typical PV pumping system for irrigation purposes is studied and optimized using two conventionels methods, a P&O method and a incremental method.These methods are essentially based on the assessment of water needs to irrigate 70 palm trees, the data on solar irradiation and the average temperature for the studied site (Sebseb, Ghardaia).

The dynamics of a bicycle on a pump track – first results on modeling and optimal control

Abstract We investigate the dynamics of a bicycle on an uneven mountain bike track split into straight sections with small jumps (kickers) and banked corners. A basic model of the interaction of bicycle and rider proposed and used to derive equations of motion, which capture the possibilities to accelerate the bicycle without pedaling. Since this is a first approach to the problem, only corners connected by straight lines are considered to compute optimal riding strategies. The simulation is validated with experimental data obtained on a real pump track. It is demonstrated that the model effectively captures the longitudinal bike acceleration resulting from the relative vertical motion between the rider’s upper body and the bicycle. Our numerical results are in good analogy with real rider’s actions on similar tracks.

Nonlinear System identification and Predictive Control for Waste Heat Recovery with Heat Pumps

The utilization of low-temperature waste heat, particularly from electrolyzers, in district heating networks via heat pumps presents a promising approach to accelerate the decarbonization of the heat sector. However, managing the electrolyzer’s specific temperature requirements and dynamic waste heat output, while simultaneously meeting the district heating network’s variable temperature demands, requires the implementation of an advanced control system for the heat pump cycle. For this, model predictive control is a promising approach since it not only ensures the satisfaction of constraints, but also facilitates a direct optimization of the heat pump’s operational efficiency. Nevertheless, model predictive control requires a dynamic heat pump model. In this context, first-principles models are often used. However, they are very complex and difficult to parameterize for real heat pumps. Therefore, in the present paper, a data-based system identification is carried out to obtain a reduced-order heat pump model from a high-fidelity first-principles simulation model. Based on the identified model, a predictive controller is implemented. The effectiveness of the obtained controller for operating the first-principles model is demonstrated in a numerical case study.

An investigative study on different magnetic ionic liquids to be used as a lubricant and sealant in a vacuum pump

The aim of this work is to explore the possibility of using a magnetic ionic liquid as both a lubricant and sealant in a vacuum pump. The current lubricant in use is due to become obsolete due to incoming legislation on fluorinated compounds. The long-term goal is to use a magnetic field to hold the novel lubricant in place to create a better seal and thus reduce pressure more effectively whilst also lubricating the necessary mechanical parts. This paper explores physical properties (contact angle, viscosity, coefficient of friction (COF) and magnetism) and chemical properties (thermal stability and chemical stability as deduced by X-ray photoelectron spectroscopy (XPS)) of three ionic liquids based on imidazolium cations with varying length of alkyl side chain and a tetrachloroferrate anion along with two control lubricants which are used currently in the field and based upon perfluoropolyether (PFPE). The work shows that the three magnetic ionic liquids possess a lower COF value than the two control lubricants when measured using a Bruker UMT TriboLab suggesting they may perform better in the real life vacuum pump. Upon analysis of the three ionic liquid samples after tribological testing using XPS, it was shown that the ionic liquid samples displayed minor differences in spectra suggesting good stability with no obvious decomposition or degradation of the sample.

Sizing of on-grid photovoltaic systems for water pumping in irrigation communities

This paper describes a technical-economical analysis to achieve the most appropriate sizing of grid-connected photovoltaic systems for water pumping in irrigation communities. The profitability of different tracking systems are analysed based on the price of the electrical energy consumed from the network. The case study of a real pumping system of an irrigation community located in Zaragoza (Spain) is presented, which supplies a geographical area of 2000 Ha, with six 630kW pumps and eight 110kW units.

Development of a CNN-based fault detection system for a real water injection centrifugal pump

Large-sized centrifugal pumps play a major role in produced water injection systems in oil and gas production. Monitoring this equipment operation is vital to guarantee its efficiency and to reduce the occurrence of unplanned downtimes. The main goal of this work was to develop a fault detection system based on artificial intelligence (AI) algorithms for a water injection centrifugal pump located at an oil and gas company’s offshore platform. The convolutional neural network (CNN) was the main algorithm investigated in this work. However, other machine learning techniques (i.e., support vector machines, random forest, and multilayer perceptrons) were used to compare against the results achieved by the CNN. A data-driven methodology was proposed for the stages of exploratory data analysis (EDA), data labeling, automatic hyperparameter optimization, training and testing of the chosen models. The results showed that the proposed methodology was effective and applicable. The CNN and the support vector machine (SVM) models presented interesting performances. The CNN model returned 93.7% precision and 59.8% recall, whereas the SVM model returned 84.6% precision and 66.7% recall. The challenges related to the use of a real dataset were also discussed, emphasizing the data labeling step. Applying the k-means clustering technique proved to be useful for labeling the data instances.

