Characterisation of Parameters Influencing the Phase Separation in Copper Solvent Extraction Systems Using Oxime-Type Extractants for the Field Operation

Solvent extraction (SX) is one of the most widely applied hydrometallurgical processes in copper production from oxide ore. As the high-grade ore deposits have been developed and depleted, now only low-grade ore deposits are being developed and are therefore facing obstacles of extreme processing conditions. This results in leaching gangue minerals and requires a more complicated solvent extraction system. Recently, synergistic solvent extraction has been introduced to separate copper from the leached solution with high impurities. However, operational obstacles arise due to the complicated solvent extraction process, including multi-stages of extraction, and using more than one extractant in a single solvent extraction system. The phase separation in solvent extraction is one of the major issues in field operation. A poor phase separation could affect the entire process and eventually cause production loss. Therefore, in this study, the phase separation behaviours were studied in consideration of the field operation. Major parameters considered in the study were the type of diluent, temperature, mixing speed, solution pH and Oxidation Reduction Potential (ORP), and addition of impurities (flocculant and colloidal silica). The phase separation behaviours in the continuous counter-current SX system using a pilot-scale mixer-settler in the above conditions was investigated.

Comparative analysis of the wear performance of spindle hook teeth during field work

The wear performance of the spindle hook teeth during field work was compared, and the causes of the wear failure of the spindle hook teeth were analyzed. Samples of three kinds of spindle were obtained from the fixed installation position under continuous field operation conditions. The hardness, phase structure, elemental composition, and micromorphology of the spindle hook teeth were characterized using microhardness, X-ray diffractometer (XRD), energy spectrum analyzer (EDS), and scanning electron microscope (SEM) after cutting of the spindles. Results show that the coating hardness and element penetration zone of No.3 spindle hook tooth are the largest, and the surface coating phase structure of the three kinds of spindle is mainly a body-centered cubic structure of Cr (211). Micro-crack and hole defects exist in the coating of all three kinds of spindle. The thicknesses of the coating of No.1, No.2, and No.3 spindles are 74, 100, and 130 μm, respectively. During the field operation, the wear of the spindle hook tooth coating is caused by abrasive wear and fatigue wear, while the wear of the substrate is the result of the combined effect of abrasive wear and oxidation wear. Extracting the wear area and width of spindle hook teeth shows that the wear area of all three kinds of spindle hook teeth increases exponentially and the wear width changes linearly with the increase in field operation area.

Perdido GoM Total Field ESP Integrated Full-Field Modeling for Surveillance and Forecast Prediction

The paper reports on the integrated modeling approach for the Gulf of Mexico ultra-Deepwater Perdido Electrical Submersible Pump (ESP) meant for performance surveillance and operational predictions n viscous fluid and multiphase flow regimes. The ESP system uses in Perdido field are multistage centrifugal pumps for high flow rate and high boost applications. Multiphase flow and viscous fluids introduce challenges for ESP operations such as performance degradation and system inefficiency which are imperative and to be considered in modeling work for accuracy and proper surveillance. The effect results from free gas and viscous fluid could be difficult to model and if not considered can result in wrong prediction of pump performance and power estimation over promising forecast and incorrect performance for surveillance and optimization. The objective of modelling work is aimed to correctly predict ESP performance by using proper modeling equations and correction factors that to understand the degradation on head, flow and horsepower, therefore further guide offshore surveillance activities and near and long term forecast predictions. The ESP system used in Perdido Field is 1025 series tandem P360 mixed flow type pumps with a 725 series 1600 hp motor. The modeling work revealed mathematical relationships among pump performance parameters (pump speed/frequency, brake horsepower, pump head and pump capacity) and free Gas/Viscosity correction factors required to accurately predict pump performance and forecasting. Using the correct approach for modeling high boost ESPs, an accuracy of 5% or less can be aimed with field operation for precise pump performance troubleshoot, optimization and long term forecast for economic analysis. The main technical contributions of this work are the detail modelling approach, analysis and data comparison with Field operation for predicting pump and motor performance under two important variables, high viscosity and two-phase flow inside the ESP.

Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>

Machine Learning based Identification and Classification of Field-Operation caused Solar Panel Failures observed in Electroluminescence Images

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.<br>