A data-analytic framework to monitor product density of foureffect falling-film evaporator for skimmed milk production

摘要

The implementation of Industry 4.0 needs the convergence in operational technologyand information technology (OT–IT) for improving the flexibility to access real-timedata and KPIs. In India, a small-scale automation industry is full of edge devices andproduces massive amount of data due to which an infrastructure may overwhelm.These industries face challenges in convergence of IT–OT like scalability. This workdeveloped a data-driven framework, which is an additional infrastructure betweenthe IT–OT platforms. The framework consists of data cleaning which helps to removethe anomaly from the sensor data. The uncertainty analysis ensures the correctnessof the data. Variable ranking and correlation matrix help to identify the KPIs of theprocess. All these stages are helpful to select the data and push this selected data tocloud for developing predictive or diagnostic models. This framework is deployed forthe performance prediction of four-effect falling-film evaporator used to produceskimmed milk. The outlet skimmed milk density is the key performance indicator forthe present case study and it is monitored here. Both linear and neural networkbasednonlinear models are used in the framework. It is noticed that linear regressionmodel is capable of predicting the plant product density of skimmed milk with goodaccuracy. However, it is not capable to identify the faults in the key parameters likeinlet milk flow rate. These faults can be better identified using k-means clustering atthe initial stage of the framework.