Blending Rule Research Articles

Perspective on Fuel Property Blending Rules for Design and Qualification of Aviation Fuels: A Review

Perspective on Fuel Property Blending Rules for Design and Qualification of Aviation Fuels: A Review

Do the True Boiling-Point Distillation Yields of Crude Oil Blends Obey the Additive Blending Rule?

Twelve crude oil blends prepared from seven individual crude oils and an imported atmospheric residue were characterized through a true boiling point (TBP) distillation analysis and their density. When comparing the measured TBP fraction yields with those estimated through the application of the additive blending rule, it was found that, for four crude oil blends, the additive blending rule was valid, while for the remaining eight crude oil blends, deviations of the measured TBP yields from the estimated ones were bigger than the TBP analysis’s repeatability limits. By the use of intercriteria analysis evaluation of the data for the deviation of the TBP yields from the additive blending rule and the molar excess volume of the crude oil blends, statistically meaningful relations between the delta TBP yields of light and heavy naphtha, as well as vacuum residue with the molar excess volume, were found. The higher the magnitude of the crude oil blend’s molar excess volume, the bigger the deviations of the TBP yields of naphtha and vacuum residue are. The bigger the deviation of the crude oil blend’s behavior from that of the regular solution, as quantified by the molar excess volume, the bigger the deviations of the TBP yields of naphtha and vacuum residue are.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures

Six hundred and seventy-five measurements of dynamic viscosity and density have been used to assess the prediction error of the Arrhenius blending rule for kinematic viscosity of hydrocarbon mixtures. Major trends within the data show that mixture complexity–binary to hundreds of components—and temperature are more important determinants of prediction error than differences in molecular size or hydrogen saturation between the components of the mixtures. Over the range evaluated, no correlation between prediction error and mole fractions was observed, suggesting the log of viscosity truly is linear in mole fraction, as indicated by the Arrhenius blending rule. Mixture complexity and temperature also impact molar volume and its prediction. However, a linear regression between the two model errors explains less than 20% of the observed variation, indicating that mixture viscosity and/or molar volume are not linear with respect to temperature and/or mixture complexity. Extensive discussion of the intermolecular forces and the geometric arrangement of molecules and vacancies in liquids, which ultimately determines its viscosity, is brought into context with the implicit approximations behind the Arrhenius blending rule. The complexity of this physics is not compatible with a simple algebraic correction to the model. However, sufficient data is now available to determine confidence intervals around the prediction of fuel viscosity based on its component mole fractions and viscosities. At −40°C, when all identified components are pure molecules the modeling error is 13.2% of the predicted (nominal) viscosity times the root mean square of the component mole fractions.

MULTIMODAL FUSION ANALYSIS OF MEDICAL IMAGES BY DISCRETE WAVELET TRANSFORMATION

Medical image fusion is the development of registry and fusion of different pots of one or more imaging modalities in order to improve image value and reduce uncertainty and concern, to improve the applicability of the medical image for clinical opinion or the evaluation of medical care. difficulties. Multimodal medical image fusion algorithms and devices have achieved a remarkable result in improving the veracity of clinical decision support in medical images. The main objective is to improve the understanding of medical images using Discrete Wavelet Transform technology. DWT mainly uses blending rules involving the average pixel. Discrete wavelet transform has been implemented using fusion technology for medical image fusion. Fusion Power is calculated based on PSNR, MSE, and Total Progression Moment. The result demonstrates the success of the fusion scheme on wavelet transform imaging (MRI) and positron production tomography (PET). Other variants of MRI (magnetic resonance imaging) and PET (positron emission tomography) performed for medical diagnosis. Keywords: Multimodal fusion of medical images, fusion rules, magnetic signs,PET.

Vehicle routing for milk collection with gradual blending: A case arising in Chile

We introduce and solve a new multi-commodity Vehicle Routing Problem, motivated by a case study of milk collection in Chile. Different grades of raw milk are collected from a number of farms scattered over a large area and transported to a single plant, allowing milk blending at the trucks. Previous works allow blending different grades of milk in the trucks, but the resulting blend is classified as its worst grade component, even if there was a single drop of it in the blend. The novelty of our work is the use of a less conservative gradual blending rule that associates milk grades to ranges of somatic cell count per milliliter, resulting in a more accurate classification of milk. The volume and somatic cell count per milliliter of the milk produced at each farm are known before the collection, and the farms are paid accordingly. The problem is to route a heterogeneous fleet of vehicles to maximize total profit at the plant, i.e., the revenue from milk minus route cost. All the milk is collected and gradual blending is applied. We propose a mixed integer linear programming formulation and solve the problem using a branch-and-cut method for small instances, and an Iterated Local Search metaheuristic for real-size instances. Both are applied to a large set of standard instances and a real case in Chile. Our results show an increase in profit over previous milk collection strategies, of 20% and 28% for test instances and up to 30% for a real instance.

