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On dynamic modeling of industrial gas turbines based on low calorific value (LCV) gaseous fuels
(Springer, 2020-05-01)
Global energy demand is expected to increase in the following two decades. Operational flexibility of power generation systems is a key aspect when assessing potential solutions seeking to help meeting this expected increase in global energy demand. In low calorific value (LCV) gaseous fuels-fired power plants, it is paramount to consider the effects of the employed LCV/lean-gases-based fuels on the associated gas turbine systems and surrounding equipment. Moreover, because of the industrial processes usually occurring upstream these power plants, the fuel supply conditions change significantly during their normal operation. Gas turbines based on LCV gaseous fuels thus present a quite distinct engine behavior, and their modeling is therefore challenging. The dynamic modeling of such engines is the main focus of this work. Accordingly, the gas turbine dynamic model developed here is initially discussed, along with topics, such as gas turbine mass flow rates, cooling systems effectiveness and gas turbine component characteristics, relevant to the dynamic modeling of LCV fuels-based power plants. The use of the developed model for the simulation of an actual combined cycle power plant with cogeneration based on a LCV fuel is next highlighted. The main results show that overall acceptable agreements between computed parameters and actual power plant operating data are obtained. The corresponding average discrepancies range from 1 to 6%. In spite of the large number of factors directly influencing the numerical results obtained from the real-time simulations carried out, the power plant operating data trends are in general well reproduced by the computed results. The obtained results highlight in particular both the model applicability to operating scenarios presenting significant gradients in gas turbine characteristic parameters, and the need of including in the modeling key processes present in power plants actual operation.
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Experimental characterization of chalcopyrite ball mill grinding processes in batch and continuous flow processing modes to reduce energy consumption
(Elsevier, 2021-11-01)
A mineralogy, rheology, and energy consumption-based experimental characterization of chalcopyrite ball mill grinding processes, in both batch and continuous flow processing modes, is carried out in this work. Accordingly, chalcopyrite ore samples are initially characterized in terms of mineralogical composition, particle size distribution, grindability characteristics, and work index. Next, a rheological characterization of actual and lab-created chalcopyrite mineral-slurries is performed. Finally, an energy consumption-based characterization of several chalcopyrite ball mill grinding processes is performed. The results from the initial mineralogical characterization indicate ore samples featuring 5% chalcopyrite. These results also highlight that 80% of the particles present in the chalcopyrite head ore have a diameter smaller than 1386 μm. In addition, they indicate that the Bond ball mill work index is equal to 15.3 kWh/ton, which corresponds to a mineral with the presence of chalcopyrite. The rheological characterization related results indicate that all actual and lab-created mineral-slurries exhibit a shear thinning rheological behavior. These results also show that, because of the higher number of particle interactions, the slurries’ apparent viscosity increases with the increase in their solids content. Finally, the energy consumption-based characterization results emphasize that energy consumption is more significantly affected by mill speed than by slurry solids content. Indeed, for the same percentage of mass passing through a 200 mesh, it is found that the specific grinding energy decreases with both the increase in slurry solids concentration and the decrease in mill speed. The results obtained in this work are consistent with findings made in previous studies.
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Soot modeling in turbulent diffusion flames: review and prospects
(Springer Science and Business Media Deutschland GmbH, 2021-04-01)
This work reviews the state of the art of the main soot modeling approaches used in turbulent diffusion flames. Accordingly, after a short introduction about the subject addressed here, the main soot formation mechanisms are described next. This description provides the basis for the discussions about the different soot modeling techniques employed nowadays for soot predictions. Since combustion and radiation models have a significant impact on soot predictions, as a consequence of the strong coupling between chemistry, turbulence and soot formation, a general overview about these models is also provided. For the sake of clarity, the main soot formation models reviewed in this work are classified as semiempirical soot precursor models and detailed ones. Both advantages and disadvantages of the referred soot modeling approaches are properly discussed. In the last part of this review, comparative results obtained using some of the main soot models currently available are presented along with a discussion about the prospects for soot modeling in turbulent flames. Finally, some conclusions and references are provided. Overall, based on the literature reviewed, it is concluded that there is yet a long path to be followed before understanding first and having then a soot model able to properly describe the formation of this critical pollutant for a variety of situations of industrial interest.
