Data acquisition for digitisation in the paraffin candle production process (Mokslo taikomieji tyrimai / Applied Research, 2023)
By utilising digital technology, digitisation helps businesses streamline procedures, increase efficiency and cut costs. Moreover, it enables businesses to gather knowledge and make data-driven choices that enhance product quality, customers’ satisfaction, and market competitiveness. Data acquisition, which permits the collection and analysis of real-time data from equipment and processes, is a crucial component of the digitalisation of industrial automation. Predictive maintenance may, therefore, be utilised to decrease downtime, enhance quality control, and optimise operations. The paraffin candle production process is being discussed, and its key variables are being identified for the purpose of digitisation. The study proposes data collection and processing methods from a candle production line and uses an IoT-enabled controller to collect and analyse data. The PLC software that reads, processes, and saves data in a SQL database on a remote PC is then built based on these techniques. An MSSQL database is used to store information on process variables, including temperatures, cycle times, and downtimes, for the different candle production stages, including the paraffin preparation tank, extruder, and conveyor for cutting candles. The database acts as a storage location for the gathered information, which can then be utilised to analyse and improve the manufacturing process. Python and the open-source package Streamlit were used to provide a user-friendly interface for operators. The interface allows the user to select historical data for each item of equipment by time and relevant variables, and it shows the filtered data in an understandable graphical style. This allows the operator to evaluate the production process, spot trends, and optimise it. With the use of support and assistance technologies, the data may be used for decision-making in future.
Keywords: digitisation; data acquisition; Python; Streamlit; MSSQL