PSO(Particle Swarm Optimization)手法による最適熱源探索

Modeling the Catalytic Performance of Coated Gasoline Particulate Filters under Various Operating Conditions

Pd-containing catalysts based on highly dispersed aerogel-derived mayenite were prepared via two approaches. The Pd@C12A7 sample was obtained through the addition of Pd nitrate solution to a fresh Ca(OH)2-Al(OH)3 gel. Pd/C12A7 was synthesized through conventional wet impregnation of the aerogel-derived mayenite. The evolution of the textural characteristics of the support (C12A7) depending on the calcination temperature was investigated. Pd-containing samples were explored using transmission electron microscopy and spin probe EPR spectroscopy. Using the latter method, the presence of active oxygen species capable of producing nitroxyl radicals from diphenylamine was observed. The activity of these species and the reproducibility of their redox behavior were studied in three cycles of temperature-programmed reduction in both hydrogen and CO atmospheres. A prompt thermal aging technique was used to access and compare the activity of the samples towards CO oxidation. The state of Pd species before and after the aging procedure was studied via UV–Vis spectroscopy. It was found that the dispersion of PdO was higher in the case of the Pd/C12A7 catalysts compared to the Pd@C12A7 sample. This is why the Pd/C12A7 catalyst demonstrated higher activity in CO oxidation and better reducibility in TPR cycles.

/pdf/comparative-study-of-pd-mayenite-catalysts-prepared-via-34gktyld.pdf

Comparative Study of Pd-Mayenite Catalysts Prepared via Aerogel Approaches

This study uses a multi-objective genetic algorithm to determine new reaction rate parameters (A’s, β’s and Ea ’s in the non-Arrhenius expressions) for the combustion of a methane/air mixture. The multi-objective structure of the genetic algorithm employed allows for the incorporation of both perfectly stirred reactor and laminar premixed flames data in the inversion process, thus enabling a greater confidence in the predictive capabilities of the reaction mechanisms obtained. Various inversion procedures based on reduced sets of data are investigated and tested on methane/air combustion in order to generate efficient inversion schemes for future investigations concerning complex hydrocarbon fuels. The inversion algorithms developed are first tested on numerically simulated data. In addition, the increased flexibility offered by this novel multi-objective GA has now, for the first time, allowed experimental data to be incorporated into our reaction mechanism development. A GA optimised methane air reaction mechanism is presented which offers a remarkable improvement over a previously validated starting mechanism in modelling the flame structure in a stoichiometric methane-air premixed flame (http://www.leeds.ac.uk/ERRI/research/res.html ). In addition, the mechanism outperforms the predictions of more detailed schemes and is still capable of modelling combustion phenomena that were not part of the optimisation process. Therefore, the results of this study demonstrate that the genetic algorithm inversion process promises the ability to assess combustion behaviour for fuels where the reaction rate coefficients are not known with any confidence and, subsequently, accurately predict emission characteristics, stable species concentrations and flame characterisation. Such predictive capabilities will be of paramount importance within the gas turbine industry.Copyright © 2003 by Crown

A Novel Approach to the Optimisation of Reaction Rate Parameters for Methane Combustion Using Multi-Objective Genetic Algorithms

Selective catalytic reduction (SCR) systems with urea injectors are widely utilized and can meet stricter NOx regulations for both light- and heavy-duty diesel vehicles. In this study, we propose a systematic parameter estimation strategy for a one-dimensional SCR model. Dual-site kinetics with 12 kinetic reactions and 28 kinetic parameters are considered in the SCR model. We estimate subsets of parameters sequentially since it is difficult to estimate all of the parameters at once. To this end, four test-rig experimental data obtained under ammonia storage, ammonia oxidation, nitrogen monoxide oxidation, and SCR reaction were used separately. We estimate a subset of parameters corresponding to the relevant reactions using each rig data because only some reactions occur under each rig experiment. To demonstrate the efficacy of this approach, we estimate the parameters simultaneously using one set of real driving data and validate the model using a new set of real driving data that was not utilized for parameter estimation. With the prediction errors for the test set of real driving data, the proposed technique, which estimates a subset of parameters sequentially, has a 7.35% lower error. 

Automated Model Calibration for Urea-SCR Systems Using Test-Rig Data

Accurate mathematical models are an essential tool in the development of aftertreatment systems, as they can provide detailed information on the impact of design changes while simultaneously reducing development costs and the time between the initiation and completion of a catalytic process development. Identifying the set of kinetic parameters that achieves a perceived acceptable level of accuracy may require significant time and effort. Optimisation techniques can be used to speed up the tuning process; however, these techniques can require a large computation time, and may not produce a satisfactory answer. This invariably leads to questioning regarding the accuracy of such automated approaches. In this paper, the performance of a number of optimisation algorithms including a GA, nPSO and a hybrid algorithm has been explored with respect to their applicability to kinetic parameters optimisation in the context of aftertreatment modelling. The focus is therefore on the assessment of the optimisers’ performance, rather than the characterisation of catalytic reactions. The different algorithms were applied to the optimisation of parameters in a number of mathematical functions and theoretical aftertreatment systems. The optimisation algorithms were tested on theoretical aftertreatment systems since these have known absolute solutions thereby allowing the optimisers’ performance to be assessed in the absence of any other external source of inaccuracy such as model structure and experimental error. The results obtained demonstrate that such optimisation approaches facilitate the determination of kinetic parameters with suitable accuracy. The proposed hybrid optimisation algorithm achieved excellent performance in considerably shorter computation time than the GA or nPSO optimisers.

/pdf/the-development-and-application-of-an-evolutionary-algorithm-347bcaf0ny.pdf

A. Pedlow

Papers

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

OSC Modelling of 3-Way Automotive Catalysts to Understand the Effect of Latent OSC on Dynamic OSC Performance