Blending Schemes Research Articles

Blending of global and regional ensembles in cycling hybrid ensemble-variational data assimilation for the convection-permitting prediction of typhoon Merbok (2017)

Several blending approaches have been implemented in regional cycling data assimilation to introduce the large-scale weather features of the global model forecast or analysis effectively into regional model forecasts. While in previous studies the blending schemes for data assimilation have generally been applied in stand-alone variational data assimilation systems, here we incorporate the new large-scale ensemble blending schemes into the regional hybrid ensemble-variational (EnVar) data assimilation system. It uses a low-pass Raymond tangent implicit filter to introduce the large-scale background or analysis information from global ensembles. Five parallel cycling hybrid ensemble-variational data assimilation experiments for the convection-permitting prediction of typhoon Merbok in 2017 show that the large-scale ensemble information from Global Ensemble Forecast System (GEFS) can suppress cumulative error growth in data assimilation cycles. On the other hand, the large-scale part of the global ensemble improves the typhoon track and intensity forecasts, possibly due to a better description of the horizontal and vertical structure of the typhoon. In addition, the blending scheme using global ensemble analysis provides more accurate rainfall forecasts of typhoon than the other blending schemes.

Air distribution and coal blending optimization to reduce slagging on coal-fired boiler water wall based on POD reduced order modeling for CFD

Slagging is a common phenomenon interfering with the safe and efficient operation of coal-fired boilers. Due to the lack of monitoring methods for species and temperature distribution near the water wall, which is critical for slagging formation, it is a tricky problem to detect and reduce the slagging in real time. A method for slagging distribution prediction and anti-slagging optimization of a 330 MW tangentially coal-fired boiler is developed in this paper. Proper orthogonal decomposition (POD) reduced order modeling and support vector machine (SVM) are used to predict the distribution of temperature and O2 near the water wall by taking 15 variables as input parameters (load, coal types, primary air rate, secondary air rate, excess air coefficient, and secondary air velocities). Next, fuzzy comprehensive evaluation (FCE) is employed to quantify the slagging occurrence and distribution on the water wall. Furthermore, the real time slagging prediction is realized with the combination of POD, SVM, and FCE. Finally, the conventional genetic algorithm (CGA) and simulated annealing genetic algorithm (SAGA) are adopted to optimize the air distribution schemes and coal blending schemes to reduce the slagging ratio, and their performances are compared. The results indicate that the predictions of temperature and O2 with POD show a good agreement with CFD simulations. In the optimization process, whether air distribution is optimized alone or both coal blending and air distribution are optimized simultaneously, the performance of SAGA is better than that of CGA. After optimizing the air distribution with SAGA, the slagging ratio of a typical operating condition with a load of 330 MW is reduced from 14.21% to 7.16%. The slagging ratio is further reduced to 5.36% after optimizing air distribution and coal blending schemes simultaneously. The proposed slagging prediction and optimization for anti-slagging opens a new perspective to alleviate slagging of water walls effectively.

Composite Asphalt Modification with Waste EPDM Rubber and Tire Pyrolytic Oil: Rheological, Chemical, and Morphological Evaluation

Waste ethylene-propylene-diene monomer (EPDM) rubber and tire pyrolytic oil (TPO) derived from waste tire pyrolysis have shown promising results for their use in asphalt binder modification. However, their incorporation has shown improvements mainly either on rutting or fatigue behavior of the resultant asphalt binders. Composite modification may be a viable option to achieve improved performance with these two materials. The aim of this study was to evaluate the rheological, morphological, and chemical properties of asphalt binders produced through both individual and composite modifications using waste EPDM rubber and TPO. For composite modification, two different blending schemes were used: (1) sequential addition of TPO and EPDM rubber to the heated base asphalt binder; and (2) premixing of TPO and EPDM rubber prior to addition to the heated base asphalt. In the latter case, before being added to the base asphalt, TPO premixed EPDM rubber was conditioned at two temperatures (25°C and 90°C) to see the effect of different conditioning temperatures in composite modification. Firstly, the conventional properties of all the binders in the unaged state were assessed, followed by the evaluation of short-term-aged binders for Superpave and Shenoy rutting parameters, frequency sweep, zero shear viscosity, multiple stress creep and recovery, and Burger’s modeling. The long-term-aged binders were evaluated through the Superpave fatigue parameter, Glover-Rowe parameter, and linear amplitude sweep. Fourier transform infrared (FTIR) spectroscopy and optical microscopy were used for chemical and morphological analyses. The premixing of EPDM and TPO accompanied with conditioning at 90°C imparted a synergistic effect with improvements in both rutting and fatigue performance of binder. The improvement in performance attributes was ascribed to the preswelling of EPDM rubber with TPO, which was evident from the morphological analysis. FTIR provided evidence of chemical interaction between EPDM rubber and TPO during the conditioning and blending processes.

