A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. Step-test data from the industrial plant is used to validate the response of the non-linear model. The results indicate that the model provides a qualitatively accurate response of the main process variables. Because the non-linear model parameters can be calculated from steady-state data, it provides an advantage over classical system identification methods as it does not require an expensive and disruptive step-test campaign to develop linear transfer function models. The model is ideal for model-based predictive process control.

逐步代数例程用于将专门为过程控制开发的动态非线性模型拟合到工业单级磨机电路的稳态过程数据。工业厂房的阶跃测试数据用于验证非线性模型的响应。结果表明，该模型提供了主要过程变量的定性准确响应。因为非线性模型参数可以从稳态数据中计算出来，它提供了优于经典系统识别方法的优势，因为它不需要昂贵且破坏性的步骤测试活动来开发线性传递函数模型。该模型非常适合基于模型的预测过程控制。