Experimental Design Procedure Research Articles

MODELING AND OPTIMIZATION OF PILLARING PROCESS USING EXPERIMENTAL DESIGN PROCEDURE

Ponte Academic JournalJul 2016, Volume 72, Issue 7 MODELING AND OPTIMIZATION OF PILLARING PROCESS USING EXPERIMENTAL DESIGN PROCEDUREAuthor(s): Georgescu Ana-Maria ,Muntianu Gabriela, Nistor Ileana Denisa, Nardou FrancoiseJ. Ponte - Jul 2016 - Volume 72 - Issue 7 doi: 10.21506/j.ponte.2016.7.18 Abstract:Calcium bentonite from Orasu Nou deposit (Romania) has been used in order to prepare Al-pillared clays, which are microporous materials with special properties. The pillaring process consists in the following steps: bentonite purification, ionic exchange of bentonite, preparation of pillaring agent, intercalation of the exchanged bentonite with pillaring agent and calcination. During calcination process the polyhydroxocation is transformed into an oxide which is strong fixed into the clay interlayer. The pillared clays present a rigid structure, a high termic stability given by the oxido-metallic pillars formed after calcinations. This paper presents the application of the kn experiment design procedure for simulation and optimization of the Al(III)-pillaring process. There were performed 9 experiments according to the experimental design procedure. The input variables were calcination temperature (x1) and calcination duration (x2) and the output variable was the basal distance (Y). The material with the optimum calcination parameters will be chosen in the aim of its using in liquid effluents depollution. Download full text:Check if you have access through your login credentials or your institution Username Password

Evaluation Commonly Used Anthelmintics Efficacy in Gastrointestinal Nematodes through Fecal Egg Count Reduction Test in Adaberga Dairy Farm, West Shewa Zone, Central Ethiopia

This study was conducted in Adaberga dairy farm West Showa Zone, from November, 2014 to April 2015 to evaluate commonly used anthelmintics efficacy against gastrointestinal nematodes. An experimental study design and purposive sampling procedure were employed to select 36 naturally infected jersey breed cattle from source population. And study populations were randomly allocated into three groups, twelve in each; the first group was treated with albendazole, the second with tetraclozan and the last group was left untreated (control). Fecal samples were collected from each cow before and after treatment and modified McMaster method was used to count eggs. Third stage larvae (L3) were recovered from the fecal cultures by the Baerman technique to identify gastrointestinal nematodes. The efficacy of each anthelmintic was determined by Fecal Egg Count Reduction Test (FECRT). SPSS Windows version 16.0 was used for data analysis. Descriptive statistics (means, standard error of mean and reduction percentages) were calculated to manage data. Means were compared among groups through analysis of variance (ANOVA) and difference between treatments was compared using least square method of multiple comparisons. The percentage reduction in mean fecal egg count, after 10 days of treatment, for Albendazole and tetraclozan were 95.51% and 98.18% respectively. There was no statistically significant difference (p=0.262) among the egg count of control, albendazole treated and tetraclozan treated groups before treatment. Statistically egg counts were not different (p=0.85) between treatment groups but there were strict differences (p=0.00) between treatment and control groups on the post-treatment. Generally, these findings indicate that albendazole and tetraclozan are effective against gastrointestinal nematodes in the study area. But, appropriate use of these anthelmintics is credible to prevent future occurrence of resistance.

Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation

This paper examines experimental design procedures used to develop surrogates of computational models, exploring the interplay between experimental designs and approximation algorithms. We focus on two widely used approximation approaches, Gaussian process (GP) regression and nonintrusive polynomial approximation. First, we introduce algorithms for minimizing a posterior integrated variance (IVAR) design criterion for GP regression. Our formulation treats design as a continuous optimization problem that can be solved with gradient-based methods on complex input domains without resorting to greedy approximations. We show that minimizing IVAR in this way yields point sets with good interpolation properties and that it enables more accurate GP regression than designs based on entropy minimization or mutual information maximization. Second, using a Mercer kernel/eigenfunction perspective on GP regression, we identify conditions under which GP regression coincides with pseudospectral polynomial approximation. ...

Optimization of Concrete Mixtures Containing Reclaimed Asphalt Pavement

This study was focused on evaluating the feasibility of using minimally processed reclaimed asphalt pavement (RAP) as aggregate replacement in concrete pavements. A statistical experimental design procedure (response surface methodology) was used to investigate the effects of key mixture parameters on concrete responses. The response surfaces generated from this analysis adequately characterized the behavior of these concrete mixtures, and were ultimately used to develop optimum mixtures to meet varying performance criteria. In laboratory tests, the optimum mixtures performed well and as predicted, thus validating the feasibility of using RAP in this application and this mixture design methodology.

