Modeling and fault detection of an industrial copper electrowinning process

Copper electrowinning plants are where high purity copper (Cu) product is obtained through electrochemical reduction of copper from the leaching solution. The presence selenium (Se) and tellurium (Te) in copper sulphide minerals may result in contamination of the leach solution and, eventually of the copper cathode. Unfortunately, hydrometallurgical processes are often difficult to monitor and control due to day-to-day fluctuations in the process as well as limitations in capturing the data at high frequencies. The purpose of this work is to model key variables in the copper electrowinning tank and to apply statistical fault detection to the selenium/tellurium removal and copper electrowinning process operations. First principle modeling was applied to the copper electrowinning tank and partial differential equation models were derived to describe the process dynamics. Industrial data were used to estimate the model parameters and validate the resulting models. Comparison with industrial model shows that the models fit reasonably well with industrial operation. Simulations of the models were run to explore the dynamics under varying operating conditions. The derived models provide a useful tool for future process modification and control development. Using the collected industrial operating data, dynamic principal component analysis (DPCA) based fault detection was applied to Se/Te removal and copper electrowinning processes at Vale’s Electrowinning Plant in Copper Cliff, ON. The fault detection results from the DPCA based approach were consistent with the industrial product quality test. After faults were detected, fault diagnosis was then applied to determine the causes of faults. The fault detection and diagnosis system helps define causes of upset conditions that lead to coppercathode contamination.Master of Applied Science (M.A.Sc.) in Natural Resources Engineerin

Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches

Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all process related variables. A low-cost alternative lies in the possibility of using knowledge about boiler operation to extract information about its condition from standard online process measurements. This approach is evaluated with the aim of enhancing online condition monitoring of a boiler’s convective pass heat exchanger network by respectively using a soft sensor and a data-driven method. The soft sensor approach is based on a one-dimensional thermofluid process model which takes measurements as inputs and calculates unmeasured variables as outputs. The model is calibrated based on design information. The data-driven method is one developed specifically in this study to identify unique fault signatures in measurement data to detect and quantify changes in unmeasured variables. The fault signatures are initially constructed using the calibrated one-dimensional thermofluid process model. The benefits and limitations of these methods are compared at the hand of a case study boiler. The case study boiler has five convective heat exchanger stages, each composed of four separate legs. The data-driven method estimates the average conduction thermal resistance of individual heat exchanger legs and the flue gas temperature at the inlet to the convective pass. In addition to this, the soft sensor estimates the average fluid variables for individual legs throughout the convective pass and therefore provides information better suited for condition prognosis. The methods are tested using real plant measurements recorded during a period which contained load changes and on-load heat exchanger cleaning events. The cleaning event provides some basis for validating the results because the qualitative changes of some unmeasured monitored variables expected during this event are known. The relative changes detected by both methods are closely correlated. The data-driven method is computationally less expensive and easily implementable across different software platforms once the fault signatures have been obtained. Fault signatures are easily trainable once the model has been developed. The soft sensors require the continuous use of the modelling software and will therefore be subject to licencing constraints. Both methods offer the possibility to enhance the monitoring resolution of modern boilers without the need to install any additional measurements. Implementation of these monitoring frameworks can provide a simple and low-cost contribution to optimized boiler performance and reliability management