How to achieve automated control in industrial production
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This post was last edited by yuchenchf on 2016-7-27 at 13:22. Due to the complexity of production processes and the harsh conditions in industrial environments, some important or critical parameters cannot be measured directly (such as concentration, viscosity, polymerization rate, etc.). To ensure product quality, predictive models are developed using online monitoring techniques in order to establish a relationship between quality metrics and operational parameters. The parameters that need to be monitored are read in real time, and the parameter control devices are adjusted automatically so that the actual values match the required values for production. This helps to reduce the rate of defective products, improve product quality, and lower energy consumption. The widespread use of soft measurement is the first step toward achieving industrial automation! What do you experts think?
I think you’re right, but what do you mean? . . For some polymerization units in the chemical industry, such predictive models are used; they essentially consist of fuzzy control plus a self-learning function. . . But I haven’t experienced it firsthand yet
We have a technology that enables online monitoring of certain parameters of liquid-phase materials, such as concentration, viscosity, polymerization rate, conversion rate, etc.