Potential of multiple injection strategies implementing the after shot and optimized with the design of experiments procedure to improve diesel engine emissions and performance

Abstract The potential of the after-injection versus engine-out emissions, combustion noise and brake specific fuel consumption has been evaluated for a Euro 5 diesel engine with a reduced compression ratio (16.3:1). The engine has been fueled with conventional diesel fuel. In particular, the effects of injection strategies that feature either pilot and after-injection shots, or double-pilot and single-after injection shots, have been assessed experimentally, in the presence of high EGR fractions. Calibrations with triple and quadruple injection schedules have been optimized by means of a design of experiments procedure. The performance of the thus calibrated propulsion system has been compared with data from a previously optimized double injection schedule, characterized by a retarded main injection timing in order to intensify the premixed combustion. The experimental data refer to different steady-state working conditions that are representative of passenger car engine applications over the European homologation cycle. In-cylinder analyses of the pressure, heat release rate, temperature and emissions have been performed in order to have a better understanding of the effects of the implemented injection strategies on engine performance. The substitution of the pilot-main injection schedule in the medium-high part-load zone of the NEDC region with a triple injection, featuring both pilot and after shots, has led to lower NOx and higher soot, while fuel consumption remains almost the same. In general, the EGR trade-off soot–NOx, bsfc–NOx, HC–NOx and CO–NOx curves do not change to any significant extent when an after shot is added to the pilot-main injection train. Reductions in the combustion noise, which depend on the changes in the pilot injection parameters that result from the design of experiments procedure, can also be obtained, as a consequence of the addition of the after-injection to the pilot-main injection schedule. Pilot–pilot-main-after strategies guarantee improved NOx engine-out emissions and bsfc–NOx EGR trade-off curve at medium loads and speeds, compared to both pilot–pilot-main and pilot-main-after strategies, and allow combustion noise to be diminished significantly.

Fast Bayesian optimal experimental design for seismic source inversion

We develop a fast method for optimally designing experiments in the context of statistical seismic source inversion. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by elastodynamic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L2 norm of the difference between the experimental data and the simulated data, is proportional to the measurement time and the number of receivers. Consequently, the posterior distribution of the parameters, in a Bayesian setting, concentrates around the "true" parameters, and we can employ Laplace approximation and speed up the estimation of the expected Kullback-Leibler divergence (expected information gain), the optimality criterion in the experimental design procedure. Since the source parameters span several magnitudes, we use a scaling matrix for efficient control of the condition number of the original Hessian matrix. We use a second-order accurate finite difference method to compute the Hessian matrix and either sparse quadrature or Monte Carlo sampling to carry out numerical integration. We demonstrate the efficiency, accuracy, and applicability of our method on a two-dimensional seismic source inversion problem.

Perceptual Distribution of Merging Phonemes

This study seeks to map the perceptual vowel space of front vowel phonemes undergoing merger before voiced velars in Pacific Northwest English (PNWE). In production, most speakers spectrally merge /Eg, eg/ at a point between their non-prevelar counterparts /E, e/, but the height of /aeg/ is more variable. With variable production in the speech community, a question of perception arises: do Northwesterners maintain the same category boundaries for prevelar front vowels as non-prevelars, or are the prevelars merged in perception as they often are in production? This study addresses the question by mapping the perceptual space of front vowels in prevelar vs. precoronal contexts. Stimuli were created to synthesize an initial /b/ followed by 24 front-vowel formant value combinations (F1, F2) with no offglide or coda transitions. Twenty Northwestern subjects were told that each stimulus was the first part of a word that had been cut off in the middle, and they indicated which word they heard with a button press. In the first three blocks of randomized stimuli presentation, the word choices were in the shape /b d/: bad, bid, bayed, bed, bead ; the second three blocks used the same randomly-presented stimuli (unbeknownst to subjects), but the word choices were /b g/: bag, big, bagel, beg, beagle. This design forces lexical access during the task, as subjects must imagine they are hearing words, not contextless phonemes. The paper is organized as follows: Section 2 presents background information on the merger in production, followed by predictions for perception. Section 3 describes the experimental design, stimuli creation, and response procedures. Section 4 presents results, and Section 5 concludes with discussion and future work.

Variation in the expression of ergot alkaloids between individual tillers of perennial ryegrass.

