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Working fluid selection for Organic Rankine Cycles based on continuous-molecular targets
... Uncited references [21]; [22]. ...
We simultaneously optimize both the design and operation of an air-cooled geothermal organic Rankine cycle, maximizing total annual return (TAR), while considering multiple operating scenarios based on different ambient temperatures. In order to accurately capture realistic off-design behavior of the heat exchangers and turbine, as well as for the overall system, we incorporate component models that consider performance variations with both size and operating conditions. We employ a hybrid mechanistic data-driven modeling approach, involving artificial neural networks (ANNs) as surrogate models for accurate fluid properties, as well as for intermediate expressions for which ANNs improve tractability of the optimization problem. We demonstrate the importance of considering multiple operating conditions within design problems and propose a methodology for formulating and solving such problems globally, using our open-source solver MAiNGO.
New heat conversion technologies need to be developed and improved to take advantage of the necessary increase in the supply of renewable energy. The Organic Rankine Cycle is well suited for these applications, mainly because of its ability to recover low-grade heat and the possibility to be implemented in decentralized lower-capacity power plants. In this paper, an overview of the different ORC applications is presented. A market review is proposed including cost figures for several commercial ORC modules and manufacturers. An in-depth analysis of the technical challenges related to the technology, such as working fluid selection and expansion machine issues is then reported. Technological constraints and optimization methods are extensively described and discussed. Finally, the current trends in research and development for the next generation of Organic Rankine Cycles are presented.
In this work, we propose a new predictive entropy-scaling approach for Newtonian shear viscosities based on group contributions. The approach is based on Rosenfelds original work [ Rosenfeld, Y. Phys. Rev. A 1977, 15, 2545-2549 ]. The entropy scaling is formulated as third order polynomial in terms of the residual entropy as calculated from a group-contribution perturbed-chain polar statistical associating fluid theory (PCP-SAFT) equation of state. In this study, we analyze the course of entropy scaling parameters within homologous series and suggest suitable mixing rules for the parameters of functional groups. The viscosity of nonpolar, of polar, and of self-associating (hydrogen bonding) components are considered. In total, 22 functional groups are parametrized to viscosity data of 110 pure substances, from 12 different chemical families. The mean absolute relative deviations (MADs) to experimental viscosity data are typically around 5%. For three chemical families, namely, branched alkanes, 1-alcohols, and aldehydes, we obtain higher MADs of about 10%. Water is correlated with a MAD-value of 3.09%.
Solvent-based separation systems have a substantial potential for improvement when the solvent and the process conditions are optimized simultaneously. The fully integrated design problem, however, leads to an optimization problem of prohibitive size and complexity owing to the many discrete degrees of freedom in selecting a solvent and the nonlinear nature of the process models. We here implement and extend the method of continuous molecular targeting–computer-aided molecular design (CoMT–CAMD) for the solvent and process optimization of a precombustion CO2-capture system with physical absorption. CoMT–CAMD is a deterministic procedure that does not require a preselection of solvent molecules. The process topology considered in our study includes all major process operations of an existing CO2-capture system: multistage absorption, desorption (two flash desorption stages with gas recycle) and CO2 compression. We measure the process performance with a single economic objective function. The objective function captures the process trade-offs and evaluates the potential process-solvent on a common basis. The solvent is represented as the pure component parameters of the perturbed-chain statistical associating fluid theory (PC-SAFT). The optimization problem is formulated with the pure component parameters of the solvent (PC-SAFT parameters) and with the process variables as degrees of freedom. Necessary auxiliary properties of the optimized solvent such as the ideal gas heat capacity and the molar mass are predicted with quantitative structure property relationship models, based on the pure component PC-SAFT parameters. As a result, one gets a unified thermodynamic framework for fluid properties based on the PC-SAFT model. With CoMT–CAMD we obtain a list of the best performing physical solvents for the considered CO2-capture application. The resulting list of best performing solvents contains state-of-the-art solvents and new green solvent molecules.
Computer-aided molecular design allows generating novel fluids fulfilling a set of target properties. An integrated design of fluid and process directly employs a process-based objective function. In this work, we solve the integrated process and fluid design problem using the continuous-molecular targeting computer-aided molecular design (CoMT-CAMD) framework. CoMT-CAMD exploits the molecular picture underlying the PC-SAFT equation of state. In the simultaneous optimization of process and fluid, relaxed pure component parameters allow for an efficient optimization. The result is a hypothetical optimal target fluid. In previous work, fluids showing similar performance as the target fluid were obtained from a mapping onto a database. Here, we integrate computer-aided molecular design to realize the actual design of novel fluids. The resulting method for fluid design is based on a group-contribution method for the PC-SAFT parameters (GPC-SAFT) and applied to the design of working fluids for Organic Rankine cycles and solvents for CO2 capture.
Chemical reaction engineering, process engineering, and product engineering models are used for design and analysis. Often, transport coefficient models are needed in equipment and in-situ models to account for the importance of momentum, heat, and mass transfer. Previous work+ demonstrated a novel component-based reference equation of state approach for correlating self-diffusion coefficient and viscosity over the entire fluid region (liquid, gas, and critical fluid). In this paper, a segment-based approach is used to extend the previous work+ from a limited number of individual component correlations to a predictive fluid viscosity correlation for a class of components consisting of n-alkanes, up to 1300 molecular weight, covering a wide range of components, temperatures, and pressures.
