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.

高炉是钢铁厂的主要炼铁厂，受多个工艺参数和炉顶煤气压力的影响，这是保持钢铁厂稳定性和生产效率的关键过程控制现象。由于其强烈的化学反应和热平衡要求的性质，高炉操作不能容忍任何中断、不平衡操作、瞬时干扰或失控。因此，通过仪器测量来监测和控制熔炉的炉顶气系统组件以保持稳定、高效的运行和系统安全是至关重要的。在这项研究中，使用土耳其 Erdemir BF#2 按时间顺序获得的实时过程数据开发了一种新型顶部气体压力跟踪系统。根据工厂维护团队的建议和软计算方法，将八个工艺参数视为输入参数，采用人工神经网络和自适应神经模糊推理系统，并应用统计回归工具，自回归综合移动平均线进行比较。性能和成功率分析使用决定系数（R ² )，平均绝对百分比误差和均方根误差项。发现自适应神经模糊推理系统的最佳模型输出为 0.95、1.21 和 0.023，对于输出值为 0.94、0.029 和 1.32 的人工神经网络模型获得的性能略低于R ²，分别是平均绝对百分比误差和均方根误差项。发现最大预测误差为 9.85% 和 10.2%，自适应神经模糊推理系统和 ANN 模型的平均预测误差分别为 1.19% 和 1.29%，以进行最佳模拟。拟议的神经模糊驱动的炉顶气压力预测系统在文献中是独一无二的，应该集成到现有的控制系统中以提高操作意识和可持续性，或者可以用作未来可能的炉顶气回收系统的输入指导。