Epichloë fungal endophytes of cool season grasses are well-known to produce a range of alkaloids of benefit to the host. Some of these compounds are advantageous to agriculture due to qualities that promote pasture persistence (e.g., the loline class of alkaloids confer insect protection) while others are detrimental to the well-being of grazing livestock. The ergot alkaloids (e.g., ergovaline), produced in ryegrass and tall fescue associations, causes poor animal health in farming regions in many countries around the world and further study is required to improve our knowledge on this class of compounds. Here we present the application of a quantitative LC-MS/MS (liquid chromatography coupled to mass spectrometry) method measuring eight ergot alkaloids (chanoclavine, agroclavine, elymoclavine, lysergol, lysergic acid, ergine, lysergyl-alanine, ergovaline) produced by endophyte infected grasses, to monitor levels in individual tillers from multiple plants of a single cultivar of perennial ryegrass (Lolium perenne cv. “Grasslands Samson”) infected with a common toxic endophyte strain (Epichloë festucae var. lolii). Monitoring the expression in individual tillers allows an estimation of the variability within a plant (between tillers) as well as between plants. The study showed that there is significant variation in the concentration of the ergot alkaloids between tillers of a single plant, at or exceeding the level of variation observed between individual plants of a population. This result emphasizes the fundamental importance of robust experimental design and sampling procedures when alkaloid expression assessment is required and these need to be rigorously tailored to the hypothesis being tested.

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (−logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool.The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2).The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures.The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.

Effects of Short-term Drought Stress and Mechanical Harvesting on Sweet Orange Tree Health, Water Uptake, and Yield

One of the primary reasons for the slow adoption of mechanical harvesting by Florida citrus growers is the physical injuries associated with it, including loss of leaves, twigs, flowers, and young fruits, limb breakage, and injuries to the bark and root. However, it has been shown that well-managed trees are capable of tolerating defoliation, limb loss, and root and bark injury caused by mechanical harvesting. Irrigation management is one of the most crucial factors that influence citrus tree health. A multiple-year field study was conducted on ‘Valencia’ sweet orange trees in a commercial citrus grove near Immokalee, FL, to determine the effect of initial tree canopy density and short-term drought stress on tree health, water uptake, and productivity of mechanically harvested trees. Three blocks were based on canopy density and overall appearance and indicated as low, moderate, and high canopy density. The experiment was laid in a split-plot design with four replications of six-tree plots of hand-harvested or mechanically harvested trees, taking drought stress or full irrigation as main treatments. The experimental design was repeated with trees in each plot of one of the three canopy density categories. After harvest, each six-tree plot was split into two three-tree subplots, where one subplot was drought-stressed and the other was fully irrigated. Harvesting was conducted in the Spring of 2010, 2011, and 2012 with the same experimental design and data collection procedures. The effects of short-term drought on water use and stem water potential were masked by heavy rains in Spring 2010 and thus no differences in the irrigation treatments were observed. In 2011 and 2012, stem water potential was unaffected by harvesting method. Water use was unaffected by harvesting method across the 3 years. Drought stress significantly increased pull force required to remove fruit and stem water potential after harvest. Although mechanically harvested trees lost leaf mass, with no rain before harvest, results from Spring 2011 and 2012 indicated that short-term drought stress had no effect on citrus leaf area irrespective of harvest method. Drought stress significantly increased fruit detachment force in low and moderate density but not in high-density trees resulting in increased force required to remove fruit from trees with moderate- to low-density canopies. Yield increased from 2010 to 2011 for mechanically harvested trees compared with hand-harvested for low-canopy density trees by 17% and moderate-canopy density trees by 8%, whereas high-density plots indicated similar yield after mechanical harvesting. Comparatively, yield in 2012 decreased in the low and moderate densities compared with yield in 2011 but increased in the high density by 14% and 53% in hand- and machine-harvested trees, respectively. Despite finding 2- to 3-fold more debris in the mechanically harvested trees than the hand-harvested trees, yields and other measured parameters were unaffected suggesting that mechanical harvesting of citrus trees did not have an adverse effect on growth and production of well-watered citrus trees.

Supply chain efficiency: a simulation cum DEA approach

The management of risks within the supply chain and external to it has become highly critical component of supply chain management. Inventory management is a vital tool to mitigate these risks. Lead times and review periods are important parameters in inventory management. The organizations focus on these parameters to enhance the system-wide supply chain performance in terms of services to customers. This paper aims at analyzing the efficiency of total supply chain in context of average fill rate performance. We analyze the efficiency of a hypothetical supply chain network structure which is subjected to time delays due to lead time and inventory review period changes. To understand the optimal relative efficiency among different values of average fill rate performance obtained through simulation, we used Data Envelopment Analysis approach (DEA). Taguchi experimental design procedure is used as a vehicle for conducting the simulation experiments and analyzing its outcome. The proposed integration of simulation with DEA framework provides practical implications to the decision maker as well as connotes to the real world situation where different enterprises compete for the frontier supply chain efficiency.