Understanding of the octane response of gasoline/MTBE blends

Despite the wide utilization of MTBE as an octane booster for market gasoline fuels, the blending characteristics of methyl tert-butyl ether (MTBE) with gasoline fuels are not fully understood. This arises from the lack of octane number measurement of MTBE/gasoline blends, along with lack of detailed analyses of MTBE blending characteristics. In an attempt to address the knowledge gap in MTBE blending, this study provides new research octane number (RON) and motor octane number (MON) measurements of MTBE blended with various pure components and gasoline fuels. The first set of blends were binary mixtures of pure components blended with MTBE. The pure components we chosen to represent PIONA (paraffins, iso-paraffins, olefins, naphthenes and aromatics) molecular classes. The second set of blends were Fuels for Advanced Combustion Engines (FACE) and market gasoline fuels with MTBE. It was observed that the addition of MTBE to n-paraffins, iso-paraffins and olefins results in synergistic octane number effect while linear-by-volume and antagonistic blending was observed for MTBE blends with naphthenes and aromatics, respectively. The octane numbers and response of gasoline/MTBE blends are also dependent on the composition of the base gasoline fuel. These new measurements along with literature dataset were utilized to develop octane predictive models for MTBE blended with gasoline fuels. Various blending rules such as linear-by-volume (LBV), octane sensitivity (OS) and PIONA based models were developed in this work. The most accurate model was the linear-by-volume with the iso-paraffins and aromatics terms where more than 80% of the predictions are within the experimental reproducibility limits. The model can be used to predict and quantify the characteristics of octane boost caused by MTBE addition to gasoline fuels. It can also be used to optimize the octane boost due to the addition of MTBE by changing the composition of the base gasoline fuel.

Threshold Sooting Index of Sustainable Aviation Fuel Candidates from Composition Input Alone: Progress toward Uncertainty Quantification

A quantitative structure–property relationship model has been developed to predict the threshold sooting index (TSI) of arbitrary mixtures of aliphatic and aromatic hydrocarbons of known composition. The model employs contributions from eight molecular fragments plus a global shift and a penalty factor for naphthenic compounds. For each coefficient, the contributions were determined by a constrained regression to data from five different experimental campaigns, which were stitched together by setting the TSI of methylcyclohexane to 5 and the TSI of 1-methylnaphthalene to 100. Unique to this study; the TSI of 1,3,5-trimethylbenzene was restricted to the range of 54.7 and 63.1, which significantly constrains uncertainty. Within the composite training dataset, which contained 65 molecules and 124 data points, including simple mixtures, the model was found to match 95% of the data within 8.9 TSI. Validation of the model against n-butylcyclohexane, dimethylcyclooctane, and a six-component surrogate jet fuel shows prediction to be well within the 95-percentile confidence band of the experiment. This model is the first to integrate the linear blending rule for the TSI with a linear quantitative structure–property relationship model for the TSI, and the first time that referee controls have been applied to ensure that all datasets, experimental and modeled, are normalized to the same scale.

Alcohol Fuels for Spark-Ignition Engines: Performance, Efficiency, and Emission Effects at Mid to High Blend Rates for Ternary Mixtures

This paper follows on from an earlier publication on high-blend-rate binary gasoline-alcohol mixtures and reports results for some equivalent ternary fuels from several investigation streams. In the present work, new findings are presented for high-load operation in a dedicated boosted multi-cylinder engine test facility, for operation in modified production engines, for knock performance in a single-cylinder test engine, and for exhaust particulate emissions at part load using both the prototype multi-cylinder engine and a separate single-cylinder engine. The wide variety of test engines employed have several differences, including their fuel delivery strategies. This range of engine specifications is considered beneficial with regard to the “drop-in fuel” conjecture, since the results presented here bear out the contention, already established in the literature, that when specified according to the known ternary blending rules, such fuels fundamentally perform identically to their binary equivalents in terms of engine performance, and outperform standard gasolines in terms of efficiency. However, in the present work, some differences in particulate emissions performance in direct-injection engines have been found at light load for the tested fuels, with a slight increase in particulate number observed with higher methanol contents than lower. A hypothesis is developed to explain this result but in general it was found that these fuels do not significantly affect PN emissions from such engines. As a result, this investigation supplies further evidence that renewable fuels can be introduced simply into the existing vehicle fleet, with the inherent backwards compatibility that this brings too.

