Power supply systems based on air-cooled proton exchange membrane fuel cell (PEMFC) stacks are becoming more popular as power sources for mobile applications. We try to create a PEMFC model that allows for predicting the PEMFC operation in various climatic conditions. A total of two models were developed and used: the membrane electrode assemble (MEA) model and the PEMFC stack model. The developed MEA model allows to determine the influence of external factors (temperature) on the PEMFC power density. The data obtained using the developed model correlate with experimental data at low ambient temperatures (10–30 °C). The difference between the simulation and experimental data is less than 10%. However, the accuracy of the model during PEMFC operation at high (>30 °C) and negative ambient temperatures remains in doubt and requires improvement. The obtained data were integrated into the air-cooled PEMFC stack model. Data of the temperature fields distribution will help to manage the processes in the PEMFC stack. The maximum temperature is slightly above 60 °C, which corresponds to the optimal conditions for the operation of the stack. The temperature gradient across the longitudinal section is very low (<20 °C), which is a positive factor for the chemical reaction. However, the temperature gradient observed across the cross section of the PEMFC stack is 30 °C. The data obtained will help to optimize the mass-dimensional characteristics of air-cooled proton exchange membrane fuel cell and increase their performance. The synergetic effect between the MEA model and the PEMFC stack model can be successfully used in the selection of materials and the development of a thermoregulation system in the PEMFC stack.
The practice of managing logistic business processes in companies has many examples when negative scenarios are realized in the course of events that are unforeseen at the planning stage, and ultimately lead to significant damage. These circumstances define the necessity to develop methods to reduce the risks of the negative impact of uncertainty or inadequate information, which is typical for the planning stage, on the final results. The paper describes methodological approaches to accounting uncertainty in models and methods which are used to plan logistics business processes in a company in order to reduce these risks. At the same time, in order to ensure that the plans are sound, it is proposed to form arbitrary reserves of allocated resources that compensate for uncertainty. Taking into account various types of the information situation at the planning stage, methods are proposed for determining appropriate levels of such reserves of allocated resources. The suggested methodological approaches to accounting uncertainty form a basis for building specific models and methodologies to be used for planning logistics business processes of various companies, given their features. These models and techniques are an important element in the digital transformation of logistics business processes. Their use leads to lower costs in the implementation of these processes.
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