Prediction Error Sum Of Squares Research Articles

(Digital Presentation) Thermal Behaviour Prediction of Commercial Lithium-Ion Cells Under Different C-Rate and Ambient Conditions Using Surrogate Modelling

Lithium-ion batteries (LIBs) have found widespread application in energy storage due to their high energy density, low self-discharge and low maintenance characteristics. However, the stability and longevity of the batteries under extreme operating conditions is yet to be fully understood. Numerous experimental and simulation studies have been performed to elucidate the effect of temperature, current, depth of discharge, and the number of cycles [1,2,3] on the performance of LIBs. These studies show that the solid-electrolyte interface (SEI) layer and gas formation accelerate at high C-rates due to increased electrolyte decomposition [4,5]. Consequently, the cell's internal resistance increases, resulting in ever-increasing irreversible heat generation inside the cell [6]. This heat generation leads to high internal temperature and a corresponding reduction in cell capacity. The data of cell temperature for a wide range of ambient temperatures and cycling rates is limited because cell performance is typically reported at room temperature. The design of battery packs for electric vehicles(EVs) is also based on test data taken at room conditions. Given that the cell's capacity, specific energy, maximum power output is bound to depend on C-rate and ambient temperature, it is necessary to test and design battery packs based on these parameters.The thermal characteristics of a lithium-ion cell can be predicted using a physics-based thermal model or data-driven methods (DDM) relying on empirical data. Experimental determination of internal cell parameters required in physics-based thermal models is challenging and time-consuming for commercial cells. On the other hand, DDM requires only cell cycling data at specific operating conditions, thereby eliminating the need for internal parameter estimation. This model predicts the experimental input and output data pattern and fits the response surface to estimate the unknown output. In this study, surrogate modelling has been employed to estimate the surface temperature, capacity, energy, and average power of commercial lithium-ion cells for different discharge currents and ambient temperatures. Surrogate modelling, a popular data analysis and reduced-order modelling technique, aims to find a global minimum of a particular objective function using a few objective function evaluations [7]. The surrogate-based model divides experimental data into training and test data sets. The training data set is employed to train the algorithm. After that, the testing data set is used to validate the model's accuracy. Experiments were performed to develop a surrogate model, with the number of experiments decided using the design of experiments[8] for a current range of 0.5C to 3C-rate and ambient temperature range of 0°C to 45°C . The cycling of cells was performed using a high current battery cycler (Arbin), and the ambient temperature was maintained using a thermal chamber (Cincinnati). The surface temperature of commercial 18650 (NMC811) lithium-ion cells was recorded using T-type thermocouples and a National Instruments DAQ module. A polynomial response surface was fitted using surrogate modelling on experimentally obtained data. The response surface shown in figure 1. contains nine data points for the preliminary study, sub-divided into a set of seven training and two testing data. The prediction error sum of squares (PRESS) is currently bounded within 10% due to the limited training data set availability and is expected to be within a band of 1% after adding data from ongoing experiments. The estimation of temperature, capacity, and specific energy can be used to optimize and develop an improved thermal management system for electric vehicles that works under various operating conditions. References Waldmann, T., Wilka, M., Kasper, M., Fleischhammer, M., & Wohlfahrt-Mehrens, M. (2014). Journal of Power Sources, 262, 129-135.Guan, T., Sun, S., Yu, F., Gao, Y., Fan, P., Zuo, P., Du,C., & Yin, G. (2018). Electrochimica Acta, 279, 204-212.Simolka, M., Heger, J. F., Traub, N., Kaess, H., & Friedrich, K. A. (2020). Journal of The Electrochemical Society, 167(11), 110502.Xu, B., Diao, W., Wen, G., Choe, S. Y., Kim, J., & Pecht, M. (2021). Journal of Power Sources, 510, 230390.Rashid, M., & Gupta, A. (2017). Electrochimica Acta, 231, 171-184.Leng, F., Tan, C. M., & Pecht, M. (2015). Scientific reports, 5(1), 1-12.Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005). Progress in aerospace sciences, 41(1), 1-28.Li, W., Xiao, M., Peng, X., Garg, A., & Gao, L. (2019). Applied Thermal Engineering, 147, 90-100. Figure 1

