An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods

Abstract

The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team s recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.