IMPROVEMENT OF MOTHER'S BREAST MILK PRODUCTION THROUGH PAPAYA FRUIT DESSERT

Breast milk (ASI) is the primary source of nutrition for infants as it can fulfill all their needs. However, the current rate of exclusive breastfeeding is still low. In Kutai Kartanegara Regency, the coverage of exclusive breastfeeding is only 47.5%. One of the reasons is insufficient breast milk production. Various efforts have been made to enhance breast milk production, such as providing papaya fruit dessert, as it contains lactagogue compounds that have the potential to stimulate hormones like oxytocin and prolactin. Compounds such as alkaloids, polyphenols, steroids, flavonoids, and other substances are believed to be most effective in increasing and facilitating breast milk production. To analyze the influence of papaya fruit dessert consumption on the enhancement of breast milk production among postpartum mothers in the working area of Sungai Merdeka Community Health Center in 2023. This study employs a quasi-experimental design with a "Pretest-Posttest, Control Group Design." The population includes all postpartum mothers who have given birth normally in the working area of Sungai Merdeka Community Health Center, with an average of 30 deliveries per month. The sample size was determined using the minimal sample size formula by Lemeshow, resulting in 26 participants. The measurement tools consist of observation sheets and a real bubee electric breast pump. Data analysis involves an independent t-test. There is an influence of papaya fruit dessert consumption on the enhancement of breast milk production among postpartum mothers in the working area of Sungai Merdeka Community Health Center, with a p-value of 0.000. The breast milk production in the group provided with papaya fruit dessert was significantly higher, with a difference of 39,231 ml compared to the group without papaya fruit dessert. The presence of compounds like alkaloids, polyphenols, steroids, and flavonoids in papaya fruit can enhance breast milk production.

Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

In surge protection, low-head profiles are deemed a challenge in pump failure events since they are prone to severe negative pressure surges that require an uneconomical surge vessel volume. A hybrid surge vessel with a dipping tube can provide required protection with reasonable economic volume. This work presents novel analyses for the hybrid surge vessel and develops a simple model for its optimum sizing using a stochastic numerical approach coupled with machine learning. Practical ranges for correct sizing of vessel components, such as ventilation tube, inlet/outlet air valves, and compression chamber, are presented for optimal protection and performance. The water hammer equations are iteratively solved using the hybrid surge vessel’s revised boundary conditions within the method of characteristics numerical framework to generate 2000 cases representing real pump failures on low-head pipelines. Genetic programming is utilized to develop simple relations for prediction of the hybrid vessel initial and expanded air volumes in addition to the compression chamber volume. Moreover, the developed model presented a classification index for low-head pipelines on which the hybrid vessel would be most economical. The developed model yielded good prediction error statistics. The developed model proves to be more accurate and easier to use than the classical design charts for the low-head pumping mains. The model clearly showed the relation between various hydraulic and pipe parameters, with pipe diameter and static head as the most influencing parameters on compression chamber volume and expanded air volume. The developed model, together with the classification indices, can be used for preliminary surge protection sizing for low-head pipelines.

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environement, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. . The results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

Rancang Bangun Prototype Sistem Monitoring Tandon Otomatis Menggunakan Mikrokontroller ESP32 Berbasis Internet Of Things

Filling reservoir water automatically using a pressure switch and radar is still not effective, because it cannot know how much water is in the reservoir in real time and how much water comes out of the reservoir. In addition, there is a problem with drilled wells where the source of water is not always available, when the well runs out the water pump will run continuously and can cause damage to the water pump. Therefore this study aims to make reservoir water filling automatic, maintain water availability in reservoirs, monitor water in reservoirs in real time, remote control and protect water pumps when well water supplies run out. This research was made using the ESP32 microcontroller, water flow sensor, water switch sensor and also utilizing the HC-SR04 ultrasonic sensor. This research produces a tool that can work well for monitoring how much water is in the reservoir, turning off or on the pump automatically based on the water level in the reservoir, monitoring how much water discharge comes out of the reservoir and monitoring whether or not there is input of water into the reservoir so that the water pump does not lights up continuously when the well or reservoir water runs out to prevent damage to the water pump. All of these systems are controlled by the ESP32 microcontroller which is connected to the internet of things and Android devices so that they can be monitored in real time.