Mixed membership nearest neighbor model with feature difference

The nearest neighbor model has been a popular model for spatial data. This model assumes a distance-based neighborhood structure among a set of entities that define the observations in a dataset. Values of a dependent variable are a function of the corresponding values of its neighbors and values of its own explanatory variables. In this paper, we extend the concept of “spatial neighbors” to a more general network dependency where the nearest neighbor is not necessarily distance-based. As each entity can belong to multiple groups, observations can be related by multiple networks. By exploring the overlapping information that are exhibited by an entity and its neighbors, we modify the traditional spatial autoregressive model with autoregressive disturbances (SARAR) in three ways. First, multiple networks are allowed. Second, differences between the explanatory variables and their corresponding neighboring averages are applied. Third, apart from independent innovations, we investigate the case where the innovations are also multiple-network dependent. Statistical inference of the model parameters is achieved via maximizing the profile log-likelihood function. The standard errors of the profile maximum likelihood estimators are calculated by the inverse Fisher information matrix. Four variations of the multiple network autoregressive models are applied to predict changes in stock prices of 555 companies listed in the Hong Kong Stock Exchange. The model with attribute differencing and dependent innovations produces a smaller out-of-sample mean prediction error sum of squares than that of the other models.

Modelling and optimization of the drying process and the quality parameters of dried osmo-pretreated onions (Allium cepa)

Modelling and optimization represent an important aspect of drying in food processing, providing a fast and convenient means for quality prediction. The research focuses on modelling and optimization of process parameters such as drying rate, water loss, solid gain, vitamin C, manganese, and iron of dried osmo-pretreated onion slices. Least square regression analysis in the Math-lab computer software was used to model and optimise the process parameters., Six (6) mathematical models were developed for each output from the regression analysis that was carried out. The criteria for adjudging these models were the values of their adjusted coefficient of multiple determinations, prediction error sum of squares (also called deleted residual), R2 for prediction, coefficient of variation CV, and the Dubin-Watson test for autocorrelation. The models were checked for adequacy using these criteria, and those found to be adequate were selected from among the other possible combinations. Hence, the best-optimized obtained results from the models are 27.50 g/h, 1.61 g/g, 0.15 g/g, 77.52 mg/100 g, 2.79 mg/1000 g, and 2.19 mg/1000 g for drying rate, water loss, solid gain, vitamin C, manganese, and iron, respectively.

Allometric Equations for Predicting Agave lechuguilla Torr. Aboveground Biomass in Mexico

Quantifying biomass is important for determining the carbon stores in land ecosystems. The objective of this study was to predict aboveground biomass (AGB) of Agave lechuguilla Torr., in the states of Coahuila (Coah), San Luis Potosí (SLP) and Zacatecas (Zac), Mexico. To quantify AGB, we applied the direct method, selecting and harvesting representative plants from 32 sampling sites. To predict AGB, the potential and the Schumacher–Hall equations were tested using the ordinary least squares method using the average crown diameter (Cd) and total plant height (Ht) as predictors. Selection of the best model was based on coefficient of determination (R2 adj.), standard error (Sxy), and the Akaike information criterion (AIC). Studentized residues, atypical observations, influential data, normality, variance homogeneity, and independence of errors were also analyzed. To validate the models, the statistic prediction error sum of squares (PRESS) was used. Moreover, dummy variables were included to define the existence of a global model. A total of 533 A. lechuguilla plants were sampled. The highest AGB was 8.17 kg; the plant heights varied from 3.50 cm to 118.00 cm. The Schumacher–Hall equation had the best statistics (R2 adj. = 0.77, Sxy = 0.418, PRESS = 102.25, AIC = 632.2), but the dummy variables revealed different populations of this species, that is, an equation for each state. Satisfying the regression model assumptions assures that the predictions of A. lechuguilla AGB are robust and efficient, and thus able to quantify carbon reserves of the arid and semiarid regions of Mexico.