Study on the Conventional Performance and Microscopic Properties of PPA/SBS-Modified Bio-Mixed Asphalt.

To promote the construction of environmentally friendly, sustainable pavements and solve the impact of the scarcity of asphalt resources on highway development, bio-mixed asphalt (BMA) modified by SBS and polyphosphoric acid (PPA) was prepared, and the influence of the ratio of bio-asphalt (BA) replacing petroleum asphalt on different PPA/SBS blending schemes was explored through conventional property tests. According to each PPA/SBS blending scheme, the optimal replacement ratio of bio-asphalt was optimized, and the microstructure and distribution morphology of different PPA/SBS-modified BMA were evaluated. Conventional property test results show that with the same PPA/SBS content, the replacement ratio of bio-asphalt has a significant impact on the conventional performance of composite-modified asphalt, but the appropriate replacement ratio of bio-asphalt can improve the storage stability and conventional performance of composite-modified asphalt; in micromorphological analysis, it was found that the number of bee-like structures on the surface of the modified BMA decreased significantly, which indicated that the molecular heterogeneity of various components in the asphalt was reduced. In addition, bio-asphalt changed the particle morphology and improved the dispersity of SBS in asphalt. The composite-modified BMA had a lower SBS content, but its conventional performance was still excellent—so it has significant application prospects in road engineering.

Hybrid dynamic coal blending method to address multiple environmental objectives under a carbon emissions allocation mechanism

The rise in global carbon emissions has led to serious ecological problems and highlighted the need to better pursue the sustainable development of coal-fired power plants. To reduce environmental pollution, this paper proposes a hybrid dynamic coal blending method under an uncertain environment to achieve a trade-off between economic development and environmental protection. This method fully considers seasonal pollutant variations, a dynamic carbon emissions quota allocation mechanism. A practical case from the Jintang coal-fired powered plant is given to verify the efficiency and practicality of the proposed optimization method. The model allows decision-makers to promote cleaner production by developing coal blending schemes to suit relevant national and local carbon and inhalable particulate matter emissions reduction policies. The results of the sensitivity analysis also confirm this view and show that the model is stable and has emission reduction limits. Specifically, compared with the maximum emissions, the model can reduce carbon emissions by about 29.1% (2.625 × 106 tonnes) and inhalable particulate matter emissions by 29.7% (0.64 × 106 tonnes). The results demonstrated that the hybrid dynamic coal blending method was able to achieve carbon emissions and inhalable particulate matter emissions reductions and that appropriate environmental constraints can improve emissions performance and encourage sustainable CPP development.

Large eddy simulation and finite-volume conditional moment closure modelling of a turbulent lifted H2/N2 flame

Large eddy simulations with three-dimensional finite-volume Conditional Moment Closure (CMC) model are performed for a hydrogen/nitrogen lifted flame with detailed chemical mechanism. The emphasis is laid on the influences of mesh resolution and convection scheme of finite-volume CMC equations on predictions of reactive scalars and unsteady flame dynamics. The results show that the lift-off height is underestimated and the reactive scalars are over-predicted with coarser CMC mesh. It is also found that further refinement of the CMC mesh would not considerably improve the results. The time sequences of the most reactive and stoichiometric hydroxyl radical mass fractions indicate that finer CMC mesh can capture more unsteady details than the coarser CMC mesh. Moreover, the coarse CMC mesh has lower conditional scalar dissipation rate, which would promote the earlier auto-ignition of the flame base. Besides, the effects of the convection scheme for the CMC equations (i.e., upwind, central differencing and their blends) on the lifted flame characteristics are also investigated. It is shown that different convection schemes lead to limited differences on the time-averaged temperature, mixture fraction and species mass fractions. Moreover, the root-mean square values of hydrogen and hydroxyl mass fractions show larger deviation from the measurements with hybrid upwind and central differencing scheme, especially around the flame base. Furthermore, the distributions of the numerical fluxes on the CMC faces also show obvious distinctions between the upwind and blending schemes. The budget analysis of the individual CMC terms shows that a sequence of CMC faces has comparable contributions with upwind scheme. However, with the hybrid schemes, the instantaneous flux is dominantly from limited CMC faces. The reactivity of a CMC cell is more easily to be affected by its neighbors when the upwind scheme is used.

