In modern production environments, there are many different systems that are interconnected within complex processes. Usually, these systems already come equipped with integrated controls from the manufacturer to ensure optimal and efficient operation. However, especially in the case of supply systems, the controls often operate autonomously, without considering changed process environments or connected systems from third-party manufacturers. In practice, this often leads to systems not interacting efficiently with each other.

Counterproductive Operation

A simple example of this is the interaction between heating and air conditioning systems. The controls regulate the operation of each individual system optimally; nevertheless, it often happens during transitional periods that the heating system is still running while the air conditioning is already operating. The reason is that the respective controls react to external influences such as temperature but are not integrated with each other. In production environments, similar behavior patterns can be observed in supply systems, such as in compressed air generation.

Software as a Reliable Controller

Typically, process specialists already have a gut feeling that something is not running optimally, but it is very difficult for them to support this with reliable data and information that would justify intervention. This is where the various mathematical and AI-supported methods in intelligent energy management software come into play. First, together with the customer, all important system, environmental, and process parameters are identified, and the corresponding data from the different sources are centrally recorded in the application.

Example: Compressed Air

For compressed air generation, this means that all important parameters are recorded both on the supply and the demand side. This includes, for example, data on power consumption, delivered pressure levels, the status of individual compressors, temperatures, but also values concerning consumers, such as the status and operating information of production systems, individual compressed air consumption, requested quantities, etc. Which values are important in detail depends, of course, on the specific process being considered.

AI as Support

Subsequently, this wealth of historical data is thoroughly analyzed using regressions, mathematical algorithms, and machine learning methods. Based on these comprehensive analyses, the mathematical relationships between the individual data points—i.e., cause and effect within the processes—can be illustrated.

Optimizing Operations with Forecasts

This knowledge of the detailed interrelationships makes it possible to identify and rectify irregularities in different operating and process conditions. To ensure the optimal operation of the systems in the long term, users then utilize this knowledge for continuous monitoring. Based on the parameters, expected consumption or behaviors can be forecasted and compared with the measured data. If actual values deviate from the forecasts, a corresponding notification can be triggered. In this way, the system automatically ensures that processes continue to run optimally and efficiently in the future.

Picture: Frolopiaton Palm/Freepik.com