Design as a source of renewal in the production of scientific knowledge in crop science

Innovation is central to the strategic orientations of many agronomic research institutes. Little attention has been paid, however, to the links between knowledge production and design processes, defined as processes resulting in the creation of new objects in pursuit of specific goals. Our aim was to analyze the conditions and specificities of the production of scientific knowledge on agro-ecosystems through design processes. Drawing on design theory, we carried out a cross-analysis of nine research projects that included design processes and produced innovative objects (for example blending rules for variety mixtures in low-input crop management routes). These projects were managed by researchers from a range of disciplines (agronomists, geneticists, crop physiologists, and ecologists), and varied in their duration (from three to 15 years) and scale (from plot to landscape). We combined semi-structured interviews with these researchers and the analysis of various documents (scientific papers, PhD theses, technical publications, and research projects or reports). Our findings show that in all case studies, original and general scientific knowledge on agro-ecosystem functioning was produced at various stages throughout the design processes. The originality of this knowledge lies with the new representations that emerged, either of the agro-ecosystem processes at stake, or of farmers' practices. We show that these representations were formed gradually, through successive iterations of both refined formulations of the design target and new knowledge produced, required for its design. Finally, our results highlight the role of confrontation with real-life situations (particularly through agronomic diagnosis or experiments) in the evolution of these representations. Engaging in design processes can thus be seen as a research practice that leads to the production of original knowledge, allowing for a greater diversity of actors' ways of knowing to be taken into account.

Processability predictions for mechanically recycled blends of linear polymers

Abstract Recycling of thermoplastic polymers is an important element of sustainable circular economy practices. The quality of mechanically recycled polymers is a concern. A method is presented to predict the structure and processability of recycled blends of polymers based on processability knowledge of their virgin precursor components. Blending rules at molecular weight distribution level are well established and form the foundation of the new method. Two essential fundamental building blocks are combined with this foundation. First, component and blend structure are related to viscosity via tube theories. Second, viscosity is related to melt flow index via a continuum mechanics approach. Emulator equations are built based on virtual experimental designs for fast forward and reverse calculations directly relating structure to viscosity and processability. The new combined method is compared with empirical blend rules, and shows important similarities and also clear quantitative differences. Finally, the new method is applied to practical recycling quality challenges.

Experimental investigation of natural gas reactivity using a fuel substitution method

Fuel substitution tests using reference fuels of methane/hydrogen and methane/propane were performed for the pure test fuels hydrogen, ethane, ethylene, propane, n-butane, n-heptane, and isooctane using HCCI combustion. The reactivity of each test fuel relative to the reference fuels was quantified with a newly defined hydrogen index (HI) and propane index (PI). It was observed that the common hydrocarbon fuels tested show a strong sensitivity of HI and PI to temperature and pressure, and that the reactivity relative to the reference fuels can change substantially at different operating conditions. The fuels that have similar low temperature chemistry (n-butane, n-heptane, and isocotane) exhibited PI that were independent of operating condition. Ethane, ethylene, and hydrogen exhibited PI that were strongly dependent on operating condition, with the relative reactivities increasing with increasing temperature. Fuel substitution tests performed with two multi-component methane number (MN) gas mixtures followed trends similar to those observed for the pure hydrocarbon fuel testing, exhibiting similar relative reactivity trends as for propane relative to the reference fuels for the different operating conditions. A linear energy-based blending rule was used to predict the HI of each fuel mixture from the individual fuel components and seen to match experimental trends; however, predicted HI were lower than experimental HI across all operating conditions. The MN was also used to predict the HI of each fuel mixture and was seen to match experimental results only at low pressure/high temperature conditions but not at high pressure/low temperature conditions.

A constraint satisfaction problem algorithm for large-scale multi-panel composite structures

This paper proposes a constraint satisfaction problem algorithm based on implicit decision trees and dynamic programming for the design of multiple composite panels subjected to a set of blending rules and external forces. This decision tree is built incrementally on the ‘fly’, adding plies from a particular set, and does not require any guiding laminate or stacking sequence table for blending purposes. The upper bound algorithm complexity is provided in this work. The technique is applied to the design of a well-known composite multi-panel problem addressed in previous studies; the novel procedure exhibits high-quality solutions (in terms of structure weight) and low computational cost (in terms of laminate evaluations). The novel algorithm scalability is also checked showing that the node tree expansion is independent of the number of panels to be designed. This algorithm could therefore be a suitable choice for large-scale multi-panel design problems.