A scaled segment viscosity-segment residual entropy correlation (V-S model) was introduced and evaluated here. PC-SAFT segment parameters and residual entropy were used in a correlation model linking viscosity to the PC-SAFT equation of state. Experimental evaluation of this V-S model used 3122 data points for eighteen n-alkanes, ranging from methane up to 2390 molecular weight linear polyethylene. Temperatures ranged from 96 °K to 650 °K, and pressures ranged from 10-4 atmospheres to 4990 atmospheres. The conditions studied are relevant to oil and gas reservoir engineering and other in-situ processes.
Based on this work, covering the entire fluid region, the V-S model was found to result in a group correlation squared (R2) of -0.998 and group average absolute deviation (AAD) of 3.9%. Individual viscosity segment correlation parameters (Bseg and Aseg) were fitted to molecular weight and used in the predictive mode. In the predictive mode, a group AAD of 6.7% was obtained for n-alkanes from methane up to 1300 molecular weight linear polyethylene, over the entire fluid region.
The scaled segment viscosity-segment residual entropy model introduced here has potential for a much broader range of applications. In addition, this model would be easy to embed in existing in-house and commercial simulators to provide predictive properties and rate-based modeling capability. + Novak, http://www.bepress.com/ijcre/vol9/A63
We examine the performance of the two rank order correlation coefficients (Spearman's rho and Kendall's tau) for describing the strength of association between two continuously measured traits. We begin by discussing when these measures should, and should not, be preferred over Pearson's product-moment correlation coefficient on conceptual grounds. For testing the null hypothesis of no monotonic association, our simulation studies found both rank coefficients show similar performance to variants of the Pearson product-moment measure of association, and provide only slightly better performance than Pearson's measure even if the two measured traits are non-normally distributed. Where variants of the Pearson measure are not appropriate, there was no strong reason (based on our results) to select either of our rank-based alternatives over the other for testing the null hypothesis of no monotonic association. Further, our simulation studies indicated that for both rank coefficients there exists at least one method for calculating confidence intervals that supplies results close to the desired level if there are no tied values in the data. In this case, Kendall's coefficient produces consistently narrower confidence intervals, and might thus be preferred on that basis. However, if there are any ties in the data, irrespective of whether the percentage of ties is small or large, Spearman's measure returns values closer to the desired coverage rates, whereas Kendall's results differ more and more from the desired level as the number of ties increases, especially for large correlation values.
Organic Rankine Cycles (ORCs) generate power from low temperature heat. To make the best use of the diverse low temperature heat sources, the cycle is tailored to each application. The objective is to maximize process performance by optimizing both process parameters and the working fluid. Today, process optimization and working fluid selection are typically addressed separately in a two-step approach: working fluids are selected using heuristic knowledge; subsequently, the process is optimized. Such an approach can lead to suboptimal solutions, since the optimal fluid might be excluded by the heuristics. We therefore present a framework for the holistic design of ORCs enabling the simultaneous optimization of the process and the working fluid based on process performance. The simultaneous optimization is achieved by exploiting the rich molecular picture underlying the PC-SAFT equation of state in a continuous-molecular targeting approach (CoMT-CAMD). To allow for the prediction of caloric properties, a quantitative structure–property relationship (QSPR) for the ideal gas heat capacity is proposed that relies on pure component parameters of PC-SAFT. The framework is used for the optimization of a geothermal ORC in a case study. A sound holistic design of process and working fluid is achieved.
This work presents a Computer-Aided Molecular Design (CAMD) method for the synthesis and selection of binary working fluid mixtures used in Organic Rankine Cycles (ORC). The method consists of two stages, initially seeking optimum mixture performance targets by designing molecules acting as the first component of the binaries. The identified targets are subsequently approached by designing the required matching molecules and selecting the optimum mixture concentration. A multiobjective formulation of the CAMD-optimization problem enables the identification of numerous mixture candidates, evaluated using an ORC process model in the course of molecular mixture design. A nonlinear sensitivity analysis method is employed to address model-related uncertainties in the mixture selection procedure. The proposed approach remains generic and independent of the considered mixture design application. Mixtures of high performance are identified simultaneously with their sensitivity characteristics regardless of the employed property prediction method.
The paper presented a working fluid selection and parametric optimization using a multi-objective optimization model by simulated annealing algorithm. The screening criteria considered included heat exchanger area per unit power output (A/Wnet) and heat recovery efficiency (Ф). The independent parameters are the evaporation and condensation pressures, working fluid and cooling water velocities in tubes. A comparison of optimized results for 13 working fluids shows that boiling temperature of working fluids will greatly affect the optimal evaporating pressure. R123 is the best choice for the temperature range of 100–180°C and R141b is the optimal working fluid when the temperature higher than 180°C. When the exhaust temperature ranges from 100°C to 220°C, the optimal pinch point at evaporator is about 15°C. Economic characteristic of system decreases rapidly with heat source temperature decrease. When the heat source temperature is lower than 100°C, ORC technology is uneconomical.
