A data‐analytic framework to monitor product density of four‐effect falling‐film evaporator for skimmed milk production

Abstract

AbstractThe implementation of Industry 4.0 needs the convergence in operational technology and information technology (OT–IT) for improving the flexibility to access real‐time data and KPIs. In India, a small‐scale automation industry is full of edge devices and produces massive amount of data due to which an infrastructure may overwhelm. These industries face challenges in convergence of IT–OT like scalability. This work developed a data‐driven framework, which is an additional infrastructure between the IT–OT platforms. The framework consists of data cleaning which helps to remove the anomaly from the sensor data. The uncertainty analysis ensures the correctness of the data. Variable ranking and correlation matrix help to identify the KPIs of the process. All these stages are helpful to select the data and push this selected data to cloud for developing predictive or diagnostic models. This framework is deployed for the performance prediction of four‐effect falling‐film evaporator used to produce skimmed milk. The outlet skimmed milk density is the key performance indicator for the present case study and it is monitored here. Both linear and neural network‐based nonlinear models are used in the framework. It is noticed that linear regression model is capable of predicting the plant product density of skimmed milk with good accuracy. However, it is not capable to identify the faults in the key parameters like inlet milk flow rate. These faults can be better identified using k‐means clustering at the initial stage of the framework.Practical applicationsThis article titled “A data‐analytic framework to monitor product density of four‐effect falling‐film evaporator for skimmed milk production” by Nivedita Wagh and Sudhir D. Agashe contains the results obtained for data‐driven framework for performance prediction and fault diagnosis for four‐effect falling‐film evaporator used for production of skimmed milk. A data‐driven framework which includes uncertainty analysis, variable ranking, various linear and neural network‐based nonlinear regression models for performance prediction, and fault detection of an evaporator is proposed. The results are presented in terms of density of the skimmed milk as a performance parameter. The objective of the work is to provide the possibility of IT–OT convergence so as to solve the scalability issue in the IT–OT convergence. The authors are able to bring out a data‐driven framework for fault identification using k‐means clustering using four‐effect falling‐film evaporator used for production of skimmed milk as a case study. We hope the results will be useful to data managers for understanding the KPIs statistics and take appropriate decision in time for the overall growth of organization.