Planning and Acquisition of Real-Time Production Data Through the Virtual Factory in Chemical Industry

Abstract

The various production problems that have arisen are closely linked to the need of the digitize products, production equipment, and their processes. With the increasing use of innovative software and hardware solutions, it is possible to monitor production processes accurately in the real-time and to manage various planning decisions according to these digital models. Such digital models allow us to react quickly to the physical production problems and to solve and also predict them. Furthermore, the virtual factory as an integrated simulation model of production units, provides an advanced decision support capability. On the other hand, Industry 4.0, the new industrial revolution has increasingly been used in the industrial sector and its development has grown exponentially in recent years. Various production equipment and activities are connected via network sensors to the Internet, where a huge amount of data is generated, stored, and analyzed. Industrial Artificial Intelligence (AI) algorithms are being used to evaluate the collected data and to provide valuable information for planning operations. This new industrial age presents new trends and challenges in the data context, such as scalability, cyber-security, and big data. Therefore, when it comes to collecting data from devices and workplaces in real time, it is also wise to analyze the necessity and efficiency of this data, using different artificial intelligence algorithms. Clean data generally enables to make efficient and effective management decisions in the future based, to ensure the highest possible efficiency in the production unit. This article outlines the principles of Industry 4.0, emphasizing the features, requirements, and challenges of Industry 4.0. Besides, a development of the virtual model of the production line, there is also developed virtual model of the Autonomous Mobile Robots (AMR). This gives a good opportunity to monitor and analyze the entire production cycle, including the throughput, lead time, and utilization of resources in a 3D simulation production environment. Moreover, the article focuses on collecting real-time data from the virtual production unit to analyze the methods and locations of data collection, which would provide the most valuable information about production data. Finally, based on the results of the collected data, the authors present and discuss the challenges and trends that lie ahead when the same data collection methods are being used for physical production units. A case study approach is used to demonstrate the relevance and feasibility of the proposed methods for real-time data acquisition in production, which uses the concept of internet of things technologies and 3D visualization.

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