In this study the option of combined heat and power generation was considered for geothermal resources at a temperature level below 450 K. Series and parallel circuits of an Organic Rankine Cycle (ORC) and an additional heat generation were compared by second law analysis. Depending on operating parameters criteria for the choice of the working fluid were identified. The results show that due to a combined heat and power generation, the second law efficiency of a geothermal power plant can be significantly increased in comparison to a power generation. The most efficient concept is a series circuit with an organic working fluid that shows high critical temperatures like isopentane. For parallel circuits and for power generation, fluids like R227ea with low critical temperatures are to be preferred.
An integrated design of processes and solvents is a prerequisite for achieving truly optimized solvent-based processes. However, solving the full integrated problem in a single optimization is usually not possible even for a predefined process topology due to the required discrete choices between molecular structures. Current approaches therefore mostly decompose the integrated problem into a process design and a molecular-design subproblem. The interaction between these subproblems is usually limited in practice, and a direct link between process performance and molecular characteristics of the solvent is not achieved. In this work, a novel methodology for the integrated process and molecular design problem is suggested where the discrete molecular decisions in the integrated design problem are circumvented by defining a hypothetical molecule. The approach is building upon a molecular-based thermodynamic model, where the parameters representing a molecule are treated as continuous. These parameters are optimized together with other process parameters, leading to an ideal hypothetical target molecule (represented by a set of parameters) and a corresponding optimized process. Only in a subsequent step, the parameters of the thermodynamic model representing the hypothetical molecule are mapped onto an existing optimal solvent. The method is illustrated for the design of solvents for carbon dioxide capture where the benefits of the integrated design approach are demonstrated. The perturbed-chain-polar-statistical-associating-fluid theory (PCP-SAFT) equation of state is used as a thermodynamic model. The framework introduced is generic in nature and thus applicable beyond the study of solvents to the integrated design of materials and processes in general.
A modified SAFT equation of state is developed by applying the perturbation theory of Barker and Henderson to a hard-chain reference fluid. With conventional one-fluid mixing rules, the equation of state is applicable to mixtures of small spherical molecules such as gases, nonspherical solvents, and chainlike polymers. The three pure-component parameters required for nonassociating molecules were identified for 78 substances by correlating vapor pressures and liquid volumes. The equation of state gives good fits to these properties and agrees well with caloric properties. When applied to vapor−liquid equilibria of mixtures, the equation of state shows substantial predictive capabilities and good precision for correlating mixtures. Comparisons to the SAFT version of Huang and Radosz reveal a clear improvement of the proposed model. A brief comparison with the Peng−Robinson model is also given for vapor−liquid equilibria of binary systems, confirming the good performance of the suggested equation of state. The applicability of the proposed model to polymer systems was demonstrated for high-pressure liquid−liquid equilibria of a polyethylene mixture. The pure-component parameters of polyethylene were obtained by extrapolating pure-component parameters of the n-alkane series to high molecular weights.
This work presents the first approach to the systematic design and selection of optimal working fluids for Organic Rankine Cycles (ORCs) based on computer aided molecular design (CAMD) and process optimization techniques. The resulting methodology utilizes group contribution methods in combination with multi-objective optimization technology for the generation of optimum working fluid candidates. Optimum designs of the corresponding ORC processes are then developed for the comprehensive set of molecules obtained at the CAMD stage, in order to identify working fluids that exhibit optimum performance in ORCs with respect to important economic, operating, safety and environmental indicators. The proposed approach is illustrated with a case study in the design of working fluids for a low-temperature ORC system. Particular attention is paid to safety and environmental characteristics such as flammability, toxicity, ozone depletion and global warming potential. The methodology systematically identified both novel and conventional molecular structures that enable optimum ORC process performance.
In small solid biomass power and heat plants, the ORC is used for cogeneration. This application shows constraints different from other ORC. These constraints are described and an adapted power plant design is presented. The new design influences the selection criteria of working fluids. A software has been developed to find thermodynamic suitable fluids for ORC in biomass power and heat plants. Highest efficiencies are found within the family of alkylbenzenes.
Theoretical performances as well as thermodynamic and environmental properties of few fluids have been comparatively assessed for use in low-temperature solar organic Rankine cycle systems. Efficiencies, volume flow rate, mass flow rate, pressure ratio, toxicity, flammability, ODP and GWP were used for comparison. Of 20 fluids investigated, R134a appears as the most suitable for small scale solar applications. R152a, R600a, R600 and R290 offer attractive performances but need safety precautions, owing to their flammability.
Comparative study regarding the methods of interpolation
- P D Dumitru
- M Plopeanu
- D Badea
Dumitru, P. D., Plopeanu, M., and Badea, D. (2013). Comparative study regarding the methods of interpolation. In 1st European Conference of Geodesy & Geomatics Engineering 2013, Recent Advanced in
Geodesy and Geomatics Engineering-Conference Proceedings, 45-52pp, Antalya, Oct. 8, volume 10.