Polymer modified bitumen: Optimization and selection

Polymer modification of bitumen has been commonly performed since the 1980s in order to decrease bitumen (and pavement) susceptibility to high and low temperatures, allowing reduction in common failure mechanisms as rutting and cracking. Bitumen modification has been commonly performed by addition of thermoplastic or elastomeric polymers. However, there are just a few studies on bitumen blends using multiple materials, seeking for specific advantages provided by addition of these modifiers. This work describes the results obtained after the preparation of multicomponent polymer-bitumen blends (MC) based on an 80/100 penetration grade bitumen with varying amounts of (i) Polyethylene wax (PW); (ii) Styrene–Butadiene–Styrene copolymer (SBS); and (iii) crumb rubber (CR). Ideal blends depending on the amount of polymer modifiers added were found by using an experimental design procedure. It was possible to propose charts allowing optimizing and selecting appropriate polymer modified bitumens (PMB) depending on target properties for a given application by following Ashby’s materials selection methodology.

A methodology for quantifying risk and likelihood of failure for carbon dioxide injection into deep saline reservoirs

Tectonically stable deep saline reservoirs are considered to be the most abundant carbon dioxide (CO2) sequestration sites. Pressure, temperature, salinity and other characteristics of these geologic formations vary at each injection site. It is essential to understand the roles of geologic and engineering factors to optimize the CO2 injection conditions and to quantify the associated risks. Factors such as the magnitude of injection-induced reservoir pressure, quantity of supercritical phase CO2 that comes in contact with caprock, and the amount of residually trapped CO2 govern the fate of CO2, and provide quantitative assessment of the storage integrity.A streamlined protocol was developed using response surface methodology to quantify the risks related to CO2 injection. The proposed methodology includes the design of simulation scenarios, selection and screening of parameters, multiple-linear regression of outcomes, and the development of probability density functions (PDF) of various potential risk factors. Multiphase numerical simulations were performed to understand the behavior of the injected CO2 and associated parameters in deep saline reservoirs with prescribed geometries and petrophysical properties. Formation thickness, formation depth, porosity, horizontal permeability, brine density, and the end-point residual CO2 saturation were the six critical parameters identified that affected important outcomes. A six-factor Box–Behnken experimental design procedure was used to establish an understanding of the sensitivity of the parameters on the important factors, and for subsequently establishing response surfaces. Closed boundary domains with different operational constraints were employed. A stepwise, sequential regression method was used to determine statistically significant coefficients of a response surface model. Monte Carlo simulations with logical distributions of input parameters were performed using the response surface coefficients. Uncorrelated and correlated porosity-permeability distributions were used to generate two types of probability density functions (PDF). PDFs of CO2 plume extent under the caprock and average reservoir overpressure after injection were generated given all of the variability in the input parameters. These results will allow initial screening of a large number of potential injection sites without detailed simulations of each.

On the influence of shape and material used for the femoral component pegs in knee prostheses for reducing the problem of aseptic loosening

The integration of materials selection and design are essential to the success of new product development, especially when applied to biomedical devices. The knee prosthesis, like any other implant, is a product that still lacks satisfactory design solutions for solving the problem of aseptic loosening. Stress shielding is one of the main causes of aseptic loosening that is intimately related to the overall design of the knee prosthesis. The design of the location pegs in the femoral component of the knee prosthesis is seen to have a critical effect on the stress shielding. In this study, therefore, different combinations of location peg geometries and material designs were assessed using finite element analyses in conjunction with a design of experiments procedure. The materials considered were Co–Cr alloy (as reference material) and functionally graded material (FGM) for the main body of the femoral component, and various porous materials for the pegs (as promising new materials). The performance outputs (responses) were stress levels in the femoral bone to assess the stress shielding effect, and stress levels in the pegs to assess adequate peg strength. The result revealed conflicts in satisfying the design objectives. Therefore, a multi-objective optimization was carried out to find the optimal geometries of the pegs for the femoral component. Based on the findings of the optimization process, a set of candidate designs was generated and a multi-criteria decision making approach used to obtain the final ranking of candidate designs. The ranking order demonstrated the superiority of using a FGM femoral component with porous material pegs of conical geometry. By comparing the results with the standard Co–Cr design, it was shown that the new design of pegs can significantly increase the magnitude of stresses seen at the distal femur; hence reduce the stress shielding effect, without over compromising on the strength of the pegs.

