Do you understand your process?
Prepare your process understanding and your database for the applications of Industry 4.0 and AI and find out how cross-ING's process engineering analytics can help you to better understand, control and optimise your processes. We will introduce you to cross-ING process engineering products and show you how you can use them to gain a competitive advantage. How data and cross-ING's ‘process engineering analytics’ are an important step towards ‘Process Engineering 4.0’.
Complex physical, chemical and biological processes are the order of the day in process engineering. Depending on the process, a whole range of parameters need to be measured. For some processes, a reduced number of parameters is also sufficient to monitor them. In many companies, standard parameters such as temperature and pressure have so far been measured to ensure safe operation. In future, this will no longer be sufficient for a comprehensive analysis of important core processes or the optimisation of individual process steps.
Work is often carried out empirically. When errors or problems occur, individual process parameters are changed in the hope of solving the problem. This trial and error approach ties up resources and often does not lead to an understanding of the causes of the problem. As a result, it is not known when the problem will reoccur or how it is related to other problems.
The database must be right
Comprehensive data collection is the basis for many future challenges. However, the principle of quality over quantity still applies. Data must be collected and stored in the right places. This requires an in-depth understanding of the existing process on the one hand and the potential applications on the other.
The inclusion of all relevant scientific processes from physics, chemistry and biology is of crucial importance. This is the only way to ensure that the process behaviour is mapped in the measurement data and that there are no ‘blind spots’.
How can the data help me?
A continuous collection of data that correctly depicts the processes is the decisive basis for many further developments and improvements to your processes.
For process simulation, as offered by the Competence Center Process Engineering, good existing measurement data is of great importance. On the one hand, the right boundary conditions can only be set with the right data. On the other hand, this data is also required for validating the simulation before improvements to the system can be implemented.
A good database is also essential for dynamic process maintenance. This is important for advanced methods such as predictive maintenance. For this type of predictive maintenance, the data set must be broader than for a simulation. In addition to the data during normal operation, measurements of faulty or problematic conditions are also essential in order to prevent them effectively.
Preventive maintenance can also be implemented in cooperation with our Competence Center in Artifical Intelligence. The data collected for maintenance is used to train the AI model. In this case, the model works more reliably if it covers as many process states as possible.
The potential for energy optimisation, e.g. by means of a PinCH analysis, can also only be determined with the correct database. For this, the focus is specifically on the material and heat flows. These must be measured across the entire system in order to calculate the potential savings.
How can cross-ING support me?
cross-ING's ‘process analytics’ service helps you to define the relevant parameters and measured variables and to install and operate a measuring system. To do this, we take an interdisciplinary approach to the process in order to gain a comprehensive scientific understanding. This is based on your requirements for future utilisation:
Would you like to develop your system further towards Industry 4.0?
Would you like to establish predictive maintenance?
Would you like to utilise the benefits of AI for your plant?
Or do you want to optimise the energy efficiency of your plant and decarbonise it?
Our experts will support you with these challenges!
This article was translated using DeepL. While we strive for accuracy, slight variations from the original German text may occur.
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