This study investigates the thermal degradation mechanisms and kinetics of TDI/MDI-based flexible polyurethane foam (FPUF) under nitrogen and air atmospheres, employing thermogravimetric (TG) analysis in conjunction w...
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This study investigates the thermal degradation mechanisms and kinetics of TDI/MDI-based flexible polyurethane foam (FPUF) under nitrogen and air atmospheres, employing thermogravimetric (TG) analysis in conjunction with TG-FTIR technique. The information gleaned from these analysis is used to construct kinetic model based on model-free approaches coupled with the shuffledcomplexevolution optimization algorithm. The results uncovered the five-step complex thermal degradation process of TDI/MDI-based FPUF compared to TDI-based or MDI-based polyurethane in both atmospheres. In nitrogen atmosphere, this involve the dissociation of TDI- and MDI-based urethane bonds, breakage of recombined urea bonds, random chain scission of polyol, and further degradation of incompletely pyrolyzed solid products. In air atmosphere, the process emcompasses dissociation of TDI- and MDI-based urethane linkages, accelerated oxidation of polyol chains, consumption of urea groups, and combustion of partially oxidized intermediates. This work also emphasizes the limitations of solely relying on TG tests for kinetic modeling, necessitating the integration of TG tests and TG-FTIR technique to obtain a comprehensive understanding of degradation mechanisms. The significance of the acquired insights lies in their potential to reshape the design of thermo-chemical reactors, resulting in heightened resource recovery, diminished environmental impact, and a stride towards a circular economy within the polyurethane industry.
Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In...
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Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified shuffledcomplexevolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffledcomplexevolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions. (C) 2010 Elsevier Inc. All rights reserved.
The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to ov...
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The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to overcome the limitations of classical optimization methods, researchers have proposed a wide variety of meta-heuristics for searching near-optimum solutions to problems. Among the existing meta-heuristic algorithms, a relatively new optimization paradigm is the shuffledcomplexevolution at the University of Arizona (SCE-UA) which is a global optimization strategy that combines concepts of the competition evolution theory, downhill simplex procedure of Nelder-Mead, controlled random search and complex shuffling. In an attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, in this paper is proposed a hybrid SCE-UA approach. The proposed hybrid algorithm is the combination of SCE-UA (without Nelder-Mead downhill simplex procedure) and a pattern search approach, called SCE-PS, for unconstrained optimization. Pattern search methods are derivative-free, meaning that they do not use explicit or approximate derivatives. Moreover, pattern search algorithms are direct search methods well suitable for the global optimization of highly nonlinear, multiparameter, and multimodal objective functions. The proposed SCE-PS method is tested with six benchmark optimization problems. Simulation results show that the proposed SCE-PS improves the searching performance when compared with the classical SCE-UA and a genetic algorithm with floating-point representation for all the tested problems. As evidenced by the performance indices based on the mean performance of objective function in 30 runs and mean of computational time, the SCE-PS algorithm has demonstrated to be effective and efficient at locating best-practice optimal solutions for unconstrained optimization. (C) 2011 IMACS. Published by Elsevier B.V
Inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this paper, we proposed and tested a new Rayleigh wave dispersion cur...
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Inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this paper, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme called the shuffledcomplexevolution (SCE) approach, which is based on a synthesis of four global optimization strategies that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches;(b) the strategy of clustering;(c) systematic evolution of a complex of points spanning the space, in the direction of global improvement;and (d) competitive evolution. Incorporating these four global optimization strategies into the inverse procedure greatly enhances the performance of the SCE method because these steps not only can effectively locate the promising areas in the solution space for a global minimum but also avoid its wandering near the global minimum in the final stage of search. The proposed inverse procedure was applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface shear (S)-wave velocity profiles. The calculation efficiency and stability of the inversion scheme are tested on four synthetic models and a real-world example from a waste disposal site in NE Italy. A comparative analysis with genetic algorithms (GA), marginal posterior probability density (MPPD) estimation, and simulated annealing (SA) is also made in the present study to further evaluate the performance of the proposed approach. Results from both synthetic and actual field data demonstrate that shuffled complex evolution algorithm promises to be robust, effective, and efficient for high-frequency surface wave analysis. (C) 2012 Elsevier Ltd. All rights reserved.
The high transportation cost of the parts of end-of-life vehicles (ELVs) has received a great deal of attention lately, and different approaches for solving the problem have been taken. In this paper, a bi-level progr...
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The high transportation cost of the parts of end-of-life vehicles (ELVs) has received a great deal of attention lately, and different approaches for solving the problem have been taken. In this paper, a bi-level programming model is developed to decompose the distribution center location problem for the parts of ELVs in order to obtain a trade-off between the location cost of the distribution center and the transportation cost. The upper level determines the sites of distribution centers and the lower level represents the delivery routes under any location pattern of candidate sites of distribution centers. The proposed model is illustrated by real data and the results demonstrate that it can be an effective method to determinate a distribution center for ELVs.
The main purpose of the colored noise effect in parameter estimation for a linear model is to make the estimators tend to the true values as closely as possible. The differential hydrological grey model with dual seri...