Optimization of modulation-assisted drilling of Ti-6Al-4V aerospace alloy via response surface method

ABSTRACT The study concerned with optimization of modulation-assisted drilling (MAD) operation for a difficult-to-cut material Ti6Al4 V was carried out via response surface method (RSM). Conventional drilling (CD) has been explored for comparison purposes. Experiments have been designed according to the Central Composite Design (CCD). The impact of machining input parameters on surface roughness (SR), machining power consumption, and tool wear (TW) was investigated for both cases. Pilot experiments were carried out to find out the best modulation range. Further drilling experiments were carried out varying feed rate, spindle speed, and tool diameter. Multiple regression analysis using RSM established relationships between these parameters and responses. ANOVA was used to develop and test the mathematical models. The models were effective in predicting the responses for MAD of Ti6Al4 V. The optimum drilling parameters are a tool diameter (TD) of 2 mm, feed rate (FR) of 0.030 mm/rev and spindle speed (SS) of 2176.87 rpm. SEM and EDS analyzes were performed to investigate the machined surfaces. From the study, it can be concluded that MAD is a promising machining process for difficult-to-cut materials with comparatively less thrust force, surface roughness and tool wear. Abbreviations SD Standard deviation; SS: Spindle Speed; TF: Thrust Force; BUE: Built-Up-Edge; PRESS: Prediction error sum of squares; TD: Tool Diameter; CD: Conventional Drilling; CV: Coefficient of variance; MAD: Modulation-Assisted Drilling; SR: Surface Roughness; VAM: Vibration-Assisted Machining; TW: Tool Wear; RSM: Response Surface Methodology; ANOVA: Analysis of Variance; FR: Feed Rate; SNR: Signal-to-Noise Ratio; FW: Flank wear

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

Time-varying (TV) nonlinear systems widely exist in various fields of engineering and science. Effective identification and modeling of TV systems is a challenging problem due to the nonstationarity and nonlinearity of the associated processes. In this paper, a novel parametric modeling algorithm is proposed to deal with this problem based on a TV nonlinear autoregressive with exogenous input (TV-NARX) model. A new class of multiple beta wavelet (MBW) basis functions is introduced to represent the TV coefficients of the TV-NARX model to enable the tracking of both smooth trends and sharp changes of the system behavior. To produce a parsimonious model structure, a locally regularized ultraorthogonal forward regression (LRUOFR) algorithm aided by the adjustable prediction error sum of squares (APRESS) criterion is investigated for sparse model term selection and parameter estimation. Simulation studies and a real application to EEG data show that the proposed MBW-LRUOFR algorithm can effectively capture the global and local features of nonstationary systems and obtain an optimal model, even for signals contaminated with severe colored noise.

A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs.

This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. The proposed method is validated on two simulations, and then applied to source level motor imagery (MI) EEGs, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEGs are physiologically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. Experimental results confirm the effectiveness of the TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition.

Nonlinear predictive model selection and model averaging using information criteria

ABSTRACTThis paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and determine which one best presents the data. Three commonly used criteria, namely, Akaike information criterion, Bayesian information criterion and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and model averaging is evaluated via a number of case studies using both simulation and real data. The results show that APRESS produces better models in terms of generalization performance and model complexity.

