The article describes an interpolation–analytical method of reconstruction of the IAPWS-95 equations of state and the modified Benedict–Webb–Rubin equations of state with 32 terms (mBWR32). The method enables us to provide the thermodynamic closure in 3D computational fluid dynamics (CFD) calculations of turbomachinery flows with real working media, such as steam and Organic Rankine Cycle (ORC) fluids. The described approach allows for the sufficient accuracy of 3D flow calculations and does not require a significant increase in computational cost over perfect gas calculations. The method is validated against experimental data from measurements and compared with computational results from the model using the Tammann equation of state. Three turbine blading systems are considered—a multi-stage configuration from a low-pressure cylinder of a large-power steam turbine and two ORC microturbines working with organic media HFE7100 and R227ea. The calculation results obtained using the described method of approximation of the IAPWS-95 and mBWR32 equations exhibit satisfactory agreement with the experimental data, considering pressures, temperatures and enthalpies in key sections, as well as turbine power and efficiency in a wide range of changing thermodynamic parameters. In contrast, the Tammann equation of state provides acceptable results only for relatively small changes of thermodynamic parameters.

Global energy consumption is increasing every year, and, despite their many negative impacts, fossil fuels are a major source of energy, but their reserves are gradually depleting. One of the promising but underutilized resources is plant biomass (phytomass). The main problem of plant biomass combustion is the low melting temperature of ash, but there are also problems with corrosion of heat exchangers and clogging of heat-exchanging surfaces. This work is concerned with the production of straw pellets in order to increase the melting temperature of ash by adding an additive. The paper sludge contains substances that can increase the melting point of ash and was therefore added to the pellet samples. This additive was mixed with straw in ratios from 90:10, 80:20 and 70:30 (straw/paper sludge). The use of paper sludge showed positive effects on increasing the melting temperature of the ash samples. The deformation temperature of the ash has already risen from 1020 to 1260 °C after the addition of 10% sludge, which is comparable to wood pellets.

In the last decade, particular attention has been paid to the organic Rankine cycle (ORC) power plant, a technology that implements a classical steam Rankine cycle using low-boiling fluid of organic origin. Depending on the specific application and the choice of the designer, the ORC can be optimized using one or several criteria. The selected objectives reflect various system performance aspects, such as: thermodynamic, economic, environmental or other. In this study, a novel criterion called exergy utilization index (XUI) is defined and used to maximize the utilization of an energy source in the ORC system. The maximization of the proposed indicator is equivalent to bring the heat carrier outlet temperature to the ambient temperature as close as possible. In the studied case, the XUI is applied along with the total heat transfer area of the system, and the multi-objective optimization is performed in order to determine the optimal operating conditions of the ORC. Moreover, to reveal a relationship between the XUI and important ORC performance indicators, a parametric study is conducted. Based on the results, it has been found that high values of the XUI (~80%) correspond to optimal values of exergy-based indicators such as: exergy efficiency, waste exergy ratio, environmental effect factor or exergetic sustainability index. Furthermore, the values of the XUI = 60%–80% are associated with beneficial economic characteristics reflected in a low payback period (<11.3 years). When considering the ecological aspect, the maximization of XUI has resulted in minimization of exergy waste to the environment. In general, the simple formulation and straightforward meaning make the XUI a particularly useful indicator for the preliminary evaluation and design of the ORC. Furthermore, the comparative analysis with respect to other well-known performance indicators has shown that it has a potential to be successfully applied as the objective function in the optimization of ORC power plants.

Power plants based on organic Rankine cycle (ORC) are known for their capacity in converting low and medium-temperature energy sources to electricity. To find the optimal operating conditions, a designer must evaluate the ORC from different perspectives including thermodynamic performance, technological limits, economic viability, and environmental impact. A popular approach to include different criteria simultaneously is to formulate a bi-objective optimization problem. This type of multi-objective optimization (MOO) allows for finding a set of optimal design points by defining two different objectives. Once the optimization is completed, the decision-making analysis shall be carried out to identify the final design solution. This study aims to develop a decision-making tool for facilitating the choice of the optimal design point. The proposed procedure is coded in MATLAB based on the commonly used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). By providing the capability to graphically identify the decisions taken, the tool developed in the study is called Tracking and Recognizing Alternative Design Solutions (TRADeS). Analysis of our data shows that certain regions of Pareto set points should be excluded from the design space. It was noted that in these regions a high rate at which one of the objectives moves away from its ideal value coincides with a low rate at which the second criterion approaches its ideal solution. Hence, it was recommended that the criteria weights corresponding to excluded regions of the Pareto set should be discarded when selecting the final design point. By comparing the results obtained using the proposed model to those of existing decision-making techniques, it was concluded that while the known approaches are appropriate for an easy and fast selection of the final design point, the presented procedure allows for a more comprehensive and well-rounded design. It was shown that our design tool can be successfully applied in the decision-making analysis for problems that aim at optimizing the ORC using two design criteria. Finally, the proposed software benefits from a generic structure and is easy to implement which will facilitate its use in other industrial applications.