Development of an Artificial Neural Network to Predict Sulphide Capacities of CaO–SiO₂–Al₂O₃–MgO Slag System

Abstract

Depletion of the high quality ores around the world has forced ferronickel producers to extract metal values from low-grade ore bodies with significant amounts of impurities. Under this condition, maintaining alloy quality is of utmost importance for the smelters; however still, accessibility of a reliable sulphide capacity model for FeNi refining processes is an issue. Many of the current models, such as those incorporating optical basicity, have proven to be erroneous and unreliable for wide ranges of composition and temperature. These models are typically developed and tested without a proper validation method thus allowing for great correlations which may not fare well with the introduction of new data. Models built from fundamental thermodynamic data perform much better in predicting sulphide capacities but are not only complicated to formulate but also too complicated to be used by operators on a day to day basis as multitude of inputs are needed. Hence, development of a reliable model based on fundamentals, which can also be directly used by plant operators is very much demanded by the industry. In the current study, an artificial neural network (ANN) approach has been used to predict sulphide capacities of slag compositions in the CaO-SiO 2 -Al 2 O 3 -MgO system with an objective to be used in ferronickel refining processes. The resulting models are evaluated on: 1) coefficient of multiple determination (R 2 ), 2) correlation strength (r), 3) root mean square error (RMSE) and 4) computation speed. The ANN based model has shown to be superior in predicting sulphide capacities to current models.

Prediction Method of Graphene Defect Modification Based on Neural Network

“…Liu et al designed a constrained multistep predictive control based on a fuzzy neural network model when studying the linear speed control of sintering production lines [13]. Ma et al combined neural network predictor with a fuzzy controller, which was applied in the control system of sewage treatment plant [14]. Oguz et al used the mode of alternating control of fuzzy controller and recurrent neural network for nonlinear model prediction, which made the control system have the advantages of selfadaptability, small overshoot and reduction, and shortened adjustment time [15].…”

Physicochemical properties and structure of titania-containing metallurgical slags: a review

“…This model claims a high heuristic capability without compositional limitations. Some models are not currently applicable to titania-containing slags, but there is a potential to extend it to titania-containing slags in future work, such as the KTH model [121], molecule coexistence theory model [122], and intelligent algorithms models [123][124]. The KTH model [121] predictions have shown a good agreement with the experimental results, but the calculation process needs lots of interaction parameters.…”

“…The rapid development of intelligent algorithms has attracted more and more researchers in the field of metallurgy. Ma et al [123] and Xin et al [124] established the sulfide capacity models based on artificial neural network and extreme learning machine for CaO-SiO2-MgO-Al2O3 system, respectively. At the same time, the intelligent algorithms method based on big data has great limitations currently on titania slag with insufficient data.…”

Sulphide capacity prediction of CaO–SiO2–MgO–Al2O3 slag system by using regularized extreme learning machine

“…The demand for sulphur control is becoming increasingly stringent in molten steel to produce high-quality steel. A much more reliable slagging regime is also highly required in the desulphurization process due to the deteriorating ore quality [1][2][3][4]. The sulphide capacity (Cs) proposed by Richardson et al [5], was a well-recognized index to evaluate the desulphurization ability of slag in the steelmaking and has attracted great attention to quantify the value of Cs under various conditions, such as temperature and basicity.…”