Multi-Objective Optimization Model of Industrial Lubricants Based on Integer Nonlinear Programming

Based on actual lubricating oil production data and the base oil performance indexes of an enterprise, two nonlinear blending schemes corresponding to viscosity and freezing point and four linear blending schemes corresponding to acid value, flash point, oxidation stability, and carbon residue are given in this paper. On the premise that the error of each index is less than 5%, a linear weighted multi-objective optimization model based on integer nonlinear programming considering cost and performance is established, and the lubricating oil blending scheme is obtained. The results show that the blending formula is simple in form and convenient in calculation, and that the overall consistency between the calculated value and the measured value is good. At the same time, the relative error of each performance index, except residual carbon, of the scheme with weight value of (0.5, 0.5) is far less than 5%. Although the performance index is slightly inferior to that of the scheme with a weight value of (0, 1), it is far higher than that of the scheme with a weight value of (1, 0). The linear weighted multi-objective optimization model based on integer nonlinear programming proposed in this paper can well-optimize the blending scheme of industrial lubricating oil, and can re-select different weight combinations according to the actual situation, providing good prospects for application.

Can efficiency evaluation be applied to power plant operation improvement? A combined method with modified weighted russell directional distance model and pattern matching

When there is no further potential to be gained from technological upgrading, it is crucial for coal-fired power plants to effectively utilize their input fuels to improve their operational efficiency and reduce emissions. This paper proposes a coal management model that combines modified weighted russell directional distance model and pattern matching to help coal-fired power plants improve their operation while finding more potential for energy saving and emission abatement. An operational strategy base is established by using efficiency evaluation results for unit daily operational data, and feasible coal blending schemes are accordingly located in the strategy base for different load conditions. The model measures the improvement potential from three perspectives: coal blending, coal input and load dispatching. The daily operational data of four units (two units with circulating fluidized bed (CFB) boilers and two sub-critical units) in a typical coal-fired power plant were collected for case study, and the results indicate that our model can provide effective decision support in plants’ operational management to achieve efficiency improvement.

Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme

This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events.

Handling heterogeneous structures and materials using blending schemes in V-reps

Additive manufacturing has recently enabled the creation of heterogeneous objects in which different materials can be specified at different locations. This creates new exciting perspectives in additive crafting but also presents several challenges, including the problem of specifying rules for varying materials at particular locations as well as encoding user specified properties (such as stress tensors) inside a volumetric model. The aim of this work is to provide an answer to these problems, with the definition of local feature preserving blending schemes that allow both the encodement and the blend of property fields over volumetric representation (V-reps) models, while preserving the original values of such properties where possible. A distance-based blending scheme satisfying this characteristic is described both theoretically and practically, with examples of both homogeneous and heterogeneous primitives in V-rep models.

Simulation of Synthetic Exploration and Geometallurgical Database of Porphyry Copper Deposits for Educational Purposes

The access to real geometallurgical data is very limited in practice, making it difficult for practitioners, researchers and students to test methods, models and reproduce results in the field of geometallurgy. The aim of this work is to propose a methodology to simulate geometallurgical data with geostatistical tools preserving the coherent relationship among primary attributes, such as grades and geological attributes, with mineralogy and some response attributes, for example, grindability, throughput, kinetic flotation performance and recovery. The methodology is based in three main components: (1) definition of spatial relationship between geometallurgical units, (2) cosimulation of regionalized variables with geometallurgical coherence and (3) simulation of georeferenced drill holes based on geometrical and operational constraints. The simulated geometallurgical drill holes generated look very realistic, and they are consistent with the input statistics, coherent in terms of geology and mineralogy and produce realistic processing metallurgical performance responses. These simulations can be used for several purposes, for example, benchmarking geometallurgical modeling methods and mine planning optimization solvers, or performing risk assessment under different blending schemes. Generated datasets are available in a public repository.

Component blending for bitumen production for Indian refineries

This investigation pertains to the bitumen production using component blending. During the manufacture of blended bitumen, the proportion of the constituents, the temperature during blending, and the duration of blending are selected to meet the penetration at 25°C and the absolute viscosity at 60°C as per the Indian specifications. The choices of the blend parameters become challenging when the constituents from multiple crudes are blended to produce different paving grades. Two constituent and three constituent blends for four different grades of bitumen were prepared in a laboratory blending facility from four different crude sources. A design of experiments framework was used to develop prediction models for penetration and viscosity. Simulations were carried out to suggest blending schemes to manufacture all the viscosity grades from different crude sources. Correspondence between the viscosity grade and high-temperature performance grade was observed for blend parameters for the crude sources investigated in this study.

