A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model

Belman-Flores, J.M.; Ledesma, Sergio; Barroso-Maldonado, J.M.; Navarro-Esbrí, Joaquin

doi:10.1016/j.ijrefrig.2015.07.017

Cited by 25 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the control volume V with a density ρ, mass m, pressure P, and temperature T, the time derivative of the chamber pressure can be obtained by using three important equations: the equation of state (ideal gas) law, the conservation of mass equation, and the energy equation. The unified form of the dynamic pressure equation in the control volume is Equation (13). The detailed derivation process can be seen in [21].…”

Section: Buffer Tankmentioning

confidence: 99%

“…Encouragingly, artificial neural networks (ANNs) have been widely used in reciprocating compressor modeling and system optimization due to their advantages of being adaptive, self-learning, and fault-tolerant and working with nonlinear mapping. Belman et al set up a physical mechanism model and an artificial neural network model of a reciprocating compressor, respectively, with an experimental refrigeration device as the research object and analyzed and compared these models through parameters such as exhaust volume, exhaust temperature, and energy consumption [13]. Barroso-Maldonado et al developed two models: one using an artificial neural network and another one using a probabilistic neural network to predict and simulate the behavior of a reciprocating compressor [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

et al. 2020

View full text Add to dashboard Cite

The regulating performance degradation of the stepless capacity regulation system for reciprocating compressors occurs frequently in long-term operations. It affects the safe and stable operation of the compressor seriously. The degradation mechanisms in a stepless capacity regulation system are mainly caused by valve leakage, degeneration of the reset spring of the unloader, and (or) deviation of the solenoid valve’s characteristic parameters. In this study, to research the system performance degradation mechanisms and the influence of control parameters on system behavior, a multi-subsystem mathematics model which integrates compressor, gas pipeline, buffer tank, and actuator was built. In order to calculate the rate of degradation, a load prediction model based on a modified back-propagation neural network was established. The rate of degradation can be calculated using the predicted results. In order to optimize system regulation performance, a degradation-based optimization framework was developed which determines optimum control parameter compensation to achieve a minimum degradation rate. In addition, in order to avoid over-compensation, an adaptive control parameter compensation optimization method was adopted. According to the deviation between the given load and the prediction load, the control parameter compensations are obtained adaptively. Finally, two optimization experiments are carried out to show the effectiveness of the developed framework. The optimization results illustrate the degradation rate of the system gradually returning to normal during 60s without any over-compensation.

show abstract

Section: Buffer Tankmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Results obtained with ANNs reduced the development time and the cost at the initial design stages. Belman-Flores compared two techniques for modelling a reciprocating compressor: ANN and physical modelling [30]. ANN had higher accuracy than physical models.…”

Section: Innovations In Energy Managementmentioning

confidence: 99%

Intelligent Energy Management of Compressed Air Systems

Thabet

Sanders

Becerra

et al. 2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

View full text Add to dashboard Cite

This work considers the use of real time sensing and AI (machine learning) to increase Compressed Air Systems (CAS) efficiency. Algorithms that automate the detection of energy inefficiencies and make decisions regarding suitable troubleshooting procedure will be created. Systems using compressed air are often inefficient and expensive to operate. Less than one unit of energy is turned into useful compressed air for every ten provided. Intelligent systems will be used to reduce energy consumption in compressors by considering real-time circumstances and the predicted needs. Sensor data will deliver information about the real time performance. AI will interpret the data and then act automatically. New intelligent techniques will be applied to save energy. This paper presents a review of the recent literature covering the topic of CAS energy efficiency. Some gaps in research were identified in the area of developing technologies and methods to detect and treat energy inefficiencies in CAS.

show abstract

“…It is developed and frequently used for heat transfer studies in IC engines. Also, it has been widely used in reciprocating compressors since, by neglecting the combustion source term, the common peak of the convective coefficient close to a crank angle of 180 • is shifted to the end of the compression step as it is expected for reciprocating compressors [8,21]. The equation of heat transfer coefficient is defined as:…”

Section: Reciprocating Compressor Sub-modelmentioning

confidence: 99%

A New Hybrid Reciprocating Compressor Model Coupled with Acoustic FEM Characterization and Gas Dynamics

et al. 2019

View full text Add to dashboard Cite

Accurate comprehension of thermodynamic demeanor and pressure pulsation propagation is of great attractiveness in a reciprocating compressor system. To consider the reciprocal interaction between compressor and pipelines, a hybrid numerical model is thus built by coupling the in-cylinder lumped parameter approach, in-pipe 1D gas dynamics and 3D acoustic characteristics of chambers. The transmission and reflection coefficients of a geometrically complex chamber are achieved by the definition of an acoustic characterization method based on acoustic FEM simulation data, with a high level of accuracy. Numerical results of this new hybrid model are compared with predictions from the traditional hybrid model with in-pipe 1D gas dynamics, together with experimental data. Through comparison analysis, the advantages are highlighted in using the acoustic FEM characterization for complex elements since the new model performs numerical solution without introducing any simplifications to the geometry of fluid domain.

show abstract

A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model

Cited by 25 publications

References 24 publications

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

Intelligent Energy Management of Compressed Air Systems

A New Hybrid Reciprocating Compressor Model Coupled with Acoustic FEM Characterization and Gas Dynamics

Contact Info

Product

Resources

About