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The main purpose of the colored noise effect in parameter estimation for a linear model is to make the estimators tend to the true values as closely as possible. The differential hydrological grey model with dual series is adopted in this study. The least squares estimation of model parameters with the colored noise effect is applied. to improve the parameter identification. By using the shuffledcomplexevolution (SCE) algorithm, the parameter derived from the least squares estimation with the colored noise effect is optimized. Then, the degree of the colored noise effect affecting the parameter estimation of a linear model is investigated. The applicability of the proposed procedure to two watersheds in Taiwan is verified and the simulated results with and without the colored noise effect are compared. From the theoretical investigation and discussion, the superiority of the parameter estimation with the colored noise effect is proved. That colored noise effect plays an important role not only in parameter estimation, but also in simulation of the rainfall-runoff process, is also verified. (C) 1998 Elsevier Science B.V. All rights reserved.
Bat algorithm (BA) is a powerful nature-inspired swarm algorithm which finds applicability to a diverse range of problem domains. Though it is efficient, it suffers from two handicaps: possibility of being trapped in ...
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Bat algorithm (BA) is a powerful nature-inspired swarm algorithm which finds applicability to a diverse range of problem domains. Though it is efficient, it suffers from two handicaps: possibility of being trapped in local optima and lost convergence speed as the algorithm progresses. This paper proposes swarm bat algorithm with improved search (SBAIS). SBAIS gains superior exploration capabilities by employing swarming characteristics inspired by shuffledcomplexevolution (SCE) algorithm. Best bats of the population are kept in a super-swarm, while all other bats are partitioned according to SCE. The super-swarm uses the search mechanism of bat algorithm with improved search to perform refined search around the best solution, which makes sure that the convergence speed of the algorithm is not lost. Every other swarm gets one solution from the super-swarm before starting their evolution process. These swarms evolve using standard bat algorithm, helping the algorithm to escape any possible local optima. SBAIS further keeps a check on the overall diversity of the population. If the diversity drops below a given threshold value, new random solutions are added to the population. Performance of SBAIS is validated by comparing it to BA and fourteen recent variants of bat algorithm over 30 standard benchmark optimization functions, CEC'05 and CEC'14 function sets. Results established the superiority of SBAIS over the compared algorithms.
One-way traffic is a kind of traffic management measure with less investment, simple operation, and obvious effects. Considering some irrational one-way lane configurations currently in practice, a bi-level optimizati...
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One-way traffic is a kind of traffic management measure with less investment, simple operation, and obvious effects. Considering some irrational one-way lane configurations currently in practice, a bi-level optimization programming model is proposed in this paper. The decision-making variable of the upper level model is the scheme of one-way traffic reconfiguration, and the optimization target is to minimize the total travel time. The lower level programming is an equilibrium traffic assignment model. The shuffled complex evolution algorithm (SCE-UA) and VisSim simulation are applied for the upper and lower models, respectively. A numerical test based on Dalian Harbour Plaza is employed to validate the proposed model. In the test, the effects of the current scheme, the scheme solved by the bi-level model using the SCE-UA and Frank-Wolfe algorithm, and the scheme proposed by this paper (simulation-optimization) are compared. As the results show, the bi-level programming model in this paper can perform well in reducing the total travel time of travelers and in improving traffic efficiency.
Estimating surface runoff for ungauged watershed is an important issue. The Soil Conservation Service Curve Number (SCS-CN) method developed from long-term experimental data is widely used to estimate surface runoff f...
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Estimating surface runoff for ungauged watershed is an important issue. The Soil Conservation Service Curve Number (SCS-CN) method developed from long-term experimental data is widely used to estimate surface runoff from gaged or ungauged watersheds. Many modelers have used the documented SCS-CN parameters without calibration, sometimes resulting in significant errors in estimating surface runoff. Several methods for regionalization of SCS-CN parameters were evaluated. The regionalization methods include: (1) average;(2) land use area weighted average;(3) hydrologic soil group area weighted average;(4) area combined land use and hydrologic soil group weighted average;(5) spatial nearest neighbor;(6) inverse distance weighted average;and (7) global calibration method, and model performance for each method was evaluated with application to 14 watersheds located in Indiana. Eight watersheds were used for calibration and six watersheds for validation. For the validation results, the spatial nearest neighbor method provided the highest average Nash-Sutcliffe (NS) value at 0.58 for six watersheds but it included the lowest NS value and variance of NS values of this method was the highest. The global calibration method provided the second highest average NS value at 0.56 with low variation of NS values. Although the spatial nearest neighbor method provided the highest average NS value, this method was not statistically different than other methods. However, the global calibration method was significantly different than other methods except the spatial nearest neighbor method. Therefore, we conclude that the global calibration method is appropriate to regionalize SCS-CN parameters for ungauged watersheds.
The main purpose of this paper is to introduce a semi-distributed parallel surface rainfall-runoff conceptual model. In this paper, a general solution of the instantaneous unit hydrograph (IUH) has been derived succes...
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The main purpose of this paper is to introduce a semi-distributed parallel surface rainfall-runoff conceptual model. In this paper, a general solution of the instantaneous unit hydrograph (IUH) has been derived successfully for N linearly connected reservoirs, each having a different storage constant. The solution is a function of geomorphologic parameters, meteorologic factors and roughness coefficients. The model also takes into account the hydrologic response which is influenced by outflow downstream of a reservoir. For calibration, the shuffledcomplexevolution (SCE) algorithm is used to search for the global optimal parameters of the model. Because of the parallel structure, the mean roughness parameter of the channel becomes a "conceptual" parameter without a real physical meaning. To evaluate the adaptability of the model adopted, three watersheds around the city of Taipei in Taiwan were chosen to test the effectiveness of the model. The study provides an appropriate rainfall-runoff model for planning flood mitigation in Taiwan. Copyright (C) 1999 John Wiley & Sons, Ltd.
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