Crown-Stump Diameter Model for Parkia biglobosa Benth. Species in Makurdi, Benue State, Nigeria

The crown of tree is the centre of physiological activity which gives an indication of the potential photosynthetic capacity on a tree. Though, its measurement remains a challenge in forest inventory task. The ability to predict crown diameter from stump diameter provides an effective technique of obtaining its estimate. This helps in detecting the excessive tree felling than actual requirements and wildlife suitability.The main objective of this study was to develop and test crown diameter prediction models for silvicultural management of naturally grown Parkia biglobosa within the University of Agriculture, Makurdi. Nine 100 m x 100 m temporary sample plots were established using simple random sampling method. Crown diameter and stump diameter were measured in all living P. biglobosa trees with stump diameter ≥10.0 cm. Least square method was used to convert the counted stumps into harvested crown dimension. Three linear and three non-linear models using stump diameter as the exploratory variable were developed and evaluated using the adjusted coefficient of determination (Adj.R 2 ), standard error of estimate (SEE), prediction error sum of squares (PRESS) and Akaike information criterion (AIC). The crown-stump diameter relationship was best described by the double logarithmic function with .The result showed that Crown diameter estimation was feasible even when the only information available is stump diameter.The resulting equation was tested for validation with independent data obtained from additional plots and was found to be desirable for estimating the crown diameter for Parkia biglobosa in Makurdi, Benue State, Nigeria.

Multivariate Calibration Applied to the Simultaneous Spectrophotometric Determination of Ascorbic Acid, Tyrosine and Epinephrine in Pharmaceutical Formulation and Biological Fluids

Background: Epinephrine (EP), ascorbic acid (AA) and tyrosine (TY) usually coexist together in human blood and are considered as important molecules for physiological processes in human metabolism. Thus, to determine these compounds in biological fluids, we need to develop sensitive, simple and rapid methods. Method: The simultaneous spectrophotometric determination of AA, EP and TY, at pH=7.0 using multivariate calibration techniques such as principal component regression (PCR), partial least squares (PLS) and their first derivative methods have been carried out. Results: Satisfactory results were achieved using first derivative-partial least squares. After optimization of all variables, the linear ranges for AA, EP and TY were 0.8-35.0, 1.0-72.01 and 1.0-145.0 µgmL-1, respectively. The detection limits for AA, EP and TY obtained 0.1, 0.35 and 0.28 µgmL-1, respectively. Principal component (NPC) for AA, EP and TY were 2, 3 and 3. Prediction error sum of squares (PRESS) for AA, EP and TY were 2.37, 0.91 and 2.57, respectively. The root mean square errors of Prediction (RMSEP) for AA, EP and TY were 0.35, 0.16 and 0.60 respectively. Conclusion: Good agreement between prediction and actual concentrations clearly indicates the utilization of this procedure for the simultaneous determination of AA, EP and TY in serum, plasma and urine samples and also for the determination of AA in vitamin c tablet and EP in epinephrine injection. Keywords: UV–vis spectrophotometry, multivariate calibration, simultaneous determination, ascorbic acid, epinephrine, tyrosine.

Modeling and Analysis for Hardness and Structure of Nonferrous Alloy Castings Produced Using Zcast Metal Casting Process through Response Surface Methodology

The purpose of this investigation is to develop a mathematical model for predicting the influence of casting parameters on hardness of non ferrous alloy (NFA) castings produces using Zcast process by employing response surface methodology (RSM). An association has been proposed between hardness of castings and shell wall thickness (SWT), weight density (WD) and pouring temperature (PT) of the shell mould fabricated using three dimensional printer (3DP). The experiments were conducted using NFA materials like; aluminium, zinc, lead, copper, brass and bronze. An effective procedure of RSM has been utilized for the optimal values of casting parameters. Mechanical Micro-Vickers hardness was measured in which the indent is produced by applying a known load to the specimen and then measuring the size of the appropriate diagonals optically. Based on experimental data the investigation has led to conclusion as Quadratic model have been established for predicting the response in the form of hardness of castings.Further, the model developed was validated by analysis of variance (ANOVA) which indicated the significance of model. The Model F-value of 2477.66 implies that the model is significant. There is only a 0.01% chance that a Model F-Value could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. The statistical analysis of second order quadratic equation results showed that the most significant parameters, favoring the hardness are B, C and A2. The superiority of the quadratic mathematical models were developed in confidence intervals of 99.75% with prediction error sum of squares of 3.6%for the prediction of the harness.Microstructure analysis was also performed for justification of hardness data.

