Model-based real-time optimization of automotive gasoline blending operations

Singh, Anushikha; Forbes, J. Fraser; Vermeer, P.J.; Woo, S.S.

doi:10.1016/s0959-1524(99)00037-2

Cited by 112 publications

(68 citation statements)

References 14 publications

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“…The use of a linear blending rule based on a simple average of compound values weighted by the volume fractions for determination of ON of fuel mixtures, has been shown in various studies [22][23][24][25][26] to be inadequate for formulating surrogate mixtures for gasoline. The linear-by-mole blending rule for determination of ONs of TRF mixtures was demonstrated in [19,21] to produce results that are as accurate as the complex formulations found in the literature and was therefore employed for formulating the TRF surrogate mixture in this study.…”

Section: Surrogate Formulationmentioning

confidence: 99%

The influence of n -butanol blending on the ignition delay times of gasoline and its surrogate at high pressures

Agbro

Tomlin

Lawes

et al. 2017

Fuel

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The influence of blending n-butanol at 20% by volume on the ignition delay times for a reference gasoline was studied in a rapid compression machine (RCM) for stoichiometric fuel/air mixtures at 20 bar and 678 K-858 K. Delay times for the blend lay between those of stoichiometric gasoline and stoichiometric n-butanol across the temperature range studied. At lower temperatures, delays for the blend were however, much closer to those of n-butanol than gasoline despite n-butanol being only 20% of the mixture. Under these conditions n-butanol acted as an octane enhancer over and above what might be expected from a simple linear blending law. The ability of a gasoline surrogate, based on a toluene reference fuel (TRF), to capture the main trends of the gasoline/ n-butanol blending behaviour was also tested within the RCM. The 3-component TRF based on a mixture of toluene, n-heptane and iso-octane was able to capture the trends well across the temperature range studied. Simulations of ignition delay times were also performed using a detailed blended nbutanol/TRF mechanism based on the adiabatic core assumption and volume histories from the experimental data. Overall, the model captured the main features of the blending behaviour, although at the lowest temperatures, predicted ignition delays for stoichiometric n-butanol were longer than those observed. A bruteforce local sensitivity analysis was performed to evaluate the main chemical processes driving the ignition behaviour of the TRF, n-butanol and blended fuels. The reactions of fuel + OH dominated the sensitivities at lower temperatures, with H abstraction from nn-butanol and the blend. At higher temperatures the decomposition of H 2 O 2 and reactions of HO 2 and that of formaldehyde with OH became critical, in common with the ignition behaviour of other fuels. Remaining uncertainties in the rates of these key reactions are discussed.

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Section: Surrogate Formulationmentioning

confidence: 99%

The influence of n -butanol blending on the ignition delay times of gasoline and its surrogate at high pressures

Agbro

Tomlin

Lawes

et al. 2017

Fuel

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“…Equation (1) and (2) define the blend recipes. Equation (3) was formulated based on the nonlinear blending rule used by Singh et al 26 Equation (4) represents the minimum and maximum product quality specifications for RVP property.…”

Section: Mathematical Modelsmentioning

confidence: 99%

Nonlinear Blend Scheduling via Inventory Pinch-based Algorithm using Discrete- and Continuous-time Models

Mahalec

Castillo

2015

Chem.Biochem.Eng.Q.

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This work uses multi-period, inventory pinch-based algorithm with continuous-time model (MPIP-C algorithm 1 ) for scheduling linear or nonlinear blending processes. MPIP-C decomposes the scheduling problem into (i) approximate scheduling and (ii) detailed scheduling. Approximate scheduling model is further decomposed into two parts: a 1 st level model which optimizes nonlinear blend models (with time periods delineated by inventory pinch points), and a 2 nd level multi-period mixed-integer linear programming model (which uses fixed blend recipes from the 1 st level solution) to determine optimal production plan and swing storage allocation, while minimizing the number of blend instances and product changeovers in the swing tanks. The 3 rd level computes schedules using a continuous-time model including constraints based on the short-term plan solution. Nonlinear constraints are used for the Reid vapor pressure in our case studies. Excellent computational performance is illustrated by comparisons with previous approach with discrete-time scheduling model.

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“…To address this problem, [4] proposed a blending RTO method which updates the model with predictions of the components' properties. Although this method improves the model accuracy, it continues to be non robust as it depends on the quality of the predictions.…”

Section: Robust Rtomentioning

confidence: 99%

“…In [3], a geometric approach is presented for the product and mixture design problems but only considering the uncertainty due to measurement imprecision. To deal with components' properties uncertainty, [4] proposed a nonlinear blend RTO system based on predictions of feedstocks properties whereas [5] presented a chance constraint model and a hybrid neural networks-genetic algorithm solution. More recently, [2] introduced a linear blending control algorithm which handles this type of uncertainties via an estimator of the components' properties.…”

Section: Introductionmentioning

confidence: 99%

Robust real-time optimization for the linear oil blending

Janaqi¹,

Aguilera²,

Chèbre³

2013

RAIRO-Oper. Res.

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In this paper we present a robust real-time optimization method for the online linear oil blending process. The blending process consists in determining the optimal mix of components so that the final product satisfies a set of specifications. We examine different sources of uncertainty inherent to the blending process and show how to address this uncertainty applying the Robust Optimization techniques. The polytopal structure of our problem permits a simplified robust approach. Our method is intended to avoid reblending and we measure its performance in terms of the blend quality giveaway and feedstocks prices. The difference between the nominal and the robust optimal values (the price of robustness) provides a benchmark for the cost of reblending which is difficult to estimate in practice. This new information can be used to adjust the level of conservatism in the model. We analyze the feasibility of a blend to be produced from a set of feedstocks when the heel of a previous blend is used in the composition of the new blend. Additional critical information for the control system is then produced.

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Model-based real-time optimization of automotive gasoline blending operations

Cited by 112 publications

References 14 publications

The influence of n -butanol blending on the ignition delay times of gasoline and its surrogate at high pressures

The influence of n -butanol blending on the ignition delay times of gasoline and its surrogate at high pressures

Nonlinear Blend Scheduling via Inventory Pinch-based Algorithm using Discrete- and Continuous-time Models

Robust real-time optimization for the linear oil blending

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