Publications

A manuscript describing this work summarized below has been submitted to Applied Spectroscopy. Comparisons of prediction models from the new ACLS and PLS multivariate spectral analysis methods were conducted using simulated data with deviations from the idealized model. Simulated uncorrelated concentration errors, and uncorrelated and correlated spectral noise were included to evaluate the methods on situations representative of experimental data. The simulations were based on pure spectral components derived from real near-infrared spectra of multicomponent dilute aqueous solutions containing glucose, urea, ethanol, and NaCl in the concentration range from 0-500 mg/dL. The statistical significance of differences was evaluated using the Wilcoxon signed rank test. The prediction abilities with nonlinearities present were similar for both calibration methods although concentration noise, number of samples, and spectral noise distribution sometimes affected one method more than the other. In the case of ideal errors and in the presence of nonlinear spectral responses, the differences between the standard error of predictions of the two methods were sometimes statistically significant, but the differences were always small in magnitude. Importantly, SRACLS was found to be competitive with PLS when component concentrations were only known for a single component. Thus, SRACLS has a distinct advantage over standard CLS methods that require that all spectral components be included in the model. In contrast to simulations with ideal error, SRACLS often generated models with superior prediction performance relative to PLS when the simulations were more realistic and included either non-uniform errors and/or correlated errors. Since the generalized ACLS algorithm is compatible with the PACLS method that allows rapid updating of models during prediction, the powerful combination of PACLS with ACLS is very promising for rapidly maintaining and transferring models for system drift, spectrometer differences, and unmodeled components without the need for recalibration. The comparisons under different noise assumptions in the simulations obtained during this investigation emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Clearly, the conclusions of the relative performance of various methods were found to be dependent on how realistic the spectral errors were in the simulated data. Results demonstrating the simplicity and power of ACLS relative to PLS are presented in the following section.

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Optimal estimation theory has been applied to the problem of estimating process variables during vacuum arc remelting (VAR), a process widely used in the specialty metals industry to cast large ingots of segregation sensitive and/or reactive metal alloys. Four state variables were used to develop a simple state-space model of the VAR process: electrode gap (G), electrode mass (M), electrode position (X) and electrode melting rate (R). The optimal estimator consists of a Kalman filter that incorporates the model and uses electrode feed rate and measurement based estimates of G, M and X to produce optimal estimates of all four state variables. Simulations show that the filter provides estimates that have error variances between one and three orders-of-magnitude less than estimates based solely on measurements. Examples are presented that verify this for electrode gap, an extremely important control parameter for the process.

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Electrode gap is a very important parameter for the safe and successful control of vacuum arc remelting (VAR), a process used extensively throughout the specialty metals industry for the production of nickel base alloys and aerospace titanium alloys. Optimal estimation theory has been applied to the problem of estimating electrode gap and a filter has been developed based on a model of the gap dynamics. Taking into account the uncertainty in the process inputs and noise in the measured process variables, the filter provides corrected estimates of electrode gap that have error variances two-to-three orders of magnitude less than estimates based solely on measurements for the sample times of interest. This is demonstrated through simulations and confined by tests on the VAR furnace at Sandia National Laboratories. Furthermore, the estimates are inherently stable against common process disturbances that affect electrode gap measurement and melting rate. This is not only important for preventing (or minimizing) the formation of solidification defects during VAR of nickel base alloys, but of importance for high current processing of titanium alloys where loss of gap control can lead to a catastrophic, explosive failure of the process.

This research involves the measurement of the electrical conductivity (K) for the ESR (electroslag remelting) slag (60 wt.% CaF{sub 2} - 20 wt.% CaO - 20 wt.% Al{sub 2}O{sub 3}) used in the decontamination of radioactive stainless steel. The electrical conductivity is measured with an improved high-accuracy-height-differential technique that requires no calibration. This method consists of making continuous AC impedance measurements over several successive depth increments of the coaxial cylindrical electrodes in the ESR slag. The electrical conductivity is then calculated from the slope of the plot of inverse impedance versus the depth of the electrodes in the slag. The improvements on the existing technique include an increased electrochemical cell geometry and the capability of measuring high precision depth increments and the associated impedances. These improvements allow this technique to be used for measuring the electrical conductivity of highly conductive slags such as the ESR slag. The volatilization rate and the volatile species of the ESR slag measured through thermogravimetric (TG) and mass spectroscopy analysis, respectively, reveal that the ESR slag composition essentially remains the same throughout the electrical conductivity experiments.

