Ensemble locally weighted partial least squares as a just‐in‐time modeling method

The predictive ability of soft sensors, which estimate values of an objective variable y online, decreases due to process changes in chemical plants. To reduce the decrease of predictive ability, adaptive soft sensors have been developed. We focused on just‐in‐time soft sensors, especially locally weighted partial least squares (LWPLS) regression. Since a set of hyperparameters in an LWPLS model has to be set beforehand and there is only onedataset, a traditional LWPLS model is difficult to accurately predict y‐values in multiple process states. In this study, we propose to combine LWPLS and ensemble learning, and predict y‐values with multiple LWPLS models, whose datasets and sets of hyperparameters are different. The weights of LWPLS models are determined based on Bayes’ theorem, considering their predictive ability. We confirmed that the proposed model has higher predictive accuracy than traditional models through numerical simulation data and two industrial data analyses. © 2015 American Institute of Chemical Engineers AIChE J, 62: 717–725, 2016     

A novel multiple linear multivariate NIR calibration model‐based strategy for in‐line monitoring of continuous mixing

The capability of near infra‐red (NIR) spectroscopy to predict many different variables, such as concentration and humidity, has been demonstrated in many published works. Several of those articles have been in the subject of real time prediction of continuous operations. However, those demonstrations have been for narrow ranges of the variables, especially for powder concentration, which could present a nonlinear behavior of the NIR absorbance as a function of the entire range of concentration. This work developed a novel strategy to predict the entire range of powder concentration using multiple linear NIR calibration models. The root mean standard error of prediction and relative standard deviation (RSD) parameters were used to establish the number of the multiple linear calibration models; other statistical features were used to establish the correct prediction. It was found that a minimum number of linear partial least squares (PLS) calibration models were necessary to accurately predict the range from 0 to 100% w/w. This technique could also be used with other nonlinear behaviors. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3123–3132, 2014     

Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l‐glutamic acid

A mathematical model for semibatch pH‐shift reactive crystallization of l ‐glutamic acid is developed that takes into account the effects of protonation and deprotonation in the species balance of glutamic acid, crystal size distribution, polymorphic crystallization, and nonideal solution properties. The crystallization mechanisms of  α‐ and β‐forms of glutamic acid are addressed by considering primary and secondary nucleation, size‐dependent growth rate, and mixing effects on nucleation. The kinetic parameters are estimated by Bayesian inference from batch experimental data collected from literature. Probability distributions of the estimated parameters in addition to their point estimates are obtained by Markov Chain Monte Carlo simulation. The first‐principles model is observed in good agreement with the experimental data and can be further used for model predictions in robust control strategies. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2828–2838, 2014     

A quality relevant non‐Gaussian latent subspace projection method for chemical process monitoring and fault detection

Partial least‐squares (PLS) method has been widely used in multivariate statistical process monitoring field. The goal of traditional PLS is to find the multidimensional directions in the measurement‐variable and quality‐variable spaces that have the maximum covariances. Therefore, PLS method relies on the second‐order statistics of covariance only but does not takes into account the higher‐order statistics that may involve certain key features of non‐Gaussian processes. Moreover, the derivations of control limits for T² and squared prediction error (SPE) indices in PLS‐based monitoring method are based on the assumption that the process data follow a multivariate Gaussian distribution approximately. Meanwhile, independent component analysis (ICA) approach has recently been developed for process monitoring, where the goal is to find the independent components (ICs) that are assumed to be non‐Gaussian and mutually independent by means of maximizing the high‐order statistics such as negentropy instead of the second‐order statistics including variance and covariance. Nevertheless, the IC directions do not take into account the contributions from quality variables and, thus, ICA may not work well for process monitoring in the situations when the quality variables have strong influence on process operations. To capture the non‐Gaussian relationships between process measurement and quality variables, a novel projection‐based monitoring method termed as quality relevant non‐Gaussian latent subspace projection (QNGLSP) approach is proposed in this article. This new technique searches for the feature directions within the measurement‐variable and quality‐variable spaces concurrently so that the two sets of feature directions or subspaces have the maximized multidimensional mutual information. Further, the new monitoring indices including I² and SPE statistics are developed for quality relevant fault detection of non‐Gaussian processes. The proposed QNGLSP approach is applied to the Tennessee Eastman Chemical process and the process monitoring results of the present method are demonstrated to be superior to those of the PLS‐based monitoring method. © 2013 American Institute of Chemical Engineers AIChE J 60: 485–499, 2014     

Comparison of real‐time methods for maximizing power output in microbial fuel cells

Microbial fuel cells (MFCs) constitute a novel power generation technology that converts organic waste to electrical energy using microbially catalyzed electrochemical reactions. Since the power output of MFCs changes considerably with varying operating conditions, the online optimization of electrical load (i.e., external resistance) is extremely important for maintaining a stable MFC performance. The application of several real‐time optimization methods is presented, such as the perturbation and observation method, the gradient method, and the recently proposed multiunit method, for maximizing power output of MFCs by varying the external resistance. Experiments were carried out in two similar MFCs fed with acetate. Variations in substrate concentration and temperature were introduced to study the performance of each optimization method in the face of disturbances unknown to the algorithms. Experimental results were used to discuss advantages and limitations of each optimization method. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Catalytic partial oxidation of CH4 over bimetallic Ni‐Re/Al2O3: Kinetic determination for application in microreactor

The activity of a novel Ni‐Re/Al₂O₃ catalyst toward partial oxidation of methane was investigated in comparison with that of a precious‐metal Rh/Al₂O₃ catalyst. Reactions involving CH₄/O₂/Ar, CH₄/H₂O/Ar, CH₄/CO₂/Ar, CO/O₂/Ar, and H₂/O₂/Ar were performed to determine the kinetic expressions based on indirect partial oxidation scheme. A mathematical model comprising of Ergun equation as well as mass and energy balances with lumped indirect partial oxidation network was applied to obtain the kinetic parameters and then used to predict the reactant and product concentrations as well as temperature profiles within a fixed‐bed microreactor. H₂ and CO production as well as H₂/CO₂ and CO/CO₂ ratios from the reaction over Ni‐Re/Al₂O₃ catalyst were higher than those over Rh/Al₂O₃ catalyst. Simulation revealed that much smoother temperature profiles along the microreactor length were obtained when using Ni‐Re/Al₂O₃ catalyst. Steep hot‐spot temperature gradients, particularly at the entrance of the reactor, were, conversely, noted when using Rh/Al₂O₃ catalyst. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1691–1701, 2018     

A test for second‐order stationarity of a time series based on the discrete Fourier transform

We consider a zero mean discrete time series, and define its discrete Fourier transform (DFT) at the canonical frequencies. It can be shown that the DFT is asymptotically uncorrelated at the canonical frequencies if and only if the time series is second‐order stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic has approximately a chi‐square distribution. To examine the power of the test statistic, the asymptotic distribution under the locally stationary alternative is established. It is shown to be a generalized non‐central chi‐square, where the non‐centrality parameter measures the deviation from stationarity. The test is illustrated with simulations, where is it shown to have good power.