Comparison of different blending combinations of virgin and RAP-extracted binder: Rheological simulations and statistical analysis

The linear viscoelastic (LVE) behaviour of several combinations of two binders, a straight run 35/50 bitumen and a RAP-extracted bitumen in various proportions (20%, 40% and 60% of RAP binder), was simulated. Two different methods were used to carry out the simulations, The first method consists of a blending rule from the 2S2P1D (2 Springs, 2 Parabolic elements, 1 Dashpot) rheological model, that was calibrated for straight run bitumen and RAP. The second approach is based on the self-consistent model proposed by Hervé and Zaoui [1], considering the two binders as a layered composite sphere embedded in a homogenous medium. Both possible juxtapositions, 35/50 binder over RAP binder (“fresh over RAP”) and vice versa (“RAP over fresh”), were taken into account. All the simulations were compared with 2S2P1D simulations of experimental data obtained from DSR and Tension/Compression tests on samples of perfect blends of the binders, in the same proportions.The two methods yielded different results. Simulations obtained with the blending rule are remarkably close to 2S2P1D simulations of experimental data. “Fresh over RAP” simulations of the self consistent approach are closer than “RAP over fresh” simulations to the results obtained with the blending rule.A statistical analysis was also performed to compare all the considered combinations. Norm (|E*|) and phase angle (φ) of complex modulus at different frequencies (from 1 × 10−6 to 1 × 10100 Hz) and steady state viscosity (calculated from norm of complex viscosity, |η*|, at 1 × 10−100 Hz) were considered as responses. First, a block design approach was used to estimate that significant differences were obtained between all various simulations and 2S2P1D fits of experimental data. Then, the Turkey method was applied in order to compare all combinations with each other and to identify which pairs of combinations show significant differences. The results of the statistical analysis generally confirm the conclusions drawn from the visual judgment of the rheological simulations.

Stacking sequence optimization and blending design of laminated composite structures

The stacking sequence optimization problem for multi-region composite structures is studied in this work by considering both blending and design constraints. Starting from an initial stacking sequence design, unnecessary plies can be removed from this initial design and layer thicknesses of necessary plies are optimally determined. The existence of each ply is represented with discrete 0/1 variables and ply thicknesses are treated as continuous variables. A first-level approximate problem is constructed with branched multipoint approximate functions to replace the primal problem. To solve this approximate problem, genetic algorithm is firstly used to optimize discrete variables, and meanwhile, a blending design scheme is proposed to generate a blended structure. Starting from the thinnest region, this scheme shares all layers of current thinnest region with its adjacent regions. For non-shared layers in the adjacent regions, local mutation is implemented to add or delete plies to make them efficient designs. The whole process is repeated until the blending rule is satisfied. After that, a second-level approximate problem is built to optimize the continuous variables of ply thicknesses for retained layers. Those procedures are repeated until the optimal solution is obtained. Numerical applications, including a two-patch panel and a corrugated central cylinder in a satellite, are conducted to demonstrate the efficacy of the optimization strategy.

Isogeometric spectral approximation for elliptic differential operators

We study the spectral approximation of a second-order elliptic differential eigenvalue problem that arises from structural vibration problems using isogeometric analysis. In this paper, we generalize recent work in this direction. We present optimally-blended quadrature rules for the isogeometric spectral approximation of a diffusion-reaction operator with both Dirichlet and Neumann boundary conditions. The blended rules improve the accuracy of the isogeometric approximation. In particular, the optimal blending rules minimize the dispersion error and lead to two extra orders of super-convergence in the eigenvalue error. Various numerical examples (including the Schrödinger operator for quantum mechanics) in one and three spatial dimensions demonstrate the performance of the blended rules.

Formulation of optimal surrogate descriptions of fuels considering sensitivities to experimental uncertainties

Transportation fuels consist of a large number of species that belong to different families of compounds. Surrogate fuel representations have been formulated to better understand their fundamental chemical composition and to emulate combustion properties. These descriptions are formulated using experiments or through computations, which has thus led to the existence of two different notions of surrogates. There is further distinction of concepts through the use of physical and chemical surrogates, which are designed to emulate those specific properties. Although several surrogate design methodologies have been proposed in literature, they do not incorporate information on experimental uncertainty. By addressing this issue, it is shown that this information is crucial for the reliable construction of surrogates through computations. To incorporate physical fuel properties, a consistent approach through the use of the recent ASTM D2887 distillation curve standard is discussed. Then, a formal computational procedure is presented that incorporates information of experimental uncertainties into the surrogate description. It is shown that surrogates then describe a feasible region and are hence not unique. Both physical and chemical properties are utilized as combustion property targets (CPTs) and consistency with experimental formulations is demonstrated for JP-8 and Jet-A (POSF 4658) surrogates. In addition, the use of convex optimization puts existing concepts for surrogate representation on a more rigorous basis and several conclusions are drawn, particularly on the importance of specific CPTs and weighting factors of regression-based approaches. Also, the effect of using simplified models for the evaluation of CPTs on the final surrogate composition is shown by considering the example of linear blending rules for ignition delay. Finally, the surrogate representation problem is connected to multi-parametric optimization and bounds on surrogate compositions are calculated as a function of the experimental uncertainty along with comparisons against experimental results.