A significant improvement to the classical least-squares (CLS) multivariate analysis method has been developed. The new method, called prediction-augmented classical least-squares (PACLS), removes the restriction for CLS that all interfering spectral species must be known and their concentrations included during the calibration. We demonstrate that PACLS can correct inadequate CLS models if spectral components left out of the calibration can be identified and if their 'spectral shapes' can be derived and added during a PACLS prediction step. The new PACLS method is demonstrated for a system of dilute aqueous solutions containing urea, creatinine, and NaCl analytes with and without temperature variations. We demonstrate that if CLS calibrations are performed with only a single analyte's concentrations, then there is little, if any, prediction ability. However, if pure-component spectra of analytes left out of the calibration are independently obtained and added during PACLS prediction, then the CLS prediction ability is corrected and predictions become comparable to that of a CLS calibration that contains all analyte concentrations. It is also demonstrated that constant-temperature CLS models can be used to predict variable-temperature data by employing the PACLS method augmented by the spectral shape of a temperature change of the water solvent. In this case, PACLS can also be used to predict sample temperature with a standard error of prediction of 0.07°C even though the calibration data did not contain temperature variations. The PACLS method is also shown to be capable of modeling system drift to maintain a calibration in the presence of spectrometer drift.

The advent of inductively coupled plasma-atomic emission spectrometers (ICP-AES) equipped with charge-coupled-device (CCD) detector arrays allows the application of multivariate calibration methods to the quantitative analysis of spectral data. We have applied classical least squares (CLS) methods to the analysis of a variety of samples containing up to 12 elements plus an internal standard. The elements included in the calibration models were Ag, Al, As, Au, Cd, Cr, Cu, Fe, Ni, Pb, Pd, and Se. By performing the CLS analysis separately in each of 46 spectral windows and by pooling the CLS concentration results for each element in all windows in a statistically efficient manner, we have been able to significantly improve the accuracy and precision of the ICP-AES analyses relative to the univariate and single-window multivariate methods supplied with the spectrometer. This new multi-window CLS (MWCLS) approach simplifies the analyses by providing a single concentration determination for each element from all spectral windows. Thus, the analyst does not have to perform the tedious task of reviewing the results from each window in an attempt to decide the correct value among discrepant analyses in one or more windows for each element. Furthermore, it is not necessary to construct a spectral correction model for each window prior to calibration and analysis: When one or more interfering elements was present, the new MWCLS method was able to reduce prediction errors for a selected analyte by more than 2 orders of magnitude compared to the worst case single-window multivariate and univariate predictions. The MWCLS detection limits in the presence of multiple interferences are 15 rig/g (i.e., 15 ppb) or better for each element. In addition, errors with the new method are only slightly inflated when only a single target element is included in the calibration (i.e., knowledge of all other elements is excluded during calibration). The MWCLS method is found to be vastly superior to partial least squares (PLS) in this case of limited numbers of calibration samples.

With the demonstration of the viability of using the electroslag remelting process for the decontamination of radionuclides, interest has increased in examining the unique aspects associated with melting steel pipe electrodes. These electrodes consist of several nested pipes, welded concentrically to atop plate. Since these electrodes can be half as dense as a solid electrode, they present unique challenges to the standard algorithms used in controlling the melting process. Naturally the electrode must be driven down at a dramatically increased speed. However, since the heat transfer is greatly influenced and enhanced with the increased area to volume ratio, considerable variation in the melting rate of the pipes has been found. Standard control methods can become unstable as a result of the variation at increased speeds, particularly at shallow immersion depths. The key to good control lies in the understanding of the melting process. Several experiments were conducted to observe the characteristics of the melting using two different control modes. By using a pressure transducer to monitor the pressure inside the pipes, the venting of the air trapped inside the electrode was observed. The measurements reveal that for a considerable amount of time. the pipes are not completely immersed in the slag, allowing the gas inside to escape without the formation of bubbles. This result has implications for the voltage swing as well as for the decontamination reactions.