Self-Motion-Assisted Tensor Completion Method for Background Initialization in Complex Video Sequences.

The background Initialization (BI) problem has attracted the attention of researchers in different image/video processing fields. Recently, a tensor-based technique called spatiotemporal slice-based singular value decomposition (SS-SVD) has been proposed for background initialization. SS-SVD applies the SVD on the tensor slices and estimates the background from low-rank information. Despite its efficiency in background initialization, the performance of SS-SVD requires further improvement in the case of complex sequences with challenges such as stationary foreground objects (SFOs), illumination changes, low frame-rate, and clutter. In this paper, a self-motion-assisted tensor completion method is proposed to overcome the limitations of SS-SVD in complex video sequences and enhance the visual appearance of the initialized background. With the proposed method, the motion information, extracted from the sparse portion of the tensor slices, is incorporated with the low-rank information of SS-SVD to eliminate existing artifacts in the initiated background. Efficient blending schemes between the low-rank (background) and sparse (foreground) information of the tensor slices is developed for scenarios such as SFO removal, lighting variation processing, low frame-rate processing, crowdedness estimation, and best frame selection. The performance of the proposed method on video sequences with complex scenarios is compared with the top-ranked state-of-the-art techniques in the field of background initialization. The results not only validate the improved performance over the majority of the tested challenges but also demonstrate the capability of the proposed method to initialize the background in less computational time.

Effect of the numerical viscosity on reproduction of mean and turbulent flow fields in the case of a 1:1:2 single block model

Large-eddy simulations were performed for the velocity fields around a 1:1:2 single block model to clarify the effect of the numerical viscosity in different advection schemes. Six types of advection schemes with different numerical viscosities were employed: second-order central, first-order upwind, and blending schemes with ratios of 95:5, 90:10, 80:20, and 60:40. The central scheme alone or the blending schemes predicted values of the mean and turbulent kinetic energy that were comparable with those of the experiments, whereas the upwind scheme significantly underestimated the experimental values. In addition to the comparison with the experimental data, the turbulent flow fields among the schemes were compared by deriving the probability and power spectral densities. Blending of the upwind scheme indeed reduced the turbulence energy contribution at high frequency. However, such a reduction in energy became influential to the reproduction of the turbulent flows only when damping of the peak spectral energy occurred. The reduction of the statistical values became ∼10% when blending the upwind scheme by 20%. In contrast, a strong or weak velocity, evaluated by the percentile velocities, was more sensitive to the selection of the advection scheme than the mean velocities.

Automated survey design for blended acquisition with irregular spatial sampling via the integration of a metaheuristic and deep learning

Blended acquisition along with efficient detector and source geometries allows for a cost-effective operation. The outcome of subsequent deblending and data reconstruction is of primary importance in determining the technical success of this manner of data acquisition. Despite its advantages over conventional seismic surveys, finding optimum survey parameters is a difficult task. Although incorporating irregularity into spatial sampling and blending schemes leads to effective deblending and data reconstruction, it inherently provides a significantly large problem space. We have developed a survey-design workflow to provide acquisition parameters that account for the source blending as well as the spatial sampling of detectors and sources in an automated manner. Our method involves an iterative scheme to derive the survey parameters that lead to optimum deblending and data reconstruction quality. The approach deals jointly with deblending and data-reconstruction via a sparse inversion in the frequency-wavenumber domain coupled with constraints based on causality and coherency. The residue from this process is subsequently used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network (CNN). Bioinspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of deblending and data reconstruction, we have used a CNN. The applied network architecture successfully rejects suboptimal solutions among newly generated ones from genetic operators. Consequently, only optimal ones are fed into the subsequent step, making our approach computationally affordable. We apply our workflow to design a seismic survey that incorporates the dispersed source array concept. A comparison among different survey-design strategies highlights the ability of the method to effectively derive optimum solutions. The resultant acquisition scenario derived from our approach yields a notable enhancement of deblending and data reconstruction quality attributed solely to the choice of survey parameters.