Application of computational fluid dynamics and surrogate-coupled evolutionary computing to enhance centrifugal-pump performance

ABSTRACTTo reduce the total design and optimization time, numerical analysis with surrogate-based approaches is being used in turbomachinery optimization. In this work, multiple surrogates are coupled with an evolutionary genetic algorithm to find the Pareto optimal fronts (PoFs) of two centrifugal pumps with different specifications in order to enhance their performance. The two pumps were used a centrifugal pump commonly used in industry (Case I) and an electrical submersible pump used in the petroleum industry (Case II). The objectives are to enhance head and efficiency of the pumps at specific flow rates. Surrogates such as response surface approximation (RSA), Kriging (KRG), neural networks and weighted-average surrogates (WASs) were used to determine the PoFs. To obtain the objective functions’ values and to understand the flow physics, Reynolds-averaged Navier–Stokes equations were solved. It is found that the WAS performs better for both the objectives than any other individual surrogate. The best individual surrogates or the best predicted error sum of squares (PRESS) surrogate (BPS) obtained from cross-validation (CV) error estimations produced better PoFs but was still unable to compete with the WAS. The high CV error-producing surrogate produced the worst PoFs. The performance improvement in this study is due to the change in flow pattern in the passage of the impeller of the pumps.

Optimal non-diagonal-type estimators in linear regression under the prediction error sum of squares criterion

ABSTRACTThis article considers the notion of the non-diagonal-type estimator (NDTE) under the prediction error sum of squares (PRESS) criterion. First, the optimal NDTE in the PRESS sense is derived theoretically and applied to the cosmetics sales data. Second, we make a further study to extend the NDTE to the general case of the covariance matrix of the model and then give a Bayesian explanation for this extension. Third, two remarks concerned with some potential shortcomings of the NDTE are presented and an alternative solution is provided and illustrated by means of simulations.

Factor analysis using mixed models of multi-environment trials with different levels of unbalancing

This study aimed to analyze the robustness of mixed models for the study of genotype-environment interactions (G x E). Simulated unbalancing of real data was used to determine if the method could predict missing genotypes and select stable genotypes. Data from multi-environment trials containing 55 maize hybrids, collected during the 2005-2006 harvest season, were used in this study. Analyses were performed in two steps: the variance components were estimated by restricted maximum likelihood, using the expectation-maximization (EM) algorithm, and factor analysis (FA) was used to calculate the factor scores and relative position of each genotype in the biplot. Random unbalancing of the data was performed by removing 10, 30, and 50% of the plots; the scores were then re-estimated using the FA model. It was observed that 10, 30, and 50% unbalancing exhibited mean correlation values of 0.7, 0.6, and 0.56, respectively. Overall, the genotypes classified as stable in the biplot had smaller prediction error sum of squares (PRESS) value and prediction amplitude of ellipses. Therefore, our results revealed the applicability of the PRESS statistic to evaluate the performance of stable genotypes in the biplot. This result was confirmed by the sizes of the prediction ellipses, which were smaller for the stable genotypes. Therefore, mixed models can confidently be used to evaluate stability in plant breeding programs, even with highly unbalanced data.

