Kinetic Modeling of Large‐Scale Reaction Systems

Ho, Teh C.

doi:10.1080/01614940802019425

Cited by 60 publications

(42 citation statements)

References 175 publications

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“…Practical identifiability is related to the amount and quality of available data. There are other techniques that can specifically be applied for formally reducing kinetic models (Ho, 2008). Any model reduction should be done with care so as to ensure that the resulting lumped parameters, if any, can be properly interpreted.…”

Section: Methods and Frameworkmentioning

confidence: 99%

Spatial Aspects in Biological System Simulations

Resat

Costa

Shankaran

2011

Computer Methods, Part C

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Mathematical models of the dynamical properties of biological systems aim to improve our understanding of the studied system with the ultimate goal of being able to predict system responses in the absence of experimentation. Despite the enormous advances that have been made in biological modeling and simulation, the inherently multiscale character of biological systems and the stochasticity of biological processes continue to present significant computational and conceptual challenges. Biological systems often consist of well-organized structural hierarchies, which inevitably lead to multiscale problems. This chapter introduces and discusses the advantages and shortcomings of several simulation methods that are being used by the scientific community to investigate the spatiotemporal properties of model biological systems. We first describe the foundations of the methods and then describe their relevance and possible application areas with illustrative examples from our own research. Possible ways to address the encountered computational difficulties are also discussed.

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Section: Methods and Frameworkmentioning

confidence: 99%

Spatial Aspects in Biological System Simulations

Resat

Costa

Shankaran

2011

Computer Methods, Part C

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“…The overall HDS reaction order for a real feed is higher than that for individual sulfur species. 35 The literature abounds with examples of higher-than-first-order HDS kinetics for various petroleum distillates. 35 Refractory feedstocks (e.g., light cycle oil) exhibit higher reaction order than reactive ones.…”

Section: Catalyst Development Catalyst Evaluationmentioning

confidence: 99%

“…35 The literature abounds with examples of higher-than-first-order HDS kinetics for various petroleum distillates. 35 Refractory feedstocks (e.g., light cycle oil) exhibit higher reaction order than reactive ones. 35 For example, the overall HDS reaction orders for three diesel fuels of increasing refractoriness are 2.2, 2.9, and 3.8 over a sulfide catalyst.…”

Section: Catalyst Development Catalyst Evaluationmentioning

confidence: 99%

“…35 Refractory feedstocks (e.g., light cycle oil) exhibit higher reaction order than reactive ones. 35 For example, the overall HDS reaction orders for three diesel fuels of increasing refractoriness are 2.2, 2.9, and 3.8 over a sulfide catalyst. 36 Due to nitrogen species' selfinhibition, the HDN of individual organonitrogen species follows fractional or zeroth order.…”

Section: Catalyst Development Catalyst Evaluationmentioning

confidence: 99%

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A theory of ultradeep hydrodesulfurization of diesel in stacked‐bed reactors

Ho¹

2017

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com)Hydrodesulfurization catalysts have two types of active sites for hydrogenation and hydrogenolysis reactions. While hydrogenation sites are more active for desulfurizing refractory sulfur species, they are more susceptible to organonitrogen inhibition than hydrogenolysis sites. In contrast, hydrogenolysis sites are more resistant to organonitrogen inhibition but are less active for desulfurizing refractory sulfur species. This dichotomy is exploited to develop an ultradeep hydrodesulfurization stacked-bed reactor comprising two catalysts of different characteristics. The performance of such a catalyst system can be superior or inferior to that of either catalyst alone. A mathematical model is constructed to predict the optimum stacking configuration for maximum synergies between the two catalysts. The best configuration provides the precise environment for the catalysts to reach their full potentials, resulting in the smallest reactor and minimum hydrogen consumption. Model predictions are consistent with experimental results. A selectivity-activity diagram is developed for guiding the development of stacked-bed catalyst systems.Bottom curve: reference catalyst followed by new catalyst configuration; middle curve: mixed-bed; top curve: new catalyst followed by reference catalyst configuration.

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“…Now, in Chemical Engineering, surrogate models have been used for modelling and optimization of conventional chemical processes, due to the high complexity and non-linearity of the involved models. In particular, the great ability of neural networks to capture complex models is well known; due to this, neural networks have been used to model and optimize conventional chemical processes in different applications such as chaotic chemical reaction systems 14 , crude distillation units 15 , large-scale reaction systems 16 , process synthesis 17 , conventional distillation sequences 18 , syngas generation and treatment 19 , integrated gasification combined cycle 20 , biodiesel production 21 , and power plant design 22 . However, the development of intensified processes has brought about important challenges in modelling and optimization, due to the more complex structure and relation between all design variables, with respect to conventional chemical processes.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Gutiérrez‐Antonio

Briones-Ramírez²

2016

Chem. Biochem. Eng. Q.

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Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front.

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Kinetic Modeling of Large‐Scale Reaction Systems

Cited by 60 publications

References 175 publications

Spatial Aspects in Biological System Simulations

Spatial Aspects in Biological System Simulations

A theory of ultradeep hydrodesulfurization of diesel in stacked‐bed reactors

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

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