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.

Traditionally the aggregate production plan helps in determining the inventory, production, and work-force, based on the demand forecasts without considering the productivity loss at a tactical level in supply chain planning. In this paper, we include the productivity loss into traditional aggregate production plan and the prescriptive analytics technique, linear programming, is used to solve this problem of practical interest in the domain of multifarious businesses and industries. In this study, we discussed two model variations of the aggregate production planning problem with and without productivity loss, i) fixed work-force, and ii) variable Work Force. The mathematical models were designated to be solved by using an open-source python pulp package in order to evaluate the impacts of the productivity loss on both the models. PuLP is an open-source modeling framework provided by the COIN-OR Foundation (Computational Infrastructure for Operations Research) for linear and integer Programing problems written in Python. The computational results indicate that the productivity loss has direct impact on the workforce hiring and firing.

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.

This paper presents one of the environmental problems occurring during underground mine closures: according to the underground coal mine closure programme in Germany, the behaviour of the land surface caused by flooding of the entire planned mining area – the Ruhr District – had to be addressed. It was highlighted that water drainage would need to be continuous; otherwise, water levels would rise again in the mining areas, resulting in flooding of currently highly urbanised zones. Based on the variant analysis, it was concluded that the expected uniform ground movements caused by the planned rise in the mining water levels (comprising a part of two concepts – flooding up to the level of –500 m a.s.l. and −600 m a.s.l.), in the RAG Aktiengesellschaft mines, will not result in new mining damage to traditional buildings. The analysis included calculations of the maximum land surface uplift and the most unfavourable deformation factor values on the land surface, important from the point of view of buildings and structures: tilt T, compressive strain ε– and tensile strain ε+. The impact of flooding on potential, discontinuous land surface deformation was also analysed.