Real-Time Dynamic Performance Enhancement for Solar-Powered Pumping Systems Using PI-Based MPPT Techniques

Abstract The integration of solar-powered pumping systems (SPPS) into agriculture and potable and wastewater sectors becomes mandatory to provide water in remote regions. The broad use of SPPS with classical maximum power point tracking controllers (MPPTCs) showed moderated voltage and power response deterioration. Therefore, the necessity for overcoming these performance degradations and pre-testing of MPPTCs is not an option for the proper operation of such systems. This paper presents a new simple, cost-effective real-time hardware-in-the-loop (RT-HIL) framework to enhance the dynamic performance of SPPS. To accomplish this study, a real pumping station was modeled and equipped with MPPTCs through matlab/simulink. Besides, a practical SPPS was implemented to evaluate the effectiveness of the proposed RT-HIL on system performance. The practical SPPS includes designing a DC-DC buck converter circuit equipped with metaheuristic optimization-based real-time MPPTCs. The tuned PI/FOPI-based MPPTCs are adopted in this work to gain the maximum power from the PV generator under measured real environmental conditions. The proposed real-time MPPTCs techniques are perturb and observe, and incremental conductance (IC) with I, PI, and fractional-order PI (FOPI) controllers. The simulation and the experimental results prove the superiority of the developed real-time FOPI-based MPPTCs in enhancing the system performance in terms of the gained power, module output current, pump flowrate, and pump efficiency. The paper’s novelty lies behind the relatively low-cost real-time execution of PI/FOPI-based MPPT techniques on SPPS. This work was simulated using matlab/simulink in conjunction with Arduino-based RT-HIL and the experimental validation was implemented at the National Water Research Center (NWRC) in Egypt.

Complements to Level 2 surf-riding/broaching vulnerability criterion regarding waterjet propelled ships and wave surge force correction

The Second Generation Intact Stability Criteria (SGISC) has officially entered the trial period for further validations and to collect feedback based on applications. This study focuses on the Level 2 surf-riding/broaching vulnerability criterion and proposes complements to the assessment procedures regarding two important issues. One is that the current procedure is only applicable to ships using propellers. However, waterjet propelled ships are common nowadays and they may be vulnerable to this stability failure mode as well. The other issue is that there lacks a both reliable and practical method for the wave surge force correction. The current procedure uses the Froude-Krylov (FK) component of the first order wave surge force as the input for the assessment, which significantly over-estimates the probability of surf-riding occurrence.Through the introduction of the thrust coefficient KT and the advance coefficient J for waterjet systems, the first issue is addressed by model the waterjet thrust based on the KT ∼ J relationship just as that of propellers. The validity of such treatment is first demonstrated through the actual data of a real pump, while the rationale behind the universality of such modelling is analyzed based on dimensional analysis. The second issue is addressed through the proposal of a practical method to estimate the wave surge force correction factor based on the ratio between the first order wave surge force and its FK component. Sample ship calculations are performed to demonstrate the reliability of the proposed method. Finally, the complete Level 2 surf-riding/broaching vulnerability assessment for a waterjet propelled ship is performed for demonstration. This study contributes to the improvement of SGISC, making it applicable to a wider range of ship types with higher accuracy.

Homogenization of spatially variable hydraulic conductivity in the presence of a geotechnical structure

This paper proposes a novel homogenization method for spatially variable hydraulic conductivity (k). The main novelty is that it can characterize the effective hydraulic conductivity “felt” by a geotechnical structure for steady state flow problems, whereas the prevailing homogenization method does not address the existence of a geotechnical structure or complex boundary conditions. An engineer can apply this relatively simple method to determine a characteristic value that is consistent with both physics and spatial variability. The proposed method is based on a weighted geometric average (GA) with weight proportional to the degree of mobilization, which can be approximately determined at a modest cost using a single deterministic finite element analysis. Monte Carlo simulation of a flow problem in a spatially variable medium requires costly repeated runs of a random finite element analysis. Based on the results from numerical examples, it is shown that the proposed method is superior to the prevailing homogenization method based on uniform GA when a geotechnical structure or complex flow boundaries are present. The proposed method reduces to the prevailing method for an example where there is no geotechnical structure. The applicability of the proposed method is verified by a real pumping test case study.

A single stage spool valve for the pressure compensator of a variable displacement pump: design, dynamic simulation and comparative study with a real pump

The study is focused on the design of a simplified spool valve to be incorporated in the pressure compensator of a variable displacement axial piston pump in order to perform a comparative study with a commercial pump having a two-stage spool valve in its compensator. The design involves the evaluation of the spool size and selection of spring from static equilibrium conditions to satisfy cut-in and cut-off pressure. Following the development of a dynamic model of the system, a design sensitivity analysis of the spool valve has been carried out through simulation to identify the critical sizes of the parameters, which affect the pump performance. By systematic design, it is possible to have a single-stage spool valve-controlled pressure compensator that can produce a performance of the variable displacement axial piston pump at par with a similar commercially available pump.