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Numerical study and optimization of air-conditioning systems grilles used in indoor environments
(Springer Science and Business Media Deutschland GmbH, 2021-12-01)
The air flow distribution characterizing conditioned indoor environments obtained following conventional design methodologies does not always guarantee both thermal comfort and indoor air quality (IAQ) for all occupants. This occurs because this air flow distribution depends on factors such as the air supply conditions, the grilles and diffusers position/size, and the environmental conditions. Accordingly, the aim of this work is to numerically study the influence of the air supply conditions and the positioning and size of air-conditioning system grilles on the thermal comfort and IAQ in indoor environments. For this purpose, an optimization scheme involving genetic algorithms and computational fluid dynamics techniques has been initially developed. Three objective functions have been next separately optimized, predicted mean vote—predicted percentage dissatisfied (PMV-PPD), percentage of dissatisfied due to draft (PD) and air change effectiveness (ACE). The optimal indoor environment configuration based on the PPD produces the best results in terms of thermal comfort (PPD = 6.6%, PD = 18.7%) indexes. This configuration also features the second lowest energy consumption (774 W). Furthermore, the configuration based on the ACE both presents PMV (≤ − 1.3) and PD (≥ 20%) values far from the acceptability criteria given by the standards, and involves the highest energy consumption (1832 W). Notice that the optimization of thermal comfort indexes implies indirectly optimizing the related systems energy demand as well. When using indexes such as PPD and PD as the optimization objective functions indeed, the total energy consumption is also reduced.
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Development of an evolutionary artificial neural network-based tool for selecting suitable enhanced oil recovery methods
(Springer Science and Business Media Deutschland GmbH, 2022-04-01)
Enhanced oil recovery (EOR) methods are proven oil recovery processes that allow mature oil fields rejuvenating and increasing their production to economically profitable quantities. The main issues associated with the implementation in practice of these methods relate to their complexity, long implementation periods, economic risks, and high investments. As a result, companies interested in using these EOR methods carry out exhaustive selection processes before embarking on a project of this category. Accordingly, in this work, for selecting the most suitable EOR method for a given mature oil field, a reliable and user-friendly computational tool is developed. This tool allows identifying, based on seven reservoir fluid and rock parameters, the EOR method with the highest probability of implementation success. In the development of the referred EOR methods selection tool, an evolutionary artificial neural network-based approach is utilized, which allows the associated neural network architecture having a relatively high performance. Indeed, the referred tool allows identifying, with an accuracy of up to 94.0%, the EOR method with the highest probability of success. Compared to previous tools, the one developed in this work features a database involving a larger number (eight) of commercial EOR methods. The application of the developed tool to a specific mature field shows its usefulness for selecting the most suitable EOR method for the referred oil field.
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Design optimization methodology of small horizontal axis wind turbine blades using a hybrid CFD/BEM/GA approach
(Springer Science and Business Media Deutschland GmbH, 2022-06-01)
In this work, a comprehensive methodology for wind turbine blade design optimization is proposed. Accordingly, a numerical model based on computational fluid dynamics (CFD) has been initially coupled to a genetic algorithm (GA)-based optimization tool. Next, several optimization processes of a wind turbine blade mid-span airfoil have been carried out using the coupled tool. Finally, using the optimum airfoil and blade element momentum (BEM) theory, a wind turbine rotor has been designed and different rotor analyses at design and off-design point conditions have been carried out. Wind turbine blades operating at a 6 m/s wind speed and rated at a 5 kW power output are particularly considered. The use of aerodynamic characteristics of a blade mid-span airfoil for wind turbine blade design and optimization represents one the main features of the methodology developed here. The optimization results show that the determined optimum airfoil features better lift to drag ratios than a NACA 4412 one. From the design of the 5 kW power output wind turbine, blade lengths of 5.244 m were obtained. For the design point regarding a tip speed ratio of 6, a maximum power coefficient (Cp) of 0.4658 was computed. The aerodynamic analysis carried out at design and off-design conditions show consistent results compared to past works. The results confirm that a representative airfoil located between 25 and 90% of the blade span can be designed and optimized to obtain improved Cp and power output horizontal axis wind turbines.
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Parallel domain decomposition of a FEM-based tool for numerical modelling mineral slurry-like flows
(Taylor and Francis Ltd., 2022-01-01)
The main parallelisation related features of a computational tool based on the finite element method (FEM) for the numerical modelling of mineral-slurry like flows are described in this work. In particular, both the domain decomposition method (DDM) and the processes communication strategy employed are discussed in detail. The DD algorithm is based on the iterative update of the boundary conditions imposed on the interfaces between subdomains, the so-called transmission conditions. Due to its versatility in several parallel architectures, the message-passing standard used here is the message passing interface (MPI) one. Since mineral-slurries rheology may change according to the prevailing local flow conditions, Newtonian and non-Newtonian viscous fluids are considered in this work. Indeed, both Newtonian and non-Newtonian laminar flows are numerically studied in two well-known canonical configurations usually found in mineral-slurry transport. The main results show that the parallel FEM based tool is capable of carrying out high-fidelity numerical simulations of mineral-slurry like flows. Finally, in all numerical simulations performed, relatively good speedups were obtained.
