The thermokinetics of sassafras wood pyrolysis were studied via thermogravimetric inverse modeling. Model-free combined with model-fitting methods were used to explore the optimal kinetics. Results ...indicate the obtained averaged activation energy and pre-exponential factor are 215.12 kJ/mol and 8.65 × 1017s−1, while the reaction model in charge is g(α) = -ln(1-α)1/3. In the following, Kissinger-based K–K method was adopted to separate the single reaction kinetics for each pseudo components. The analysis of K–K method provided the basis of the search range for each kinetic parameters. Finally, a new optimization algorithm was put forward, and the multi parallel reactions scheme incorporated into the Grey Wolf Optimization coupled with the Least Squared Fitting procedure was adopted to perform the optimization. The predicted MLR curves based on the simultaneously optimized kinetic parameters fit well with the experimental data not only at heating rates of 10, 20 and 30 K/min, but also at 5 and 40 K/min which were not used in the optimization. Such results indicated the excellent applicability of the optimized parameters and efficiency of the hybrid optimization procedure. Results from the present work could be a guidance for further biomass pyrolysis evaluation under more complex practical scenarios.
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•Model-free combined with model-fitting methods were used for kinetics study.•The most suitable model for sassafras wood pyrolysis was g(α) = -ln(1-α)1/3.•A Grey Wolf Optimization based hybrid fitting procedure was proposed.•Optimization model integrated with TPR schemes improves efficiency and accuracy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Studying two-stage assembly flow shop scheduling problem with release time of jobs.•Finding the jobs sequence such that completion of the last processed job (makespan) is minimized.•Proposing a ...noble algorithm called Grey Wolf Optimizer (GWO) inspired from living and haunting behavior of wolves for solving the studied problem.•Developing a lower bound as well as several heuristic algorithms.
This article addresses the two-stage assembly flow shop scheduling problem with release time of jobs which is applicable in many industrial areas, such as computer manufacturing industry, fire engine assembly plant, etc. The first stage of the problem is called “fabrication stage” and includes identical parallel machines while the second stage is called “assembly stage” with a single assembly machine. The jobs have components which they need to be firstly processed at the fabrication stage and then they should go under assembly operation at the assembly stage. The goal of this research is to find the jobs sequence such that completion time of the last processed job is minimized. For this problem, several heuristic techniques as well as a lower bound are developed. Also, a novel meta-heuristic algorithm called Grey Wolf Optimizer (GWO), which is inspired by living and haunting behavior of wolves, is then proposed. An extensive statistical analysis is conducted to compare the performance of the employed algorithms on randomly generated instances. The obtained results indicate that the methods based on Johnson’s algorithm yield better results than the other heuristic algorithms. Moreover, the consequences show that the proposed LB is tight enough. Finally, the experiments show that the GWO outperforms the other employed well-known meta-heuristic algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
493.
In wolf country Yuskavitch, James
2015., 2015, 2015-02-03
eBook
In Wolf Country tells the story of the first groups of wolves that emigrated from reintroduced areas in Idaho to re-colonize their former habitat in the Pacific Northwest, how government officials ...prepared for their arrival, and the battles between the people who welcome them and the people who don't, set against the backdrop of the ongoing political controversy surrounding wolf populations in the Northern Rockies. The political maneuvering and intense controversy that has defined wolves' recovery in the West makes this a compelling and timely read.
The current stability studies of the hydraulic turbine regulation system (HTRS) mostly adopt the linear hydro-turbine model ignoring its strong nonlinearity, leading to insufficient disclosure of the ...true characteristics of the HTRS and also causing great inconvenience to the controller parameter tuning. To address this issue, based on the Hopf bifurcation theory, bisection method, and stability criterion, this paper proposes an algorithm (HBBSC) for determining the controller parameter constraint considering the nonlinearity of the hydro-turbine. Firstly, the nonlinear model of the hydro-turbine is constructed based on the model reconstruction strategy (NNGW) combining the backpropagation neural network (BPNN) with the improved grey wolf optimization algorithm (IGWO) to obtain an accurate nonlinear HTRS numerical simulation platform under the power control mode (PCM) and frequency control mode (FCM). Then, the HBBSC-based quantitative calculation procedure of stability region constraint is introduced in detail. Further, in a case study of stability region calculation of HTRS, the HBBSC is applied to calculate the stability region constraint, and the HBBSC-based stability region is verified through a simulation platform. Finally, the stability region of complex HTRS under all operating conditions is calculated based on HBBSC. The results indicate that the HBBSC can replace the traditional methods for stability region calculation during stability analysis of HTRS, outperforming the latter in accuracy and reliability.
•A model reconstruction strategy for hydraulic turbine is proposed.•An accurate nonlinear model of the hydraulic turbine regulation system is constructed.•An algorithm with fast computing speed for determining the controller parameter constraint is proposed.•The stability of the hydraulic turbine regulation system is quantitatively analyzed under all operating conditions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Context
. The mass loss of helium-burning stars, which are partially or completely stripped of their outer hydrogen envelope, is a catalyst of the cosmic matter cycle and decisive ingredient of ...massive star evolution. Yet, its theoretical fundament is only starting to emerge with major dependencies still to be uncovered.