Modeling and Analysis for Hardness and Structure of Nonferrous Alloy castings produced using Zcast Metal casting Process through Response Surface Methodology

The purpose of this investigation is to develop a mathematical model for predicting the influence of casting parameters on hardness of non ferrous alloy (NFA) castings produces using Zcast process by employing response surface methodology (RSM). An association has been proposed between hardness of castings and shell wall thickness (SWT), weight density (WD) and pouring temperature (PT) of the shell mould fabricated using three dimensional printer (3DP). The experiments were conducted using NFA materials like; aluminium, zinc, lead, copper, brass and bronze. An effective procedure of RSM has been utilized for the optimal values of casting parameters. Mechanical Micro-Vickers hardness was measured in which the indent is produced by applying a known load to the specimen and then measuring the size of the appropriate diagonals optically. Based on experimental data the investigation has led to conclusion as Quadratic model have been established for predicting the response in the form of hardness of castings.Further, the model developed was validated by analysis of variance (ANOVA) which indicated the significance of model. The Model F-value of 2477.66 implies that the model is significant. There is only a 0.01% chance that a Model F-Value could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. The statistical analysis of second order quadratic equation results showed that the most significant parameters, favoring the hardness are B, C and A2. The superiority of the quadratic mathematical models were developed in confidence intervals of 99.75% with prediction error sum of squares of 3.6%for the prediction of the harness. Microstructure analysis was also performed for justification of hardness data.

The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples

Partial least squares (PLS1) and principal component regression (PCR) are two multivariate calibration methods that allow simultaneous determination of several analytes in spite of their overlapping spectra. In this research, a spectrophotometric method using PLS1 is proposed for the simultaneous determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA). The linear concentration ranges for AA, DA and UA were 1.76–47.55, 0.57–22.76 and 1.68–28.58 (in μgmL−1), respectively. However, PLS1 and PCR were applied to design calibration set based on absorption spectra in the 250–320nm range for 36 different mixtures of AA, DA and UA, in all cases, the PLS1 calibration method showed more quantitative prediction ability than PCR method. Cross validation method was used to select the optimum number of principal components (NPC). The NPC for AA, DA and UA was found to be 4 by PLS1 and 5, 12, 8 by PCR. Prediction error sum of squares (PRESS) of AA, DA and UA were 1.2461, 1.1144, 2.3104 for PLS1 and 11.0563, 1.3819, 4.0956 for PCR, respectively. Satisfactory results were achieved for the simultaneous determination of AA, DA and UA in some real samples such as human urine, serum and pharmaceutical formulations.

Apparent metabolizable energy and prediction equations for reduced-oil corn distillers dried grains with solubles in broiler chicks from 10 to 18 days of age

An experiment consisting of 2 identically designed trials was conducted to determine the nutrient composition and AMEn content of distillers dried grains with solubles (DDGS) to develop prediction equations for AMEn in broilers. Fifteen samples of DDGS ranging in ether extract (EE) from 3.15 to 13.23% (DM basis) were collected from various dry-grind ethanol plants and were subsequently fed to broiler chicks to determine AMEn content. A corn-soybean meal control diet was formulated to contain 15% dextrose, and test diets were created by mixing the control diet with 15% DDGS at the expense of dextrose. In each trial, 672 male Ross × Ross 708 chicks were housed in grower battery cages with 7 birds per cage (0.06 m2/bird) and received a common starter diet until 10 d of age. Each cage was randomly assigned to 1 of 16 dietary treatments, with 6 replicate pens per treatment. Experimental diets were fed over a 6-d acclimation period from 10 to 16 d of age, followed by a 48-h total excreta collection period. Gross energy (GE) and CP of the experimental diets and excreta were determined to calculate AMEn for each DDGS sample. On a DM basis, AMEn of the 15 DDGS samples ranged from 1,869 to 2,824 kcal/kg. Analyses were conducted to determine the GE, CP, EE, DM, starch, total dietary fiber (TDF), neutral detergent fiber (NDF), acid detergent fiber (ADF), and ash content of the DDGS samples. Stepwise regression resulted in the following best-fit equation for AMEn (DM basis) based on the adjusted coefficient of determination (R2adj), SE, and prediction error sum of squares (PRESS): AMEn, kcal/kg = −12,282 + (2.60 × GE) + (89.75 × CP) + (125.80 × starch) – (40.67 × TDF; R2adj = 0.86; SE = 98.76; PRESS = 199,819; P ≤ 0.001). These results indicated that the composition of DDGS with variable EE content may be used to predict AMEn in broiler chicks.

