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

2020 ◽  
Vol 10 (2) ◽  
pp. 704 ◽  
Author(s):  
Wenhua Liu ◽  
Zhinong Jiang ◽  
Yao Wang ◽  
Chao Zhou ◽  
Xu Sun ◽  
...  
Keyword(s):  
Degradation Rate ◽  
Control Parameter ◽  
Back Propagation ◽  
Optimization Method ◽  
Performance Degradation ◽  
Regulation System ◽  
Stable Operation ◽  
Degradation Mechanisms ◽  
Load Prediction ◽  
Parameter Compensation

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.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

scholarly journals Performance improvement of wastewater treatment processes by application of machine learning

2020 ◽  
Vol 82 (12) ◽  
pp. 2671-2680
Author(s):  
O. Icke ◽  
D. M. van Es ◽  
M. F. de Koning ◽  
J. J. G. Wuister ◽  
J. Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Predictive Control ◽  
Stable Operation ◽  
Load Prediction ◽  
Network Modelling ◽  
Quality Requirements ◽  
Effluent Quality ◽  
Treatment Processes ◽  
Advanced Analytics

Abstract Improving wastewater treatment processes is becoming increasingly important, due to more stringent effluent quality requirements, the need to reduce energy consumption and chemical dosing. This can be achieved by applying artificial intelligence. Machine learning is implemented in two domains: (1) predictive control and (2) advanced analytics. This is currently being piloted at the integrated validation plant of PUB, Singapore's National Water Agency. (1) Primarily, predictive control is applied for optimised nutrient removal. This is obtained by application of a self-learning feedforward algorithm, which uses load prediction and machine learning, fine–tuned with feedback on ammonium effluent. Operational results with predictive control show that the load prediction has an accuracy of ≈88%. It is also shown that an up to ≈15% reduction of aeration amount is achieved compared to conventional control. It is proven that this load prediction-based control leads to stable operation and meeting effluent quality requirements as an autopilot system. (2) Additionally, advanced analytics are being developed for operational support. This is obtained by application of quantile regression neural network modelling for anomaly detection. Preliminary results illustrate the ability to autodetect process and instrument anomalies. These can be used as early warnings to deliver data-driven operational support to process operators.

scholarly journals Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4749 ◽  
Author(s):  
Julián Ascencio-Vásquez ◽  
Ismail Kaaya ◽  
Kristijan Brecl ◽  
Karl-Anders Weiss ◽  
Marko Topič
Keyword(s):  
Degradation Rate ◽  
Global Climate ◽  
Global Scale ◽  
Degradation Mechanisms ◽  
Climate Data ◽  
Pv Systems ◽  
Degradation Rates ◽  
Pv Modules ◽  
Long Term Performance ◽  
Term Performance

Photovoltaic (PV) systems are the cheapest source of electricity in sunny locations and nearly all European countries. However, the fast deployment of PV systems around the world is bringing uncertainty to the PV community in terms of the reliability and long-term performance of PV modules under different climatic stresses, such as irradiation, temperature changes, and humidity. Methodologies and models to estimate the annual degradation rates of PV modules have been studied in the past, yet, an evaluation of the issue at global scale has not been addressed so far. Hereby, we process the ERA5 climate re-analysis dataset to extract and model the climatic stresses necessary for the calculation of degradation rates. These stresses are then applied to evaluate three degradation mechanisms (hydrolysis-degradation, thermomechanical-degradation, and photo- degradation) and the total degradation rate of PV modules due to the combination of temperature, humidity, and ultraviolet irradiation. Further on, spatial distribution of the degradation rates worldwide is computed and discussed proving direct correlation with the Köppen-Geiger-Photovoltaic climate zones, showing that the typical value considered for the degradation rate on PV design and manufacturer warranties (i.e., 0.5%/a) can vary ± 0.3%/a in the temperate zones of Europe and rise up to 1.5%/a globally. The mapping of degradation mechanisms and total degradation rates is provided for a monocrystalline silicon PV module. Additionally, we analyze the temporal evolution of degradation rates, where a global degradation rate is introduced and its dependence on global ambient temperature demonstrated. Finally, the categorization of degradation rates is made for Europe and worldwide to facilitate the understanding of the climatic stresses.

Design of Wind-Solar-Diesel-Battery Hybrid Power System

2014 ◽  
Vol 556-562 ◽  
pp. 1818-1821 ◽  
Author(s):  
Wen Lei Hu ◽  
Run Sheng Yang ◽  
Pu Xie
Keyword(s):  
Intelligent Control ◽  
Power Supply System ◽  
Optimization Method ◽  
Supply System ◽  
Stable Operation ◽  
Network Technology ◽  
System Configuration ◽  
Hybrid Power ◽  
Independent Power Supply ◽  
Working Principle

In order to solve the problem of electricity from the grid, designing a set of independent power supply system based on micro-network technology, through the use of solar, wind, diesel, battery, etc. This paper introduces the structure and working principle of the system, focuses on the design of the control system to meet under no circumstances, intelligent control of the entire system to make it stable operation, and optimization method proposed system configuration.

scholarly journals Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

2022 ◽  
Vol 2161 (1) ◽  
pp. 012068
Author(s):  
Sthitprajna Mishra ◽  
Bibhu Prasad Ganthia ◽  
Abel Sridharan ◽  
P Rajakumar ◽  
D. Padmapriya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Smart Grid ◽  
User Interfaces ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Load Prediction ◽  
Artificial Neural ◽  
Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

scholarly journals THE INFLUENCE OF DEGRADATION SPEED ON ESTIMATION OF RESIDUAL RESOURCE OF REINFORCED CONCRETE HIGHWAY BRIDGES IN UKRAINE

2019 ◽  
pp. 10-18
Author(s):  
A. DAVYDENKO
Keyword(s):  
Reinforced Concrete ◽  
Prediction Model ◽  
Prediction Error ◽  
Degradation Rate ◽  
Control Parameter ◽  
Highway Bridges ◽  
Theoretical Research ◽  
Natural Rate ◽  
Practical Tool ◽  
First Time

Purpose. Analyze the effect of degradation rate on estimation of a remaining lifetime of Ukrainian highway bridges. Methods. Theoretical research. Results. The significant effect of degradation rate on the estimation of a remaining lifetime as the only control parameter of the Markov prediction model is proved. Originality. For the first time, the prediction error range of a remaining lifetime have been set at a constant natural rate of degradation. Practical value. The obtained results are a practical tool for managing the reliability and resource of reinforced concrete highway bridges.

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