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Mathematical modelling of coupled and decoupled water electrolysis systems based on existing theoretical and experimental studies
(Elsevier Ltd, 2022-05-08)
Since it has the potential to significantly reduce gaseous emissions in the near future, electrolytic hydrogen production using electricity generated from renewable energy sources, such as solar radiation, is key. Water splitting processes occurring in electrolyzer cells are complex phenomena. Therefore, to fully realize such processes, different technologies have been accounted for. The focus of this work is on the mathematical modeling of three different electrolyzer cells related technologies, (i) alkaline, (ii) proton exchange membrane (PEM), and (iii) decoupled water splitting. Accordingly, several existing mathematical models for alkaline and PEM electrolyzers are initially revised. Next, a comprehensive mathematical model capable of properly predicting the performance of the three electrolyzer technologies accounted for here is proposed. The developed mathematical models are then used to predict the behavior of electrolyzer cells under different operation conditions. The obtained results are finally compared in terms of cell voltages, cell efficiencies, and hydrogen production rates. When compared to other results available in the literature, the cell voltage ones obtained using the new proposed model are in relatively good agreement. Specifically, for a current density range of 0–200 mA/cm2, cell pressures between 10 and 40 bar, and a cell temperature of 60 °C, cell voltage requirements are between 1.25 and 1.75 V, with the E-TAC technology performing better than the other two ones accounted for. In addition, for current densities of more than 100 mA/cm2 and cell pressures below 5 bar, Faraday's efficiencies are almost the same for all three technologies, i.e., about 95%. However, for higher cell pressures, significant differences in Faraday's efficiency appear. Based on the work carried out, it is concluded that developing a sound mathematical model is crucial both for the comprehension of coupled and decoupled water electrolysis-related processes and for their use in the simplest and most reliable way.
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Genetic algorithms-based size optimization of directly and indirectly coupled photovoltaic-electrolyzer systems
(Elsevier Ltd, 2022-10-15)
Since relatively high costs and low efficiencies are usually associated with photovoltaic-electrolyzer (PV-EL) systems, the coupling of a PV system to an EL one is a critical aspect when sizing PV-EL systems. Accordingly, using a genetic algorithms-based optimization approach and considering a hydrogen production target of 100 g per day, the size of different directly and indirectly coupled PV-EL systems is optimized in this work. The referred optimization processes are carried out for five PV-EL system configurations, one related to directly coupled systems and four (one per each DC/DC converter topology accounted for) to indirectly coupled ones. In addition, seeking to maximize hydrogen production, minimize losses, and increase system efficiency, four objective functions are assessed. Some of the results highlight that, when using system cost and overall efficiency as objective functions, properly sized indirectly coupled PV-EL systems feature lower implementation costs than directly coupled ones. In addition, the differences in the overall efficiencies characterizing the optimum directly and indirectly coupled PV-EL systems so determined are relatively small (>1%). One of the original contributions of this work relates to the fact that this is one of the first works dealing with optimization processes of both directly and indirectly coupled PV-EL systems, where optimum system configurations are compared with each other.
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Clean energy transition in Peru: A green hydrogen perspective
(2022-12-22)
For anyone concerned about climate change, fostering the energy transition from fossil-based to low- or zero-carbon energy sources is a must. In this context, this work provides a brief overview of the clean energy transition in Peru, accounting for a green hydrogen perspective. Accordingly, after the corresponding introduction to the subject, the current situation of renewable energies in Peru is highlighted, along with their historical evolution during the last two decades or so, and the prospects for these more environmentally friendly energy sources in the following years. Next, the potential for renewable energy production in Peru is discussed, with especial emphasis on hydropower, wind, solar, and biomass. Finally, green hydrogen and its potential to contribute to the energy transition in Peru is addressed. A particular emphasis is put in this case on the production of green hydrogen and its applications in Peru and worldwide. From the discussions carried out in this work, it is concluded that, although Peru has a large potential to become a green hydrogen producing and exporting country, there is still a long way to go before Peru can achieve the desired carbon neutrality in the coming decades.

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