Advanced online thermochemical model for metallurgical control at Xstrata Nickel's Sudbury smelter

Abstract

An advanced online thermochemical model was developed for Xstrata Nickel's Sudbury smelter. The model is connected to a PI server to upload plant operating data in real time. The PI server receives and archives plant data through various interfaces, while PI DataLink retrieves the data from the server into a Microsoft Excel spreadsheet. The model was built on the ChemSheet platform, which combines the practicality and simplicity of a spreadsheet, with the robustness of FactSage, a well known thermochemical calculator. The interface of the model is relatively simple and easy-to-use. For effective modelling, the industrial unit operations were divided into thermochemical modules; the modules were then interconnected and compiled sequentially to produce the final result. Online plant data, such as flowrates, compositions and operating conditions, are dynamically entered into the model. For a given feed scenario, the model converges in ∼20 s, producing final results for calcine, matte, slag and off-gas compositions.

The smelter primarily treats Ni–Cu sulphide concentrate via roasters, electric furnace and converters, producing a high grade Bessemer matte product for further refining in Norway. The described model integrates the thermochemistry of the roasters and electric furnace, and predicts important process parameters such as degree of sulphur elimination in the fluid-bed roasters, matte grade, iron metallisation, slag losses and the iron to silica ratio in the electric furnace slag. One of the main objectives of the model was to assist process engineers and operators in calculating the addition rates of coke, flux and air for different feed scenarios. The model is also valuable in helping to stabilise the metallurgy of the converter aisle operation, which is a batch process. Before implementation, the model was extensively validated using daily historical plant data for five randomly selected months from years 2011–2012. The predicted values from the model were found to be in good agreement with the plant data.