Multivariate Optimization Model in a Partial Least Squares-1 Method for Simultaneous Determination of Dorzolamide Hydrochloride and Timolol Maleate in Eye Drops

The aim of this study is optimization of experimental factors in a UV-visible spectrophotometric method for simultaneous determination of dorzolamide hydrochloride and timolol maleate in eye drops. For this purpose, partial least squares-1 method was developed for resolving overlapped UV-visible spectra of these compounds. Principal component number, wavelength range (number of wavelengths) and data preprocessing applications were investigated as experimental factors. The affected experimental response in this method is PRESS (Prediction Error Sum of Squares) values. Response Surface Methodology (RSM) method was used for interpretation of relationships between these factors and PRESS values, and results were shown as equations and contour graphs. Multivariate calibration techniques which were used in the method development have some advantages such as using whole spectrum for the simultaneous analysis of the mixture component that allow to minimize the effects of noise peak, shorter analysis time, low cost and simple instrumentation without any chemical pre-treatment. This optimized method was validated and applied to a pharmaceutical preparation, eye drops, and results were compared with a literature method statistically and no significant difference was observed. Keywords: Multivariate optimization, PLS-1, validation, pharmaceutical preparation, dorzolamide hydrochloride, timolol maleate, eye drops.

System identification methods for metal rubber devices

Metal rubber (MR) devices, a new wire mesh material, have been extensively used in recent years due to several unique properties especially in adverse environments. Although many practical studies have been completed, the related theoretical research on metal rubber is still in its infancy. In this paper, a semi-constitutive dynamic model that involves nonlinear elastic stiffness, nonlinear viscous damping and bilinear hysteresis Coulomb damping is adopted to model MR devices. The model is first approximated by representing the bilinear hysteresis damping as Chebyshev polynomials of the first kind and then generalised by taking into account the effects of noises. A very efficient systematic procedure based on the orthogonal least squares (OLS) algorithm, the adjustable prediction error sum of squares (APRESS) criterion and the nonlinear model validity tests is proposed for model structure detection and parameter estimation of MR devices for the first time. The OLS algorithm provides a powerful tool to effectively select the significant model terms step by step, one at a time, by orthogonalising the associated terms and maximising the error reduction ratio, in a forward stepwise manner. The APRESS statistic regularises the OLS algorithm to facilitate the determination of the optimal number of model terms that should be included into the model. And whether the final identified dynamic model is adequate and acceptable is determined by the model validity tests. Because of the orthogonal property of the OLS algorithm, the selection of the dynamic model terms and noise model terms are totally decoupled and the approach also leads to a parsimonious model. Numerical ill-conditioning problems which can arise in the conventional least squares algorithm can be avoided as well. The methods of choosing the sampling interval for nonlinear systems are also incorporated into the approach. Finally by utilising the response of a cylindrical MR specimen, it is shown how the model structure can be detected in a practical application.

High-efficiency design of a tunnel ventilation jet fan through numerical optimization techniques

This paper describes a numerical optimization procedure for performance improvement of a tunnel ventilation jet fan. We employ optimization techniques based on a PRESS (predicted error sum of squares)-based-averaging (PBA) model to improve the aerodynamic performance of a tunnel ventilation jet fan. For numerical analyses, three-dimensional Reynolds-averaged Navier-Stokes (RANS) equations with a shear stress transport turbulence model are discretized using finite volume approximations and solved on hexahedral grids to evaluate the total efficiency at operating conditions as the objective function. Numerical results are verified by using experimental data for the total efficiency and the effective outlet velocity at operating conditions. Further, four geometric variables are used defining the meridional length and the thickness profile at the hub and shroud in the jet fan rotor as the design variables for numerical optimization. The results of numerical optimization show a significant improvement in the total efficiency of the optimum model in comparison with the base model.