Identification of working fluids and development of their mathematical models should always precede construction of a proper model of the analysed thermodynamic system. This paper presents method of development of a mathematical model of working fluids in a gas turbine system and its implementation in Python programming environment. Among the thermodynamic parameters of the quantitative analysis of systems, the following were selected: specific volume, specific isobaric and isochoric heat capacity and their ratio, specific enthalpy and specific entropy. The development of the model began with implementation of dependencies describing the semi-ideal gas. The model was then extended to the real gas model using correction factors reflecting the impact of pressure. The real gas equations of state were chosen, namely due to Redlich–Kwong, Peng–Robinson, Soave– Redlich–Kwong, and Lee–Kesler. All the correction functions were derived analytically from the mentioned equations of real gas behaviour. The philosophy of construction of computational algorithms was presented and relevant calculation and numerical algorithms were discussed. Created software allowed to obtain results which were analysed and partially validated.
The rapid global economic development of the world economy depends on the availability of
substantial energy and resources, which is why in recent years a large share of non-renewable
energy resources has attracted interest in energy control. In addition, inappropriate use of
energy resources raises the serious problem of inadequate emissions of greenhouse effect gases,
with major impact on the environment and climate. On the other hand, it is important
to ensure efficient energy consumption in order to stimulate economic development and
preserve the environment. As scheduling conflicts in the different workshops are closely
associated with energy consumption. However, we find in the literature only a brief work
strictly focused on two directions of research: the scheduling with PM and the scheduling
with energy. Moreover, our objective is to combine both aspects and directions of in-depth
research in a single machine. In this context, this article addresses the problem of integrated
scheduling of production, preventive maintenance (PM) and corrective maintenance (CM)
jobs in a single machine. The objective of this article is to minimize total energy consumption
under the constraints of system robustness and stability. A common model for the integration
of preventive maintenance (PM) in production scheduling is proposed, where the sequence
of production tasks, as well as the preventive maintenance (PM) periods and the expected
times for completion of the tasks are established simultaneously; this makes the theory put
into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software,
the performance of the proposed integer binary mixed programming model is tested and
evaluated. Indeed, after numerically experimenting with various parameters of the problem,
the B&B algorithm works relatively satisfactorily and provides accurate results compared
to the GA algorithm. A comparative study of the results proved that the model developed
was sufficiently efficient.