One-dimensional Swelling Behavior of Clay and Shale Under Electrical Potential Gradient

Clay and shale formations are potentially subjected to swelling and softening when they are exposed to fresh water. Conventional swell test on clay and shale is very slow and it can take months or years to achieve the full swelling potential. This paper proposes the use of an electrical potential gradient applied across the clay sample to accelerate the swelling process. The fluid flow and ions transport in a clayey porous medium under an electrical potential gradient are examined along with possible chemical reactions. Details of the experimental design, setup, and testing procedures are presented. The test materials include reconstituted kalonite and bentonite samples saturated with sodium chloride solution and natural shale cores. Results of this study confirm that the applied electrical potential gradient enhances the ionic depletion and fluid flow, and subsequently the swelling process.

Study on the Synthesis of Micron-Sized Magnesium Hydroxide Flame Retardant by Using a Gaseous Ammonia Bubbling Method

Micron-sized Mg (OH)2 particles were synthesized by precipitation of MgSO4 with gaseous ammonia. An experimental design procedure was used to investigate the effects of the concentration of MgSO4, temperature of the reaction, time of the reaction, assembly time, speed of stirring, ultraphonic, the introduce of NaOH and surfactant on the mean particle size and the particle size distribution. Laser particle size analyzer, XRD, TG-DTG, SEM and other instrument was used to characterize the product and corresponding particle size.

Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

This paper presents a fuzzy logic-based prediction method to reveal the performance and emission characteristics of a single cylinder spark ignition (SI) engine, which uses different fuel mixtures (gasoline–water macro-emulsions, which contains isopropanol). Adaptive neuro-fuzzy inference system, ANFIS, was used to determine some characteristic parameters due to the combustion, such as exhaust emissions (CO, CO2, HCs). Experimental data such as engine power, torque, engine speed, brake mean effective pressure, brake specific fuel consumption were used as training and checking inputs for the ANFIS model to provide a predictive algorithm. The main purpose of this study is to provide a reliable model that can reveal different performance characteristics, which can be obtained from various gasoline–water macro-emulsions and doing this by the elimination of new experiments. The preliminary results show that an acceptable ANFIS model can also be used in experimental design procedures, by providing quick data handling and the results.

Application of experimental design for the precipitation of calcium carbonate in the presence of biopolymer

Calcium carbonate (CaCO3) was crystallized in the presence of the water-soluble biopolymer carboxymethyl inulin (CMI) by a precipitation method under varying experimental conditions. The CaCO3 crystals obtained were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and BET (Brunauer, Emmett and Teller). Calcium carbonate crystals with different polymorphs and morphologies were produced by varying key parameters: the initial concentration of [Ca2+], the concentration of biopolymer and the feed rate of reactant. An experimental design procedure was applied to determine the significance of the design variables and their interaction with the specific surface area of the crystals and polymorph formation. The experimental design was studied at three levels. The range of the initial concentration of [Ca2+], the feed rate of reactant and the concentration of biopolymer were 20–100mmol, 1–5mL/min and 0–1g/L, respectively. Based on these experimental conditions, model equations were proposed for the relative fraction of the vaterite polymorph and the specific surface area of the CaCO3 crystals obtained. Statistical analysis revealed that the biopolymer concentration had the greatest effect on the morphology and polymorph of calcium carbonate.

Impact of Information Sharing in Alternative Supply Chain Network Structures

Given the inherent uncertainties pervading the operational environment within real-world supply chains, it becomes imperative for each partnering echelon to focus on individual information requirements from the viewpoint of global optimization of overall supply chain (SC) performance. With this in perspective, it is expedient to explicitly model the SC network to synchronize activities across the cooperating partners. This research is concerned with the performance behaviour of two different SC network structures given different design and control parameters adopted by the partnering echelons within the assumed SC configurations. Accordingly, the authors developed discrete event simulation models of two hypothetical supply chain structures and exploit the Taguchi experimental design procedure as a vehicle for conducting the simulation experiments and analyzing its outcome. The results highlight the relative effects of the assumed design and controlling factors on system-wide SC performance and identify appropriate combinations of these factors for optimal performance concerned. For the average inventory level performance measure, key results reveal that sharing of demand information between partnering echelons should not automatically be taken for granted as a direction for performance enhancement.