Facing environmental challenges, high energy costs and a growing public awareness, the global brewing industry is increasingly publishing ambitious targets toward a more sustainable production. Small and medium-sized enterprises of the brewing and beverage industry cannot ensure energy and media efficiency mainly due to capital and knowledge inadequacy. This article addresses this problem and presents a pragmatic method to determine the energy and media demand. Accordingly, a modeling editor as well as a standardized data structure and automatic simulation parameter determination tools were developed to implement the method. A given production plant can be modeled with adequate details using the presented editor. Based on a configuration file, a holistic simulation model can be generated automatically in a simulation environment. A beverage bottling plant was studied, and the necessary datasets were obtained for implementing the proposed editor and, thereby, the method. It was confirmed that the simulated values of electrical energy and compressed air consumption match the measured empirical data. The measures to increase energy and media efficiency were also found effective. Using the presented method, enterprises of the brewing and beverage industry can easily uncover avenues for potential savings, test the effectiveness of optimization strategies, and substantiate possible investment decisions.

The global brewing industry is facing enormous environmental challenges and is urgently required to produce sustainably and efficiently. The rising costs of energy and electricity are forcing small and medium-sized breweries in particular, which are confronted with barriers such as lack of capital and know-how, to make substantial changes. This article presents an extended approach to prognose the energy and media demand for batch-oriented production of a brewery. Therefore, based on a modeling editor as well as a standardized data structure and an approach to determine the simulation-relevant parameters, a solution for fast and easy model generation was developed. Extensive measurement recordings within a brewhouse were performed to create a comprehensive model with recipe-specific parameters and detailed production plans. A simulation model can be generated automatically from a configuration file in a simulation environment that has been extended to include the mapping of batch-oriented operation. A validation is presented and a maximum deviation of the electrical and thermal energy demand of 1–2% is achieved. In combination with a preliminary work, the holistic simulation of the complex combined production of batch-oriented and discrete operation within the brewery is presented and allows comprehensive analysis as well as optimization towards sustainable production.