Predicting neurological targets for chemical neurotoxins using ToxCast in vitro data and read-across within QSAR Toolbox

Transportation fuels consist of a large number of species that belong to different families of compounds. Surrogate fuel representations have been formulated to better understand their fundamental chemical composition and to emulate combustion properties. These descriptions are formulated using experiments or through computations, which has thus led to the existence of two different notions of surrogates. There is further distinction of concepts through the use of physical and chemical surrogates, which are designed to emulate those specific properties. Although several surrogate design methodologies have been proposed in literature, they do not incorporate information on experimental uncertainty. By addressing this issue, it is shown that this information is crucial for the reliable construction of surrogates through computations. To incorporate physical fuel properties, a consistent approach through the use of the recent ASTM D2887 distillation curve standard is discussed. Then, a formal computational procedure is presented that incorporates information of experimental uncertainties into the surrogate description. It is shown that surrogates then describe a feasible region and are hence not unique. Both physical and chemical properties are utilized as combustion property targets (CPTs) and consistency with experimental formulations is demonstrated for JP-8 and Jet-A (POSF 4658) surrogates. In addition, the use of convex optimization puts existing concepts for surrogate representation on a more rigorous basis and several conclusions are drawn, particularly on the importance of specific CPTs and weighting factors of regression-based approaches. Also, the effect of using simplified models for the evaluation of CPTs on the final surrogate composition is shown by considering the example of linear blending rules for ignition delay. Finally, the surrogate representation problem is connected to multi-parametric optimization and bounds on surrogate compositions are calculated as a function of the experimental uncertainty along with comparisons against experimental results.

Dispersion-minimizing quadrature rules for [formula omitted] quadratic isogeometric analysis

We develop quadrature rules for the isogeometric analysis of wave propagation and structural vibrations that minimize the discrete dispersion error of the approximation. The rules are optimal in the sense that they only require two quadrature points per element to minimize the dispersion error (Bartoň et al., 2017 [1]), and they are equivalent to the optimized blending rules we recently described. Our approach further simplifies the numerical integration: instead of blending two three-point standard quadrature rules, we construct directly a single two-point quadrature rule that reduces the dispersion error to the same order for uniform meshes with periodic boundary conditions. Also, we present a 2.5-point rule for both uniform and non-uniform meshes with arbitrary boundary conditions. Consequently, we reduce the computational cost by using the proposed quadrature rules. Various numerical examples demonstrate the performance of these quadrature rules.

Optimization of the octane response of gasoline/ethanol blends

The octane responses of gasoline/ethanol mixtures are not well understood because of the unidentified intermolecular interactions in such blends. In general, when ethanol is blended with gasoline, the Research Octane Number (RON) and the Motor Octane Number (MON) non-linearly increase or decrease, and the non-linearity is determined by the composition of the base gasoline and the amount of added ethanol. The complexity of commercial gasolines, comprising of hundreds of different components, makes it challenging to understand ethanol-gasoline synergistic/antagonistic blending effects. Understanding ethanol blending effects with simpler gasoline surrogates is critical to acquire knowledge about ethanol blending with complex multi-component gasoline fuels. In this study, the octane numbers (ON) of ethanol blends with five relevant gasoline surrogate molecules were measured. The molecules investigated in this study include: n-pentane, iso-pentane, 1,2,4-trimethylbenzene, cyclopentane and 1-hexene. These new measurements along with the available data of n-heptane, iso-octane, toluene, various primary reference fuels (PRF) and toluene primary reference fuels (TPRF) with ethanol are used to develop a blending rule for the octane response (RON and MON) of multi-component blends with ethanol. In addition, new ON data are collected for six Fuels for Advanced Combustion Engine (FACE) with ethanol. The relatively simple volume based model successfully predicts the octane numbers (ON) of the various ethanol/PRF and ethanol/TPRF blends with the majority of predictions being within the ASTM D2699 (RON) and D2700 (MON) reproducibility limits. The model is also successfully validated against the ON of the FACE gasolines blended with ethanol with the majority of predictions being within the reproducibility limits. Finally, insights into the possible causes of the synergistic and antagonistic effects of different molecules with ethanol are provided.