Aims
. A temperature or radius dependence is usually not included in descriptions for the mass loss of classical Wolf-Rayet (cWR) stars, despite being crucial for other hot star wind domains. We thus aim to determine whether such a dependency will also be necessary for a comprehensive description of mass loss in the cWR regime.
Methods
. Sequences of dynamically consistent stellar atmosphere models were calculated with the hydrodynamic branch of the PoWR code along the temperature domain, using different choices for the luminosity, mass, and surface abundances. For the first time, we allowed nonmonotonic velocity fields when solving the hydrodynamic equation of motion. The resulting velocity structures were then interpolated for the comoving-frame radiative transfer, ensuring that the main wind characteristics were preserved.
Results
. We find a strong dependence of the mass-loss rate with the temperature of the critical/sonic point which mainly reflects the different radii and resulting gravitational accelerations. Moreover, we obtain a relation between the observed effective temperature and the transformed mass-loss rate
Ṁ
t
which seems to be largely independent of the underlying stellar parameters. The relation is shifted when different density contrasts are assumed for the wind clumping. Below a characteristic value of log (
Ṁ
t
M
⊙
yr
−1
) −4.5, the slope of this relation changes and the winds become transparent for He
II
ionizing photons.
Conclusions
. The mass loss of cWR stars is a high-dimensional problem but also shows inherent scalings which can be used to obtain an approximation of the observed effective temperature. For a more realistic treatment of cWR stars and their mass loss in stellar evolution, we recommend the inclusion of a temperature dependency and ideally the calculation of hydrodynamic structure models.
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Accurate power load prediction is beneficial to the efficient use of electric energy and the orderly development of power systems. Given the strong volatility and complexity of power load series, a ...hybrid load forecasting method based on multiscale and mesoscale information fusion, signal decomposition, model optimization, and bi-long-short-term memory (BiLSTM) is proposed. Firstly, the load sequence is analyzed on different time scales, and the extracted multi-scale information and mesoscale information are fused to improve the perception ability. Secondly, the empirical wavelet transform (EWT) with adaptive decomposition ability is used to decompose the sequence and extract the rich feature information. Thirdly, the complexity, volatility, and uncertainty of each mode component were analyzed, the data features were fully mined, and the feature fusion was carried out by the TOPISIS evaluation method. The BiLSTM model and the GWO-BiLSTM model are used to predict the low-frequency component and the high-frequency component, respectively. The optimization of Grey Wolf optimization (GWO) algorithm can improve the BiLSTM model's ability to learn long-term time series. Finally, the analysis of application examples shows that compared with various prediction models, the prediction error of mixed model EWT-SGEO-BiLSTM is the smallest, MAPE is as low as 1.07 %, and goodness of fit R2 is 0.99 which verifies the accuracy and applicability of the intelligent model.
•An electric load forecasting method based on multiscale and mesoscale feature extraction was proposed.•Signal decomposition, model optimization and bi-long-short-term memory are employed to design the proposed model.•The results also indicate the proposed model is superior to other multiple prediction models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•We propose a new approach for feature selection in hyperspectral image classification.•The problem of band selection is reformulated as a combinatorial problem.•We design a new ...objective function which takes into account two term, the classification error rate and the class separability distance.•To optimize the objective function, we propose to use a new meta-heuristic called Gray Wolf Optimizer.•The experiments have been conducted in three widely used hyperspectral images and compared with other approaches.
In this paper, we propose a new optimization-based framework to reduce the dimensionality of hyperspectral images. One of the most problems in hyperspectral image classification is the Hughes phenomenon caused by the irrelevant spectral bands and the high correlation between the adjacent bands. The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate. We propose to reformulate the problem of band selection as a combinatorial problem by modeling an objective function based on class separability measures and the accuracy rate. We use the Gray Wolf Optimizer, which is a new meta-heuristic algorithm more efficient than Practical Swarm Optimization, Gravitational Search Algorithm, Differential Evolution, Evolutionary Programming and Evolution Strategy. The experimentations are performed on three widely used benchmark hyperspectral datasets. Comparisons with the state-of-the-art approaches are also conducted. The analysis of the results proves that the proposed approach can effectively investigate the spectral band selection problem and provides a high classification accuracy rate by using a few samples for training.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the ...distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows’ memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
500.
Yellowstone after Wolves SMITH, DOUGLAS W; PETERSON, ROLF O; HOUSTON, DOUGLAS B
Bioscience,
04/2003, Volume:
53, Issue:
4
Journal Article
Peer reviewed
With gray wolves restored to Yellowstone National Park, this ecosystem once again supports the full native array of large ungulates and their attendant large carnivores. We consider the possible ...ecological implications of wolf restoration in the context of another national park, Isle Royale, where wolves restored themselves a half-century ago. At Isle Royale, where resident mammals are relatively few, wolves completely eliminated coyotes and went on to influence moose population dynamics, which had implications for forest growth and composition. At Yellowstone, we predict that wolf restoration will have similar effects to a degree, reducing elk and coyote density. As at Isle Royale, Yellowstone plant communities will be affected, as will mesocarnivores, but to what degree is as yet undetermined. At Yellowstone, ecosystem response to the arrival of the wolf will take decades to unfold, and we argue that comprehensive ecological research and monitoring should be an essential long-term component of the management of Yellowstone National Park.
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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