A planning model for multiple blending schemes

The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon, since they affect the effective utilization of components of refineries and hence profits. The optimization is difficult, because of many complicated product production–consumption relationships in production processes, which are closely related to the running modes of the units. Additionally, the blending products, such as gasoline and diesel, may use multiple blending schemes for their production that increase the complexity of the problem. This paper models the production planning problem as a mixed integer nonlinear programming. Computational experiments for a refinery show the effectiveness of the model. The optimal results give the effective utilization of the self-produced components and increase of the profit.

A simulation-based fuzzy possibilistic programming model for coal blending management with consideration of human health risk under uncertainty

In this research, a simulation-based fuzzy possibilistic programming (SFPP) model was advanced through integrating California puff (CALPUFF), fuzzy sets theory and inexact optimization within a general framework. It has advantages in uncertainty reflection, pollutant dispersion modeling, and the management of coal blending and the related human health risks. The developed SFPP model was solved through a direct search approach which coupled fuzzy simulation and Genetic Algorithm (GA). This approach can not only handle a coupled simulation–optimization problem considering uncertainties that can be expressed as fuzzy sets, but also provided the additional information (i.e. possibility of constraint satisfaction) indicating that how likely a decision maker can believe the decision results. It also can reduce the chances of being trapped in local optima as GA converges to global optima. Moreover, the employed direct search method can avoid the approximation error originating from surrogate simulators and enhance the confidence level of the generated optimal solutions. The developed model was applied to the planning of coal blending in Gaojing and Shijingshan power plants in the west of Beijing. The results indicated that the developed SFPP model was useful for generating a series of coal blending schemes under different acceptable possibility levels, ensuring that the risk to human health reduce to an acceptable level, identifying desired coal blending strategies for decision makers, and considering a proper balance between system costs and acceptable possibility levels.

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework. In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.Implications:A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-off between coal purchase cost and health risk.

Bounded biharmonic weights for real-time deformation

Changing an object's shape is a basic operation in computer graphics, necessary for transforming raster images, vector graphics, geometric models, and animated characters. The fastest approaches for such object deformation involve linearly blending a small number of given affine transformations, typically each associated with bones of an internal skeleton, vertices of an enclosing cage, or a collection of loose point handles. Unfortunately, linear blending schemes are not always easy to use because they may require manually painting influence weights or modeling closed polyhedral cages around the input object. Our goal is to make the design and control of deformations simpler by allowing the user to work freely with the most convenient combination of handle types. We develop linear blending weights that produce smooth and intuitive deformations for points, bones, and cages of arbitrary topology. Our weights, called bounded biharmonic weights, minimize the Laplacian energy subject to bound constraints. Doing so spreads the influences of the handles in a shape-aware and localized manner, even for objects with complex and concave boundaries. The variational weight optimization also makes it possible to customize the weights so that they preserve the shape of specified essential object features. We demonstrate successful use of our blending weights for real-time deformation of 2D and 3D shapes.

Investigation of a novel method for quality control of Chinese herbal compound prescription: HPLC fingerprint and multi-index components combining blending technology control for quality stability of Zhou's prescription extract

A novel method that combines high performance liquid chromatography (HPLC) fingerprint with blending technology was developed and validated for ensuring quality stability of Zhou's prescription extract. For fingerprint analysis, 32 peaks were selected as the common peaks by comparing the chromatograms of 15 batches of Zhou's prescription extracts. Meanwhile, these common peaks were identified by using electronic spray ion mass spectrometry. The batch, which contained herbs that were obtained from geo-authentic habitats during their best harvest times, was selected as the standard extract (SE). The quality difference between SE and 14 batches of Zhou's prescription extracts was evaluated by using the fingerprint similarity, relative deviation of content (RDC), hierarchical clustering analysis (HCA) and principal component analysis (PCA), and narrowed by applying blending technology that could lead to homogenization of high quality of the 14 batches of Zhou's prescription extracts by using nonlinear programming. According to the mode of finding difference and narrowing difference, the previous two kinds of quality control method was combined smartly to ensure the quality stability of Zhou's prescription. Finally, this method was verified and the results indicated that fingerprint similarities of 10 different blending schemes increased to the range of 0.9208 to 0.9797 from 0.7338 to 0.8925. The average RDCs of 28 index components decreased to the range of 0.1549 to 0.2790 from 0.4768 to 0.6083. The diagrams of HCA and PCA show that the 10 blending schemes were grouped with the standard Zhou's prescription extract. These results demonstrated that this new method is an efficient and reliable approach for ensuring quality stability of Zhou's prescription extract.