Multi-objective analysis of an influence of a geothermal water salinity on optimal operating parameters in low-temperature ORC power plant

Jankowski, Marcin; Borsukiewicz-Gozdur, A.; Wiśniewski, S.; Hooman, Kamel

doi:10.1016/j.energy.2020.117666

Cited by 33 publications

(12 citation statements)

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Section: Bi-objective Optimizationmentioning

confidence: 99%

“…The detailed description of the decision variables and their limits can be found in [26]. Furthermore, the assumed operating parameters of the ORC are the same as those in [26] as listed in Table 1. To calculate basic thermodynamic parameters, including the mass flow rates or the temperature distributions of the fluids (geothermal water, working fluid, cold water), each component (or part of the component) of the ORC is considered as a control volume.…”

Section: Bi-objective Optimizationmentioning

confidence: 99%

“…Using the parameters determined in the previous step, the radial-inflow turbine is designed according to the procedure partly discussed in [26]. The key output variable at this stage is a turbine total-to-static efficiency which is calculated iteratively using the following formula:…”

Section: Bi-objective Optimizationmentioning

confidence: 99%

“…The optimization problem which was analyzed in [26] is recalled in this paper. Following the notation presented in Equations (7)-(9), the discussed BOO problem can be formulated as follows:…”

Section: Optimization Problemmentioning

confidence: 99%

“…The practical limitation imposed on the outlet temperature T gw2 of the geothermal water, which allows avoiding the silica oversaturation, is presented in Equation (23). A more detailed description of the optimization problem can be found in [26].…”

Section: Optimization Problemmentioning

confidence: 99%

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Development of Decision-Making Tool and Pareto Set Analysis for Bi-Objective Optimization of an ORC Power Plant

2020

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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.

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Section: Bi-objective Optimizationmentioning

confidence: 99%

Section: Bi-objective Optimizationmentioning

confidence: 99%

Section: Bi-objective Optimizationmentioning

confidence: 99%

Section: Optimization Problemmentioning

confidence: 99%

Section: Optimization Problemmentioning

confidence: 99%

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