Performance Analysis of a Full-Scale Desalination Plant with Reverse Osmosis Membranes for Irrigation

Reverse osmosis (RO) is the most widely used technology for seawater desalination purposes. The long-term operating data of full-scale plants is key to analyse their performance under real conditions. The studied seawater reverse osmosis (SWRO) desalination plant had a production capacity of 5000 m3/d for irrigation purposes. The operating data such as conductivities flows, and pressures were collected for around 27,000 h for 4 years. The plant had sand and cartridge filters without chemical dosing in the pre-treatment stage, a RO system with one stage, 56 pressure vessels, seven RO membrane elements per pressure vessel and a Pelton turbine as energy recovery device. The operating data allowed to calculate the average water and salt permeability coefficients (A and B) of the membrane as well as the specific energy consumption (SEC) along the operating period. The calculation of the average A in long-term operation allowed to fit the parameters of three different models used to predict the mentioned parameter. The results showed a 30% decrease of A, parameter B increase around 70%. The SEC was between 3.75 and 4.25 kWh/m3. The three models fitted quite well to the experimental data with standard deviations between 0.0011 and 0.0015.

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants

Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.

Multi-Dimensional Analysis of Load Characteristics of Electrical Vehicles Based on Power Supply Side Data and Unsupervised Learning Method

This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.