Worst‐case design of subsea production facilities using semi‐infinite programming

The problem of designing novel process systems for deployment in extreme and hostile environments is addressed. Specifically, the process system of interest is a subsea production facility for ultra deepwater oil and gas production. The costs associated with operational failures in deepwater environments are prohibitively high and, therefore, warrant the application of worst‐case design strategies. That is, prior to the construction and deployment of a process, a certificate of robust feasibility is obtained for the proposed design. The concept of worst‐case design is addressed by formulating the design feasibility problem as a semi‐infinite optimization problem with implicit functions embedded. A basic model of a subsea production facility is presented for a case study of rigorous performance and safety verification. Relying on recent advances in global optimization of implicit functions and semi‐infinite programming, the design feasibility problem is solved, demonstrating that this approach is effective in addressing the problem of worst‐case design of novel process systems. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2513–2524, 2014     

NON‐PARAMETRIC ESTIMATION OF HIGH‐FREQUENCY SPOT VOLATILITY FOR BROWNIAN SEMIMARTINGALE WITH JUMPS

The availability of high‐frequency financial data has led to substantial improvements in our understanding of financial volatility. Most existing literature focuses on estimating the integrated volatility over a fixed period. This article proposes a non‐parametric threshold kernel method to estimate the time‐dependent spot volatility and jumps when the underlying price process is governed by Brownian semimartingale with finite activity jumps. The threshold kernel estimator combines the threshold estimation for integrated volatility and the kernel filtering approach for spot volatility when the price process is driven only by diffusions without jumps. The estimator proposed is consistent and asymptotically normal and has the same rate of convergence as the estimator studied by Kristensen (2010) in a setting without jumps. The Monte Carlo simulation study shows that the proposed estimator exhibits excellent performance over a wide range of jump sizes and for different sampling frequencies. An empirical example is given to illustrate the potential applications of the proposed method.     

Ensembles‐based and GA‐based optimization for landfill gas production

The efficient and economic production of landfill gases (LFG) by optimally adjusting LFG production settings is of high interest as a promising source of biomass energy. A key obstacle in LFG production optimization is the large‐scale and complex system with overwhelming uncertainty and heterogeneity. We propose a simplified ensemble‐based optimization (EnOpt) method to solve the LFG production optimization problem when constraints are not a concern, where the gradient information is obtained from an ensemble of realizations of the system. For constrained optimization, a novel parameterless genetic algorithm is proposed and successfully applied to the simulated LFG process. The effectiveness of the proposed (EnOpt) method and the parameterless genetic algorithm is demonstrated with the simulation of a landfill and gas generation and transport therein, using a parallel computation strategy. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2063–2071, 2014     

Fault detection and isolation in hybrid process systems using a combined data‐driven and observer‐design methodology

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014     

Distributed lyapunov‐based model predictive control with neighbor‐to‐neighbor communication

This work considers distributed predictive control of large‐scale nonlinear systems with neighbor‐to‐neighbor communication. It fulfills the gap between the existing centralized Lyapunov‐based model predictive control (LMPC) and the cooperative distributed LMPC and provides a balanced solution in terms of implementation complexity and achievable performance. This work focuses on a class of nonlinear systems with subsystems interacting with each other via their states. For each subsystem, an LMPC is designed based on the subsystem model and the LMPC only communicates with its neighbors. At a sampling time, a subsystem LMPC optimizes its future control input trajectory assuming that the states of its upstream neighbors remain the same as (or close to) their predicted state trajectories obtained at the previous sampling time. Both noniterative and iterative implementation algorithms are considered. The performance of the proposed designs is illustrated via a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J 60: 4124–4133, 2014     

A Bayesian framework for real‐time identification of locally weighted partial least squares

Just‐in‐time (JIT) learning methods are widely used in dealing with nonlinear and multimode behavior of industrial processes. The locally weighted partial least squares (LW‐PLS) method is among the most commonly used JIT methods. The performance of LW‐PLS model depends on parameters of the similarity function as well as the structure and parameters of the local PLS model. However, the regular LW‐PLS algorithm assumes that the parameters of the similarity function and structure of the local PLS model are known and do not fully utilize available knowledge to estimate the model parameters. A Bayesian framework is proposed to provide a systematic way for real‐time parameterization of the similarity function, selection of the local PLS model structure, and estimation of the corresponding model parameters. By applying the Bayes' theorem, the proposed framework incorporates the prior knowledge into the identification process and takes into account the different contribution of measurement noises. Furthermore, Bayesian model structure selection can automatically deal with the model complexity problem to avoid the overfitting issue. The advantages of this new approach are highlighted through two case studies based on the real‐world near infrared data. © 2014 American Institute of Chemical Engineers AIChE J, 61: 518–529, 2015     

Time‐series clustering via quasi U‐statistics

The problem of time‐series discrimination and classification is discussed. We propose a novel clustering algorithm based on a class of quasi U‐statistics and subgroup decomposition tests. The decomposition may be applied to any concave time‐series distance. The resulting test statistics are proven to be asymptotically normal for either i.i.d. or non‐identically distributed groups of time‐series under mild conditions. We illustrate its empirical performance on a simulation study and a real data analysis. The simulation setup includes stationary vs. stationary and stationary vs. non‐stationary cases. The performance of the proposed method is favourably compared with some of the most common clustering measures available.     

Industrial melt index prediction with the ensemble anti‐outlier just‐in‐time Gaussian process regression modeling method

Several data‐driven soft sensors have been applied for online quality prediction in polymerization processes. However, industrial data samples often follow a non‐Gaussian distribution and contain some outliers. Additionally, a single model is insufficient to capture all of the characteristics in multiple grades. In this study, the support vector clustering (SVC)‐based outlier detection method was first used to better handle the nonlinearity and non‐Gaussianity in data samples. Then, SVC was integrated into the just‐in‐time Gaussian process regression (JGPR) modeling method to enhance the prediction reliability. A similar data set with fewer outliers was constructed to build a more reliable local SVC–JGPR prediction model. Moreover, an ensemble strategy was proposed to combine several local SVC–JGPR models with the prediction uncertainty. Finally, the historical data set was updated repetitively in a reasonable way. The prediction results in the industrial polymerization process show the superiority of the proposed method in terms of prediction accuracy and reliability. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41958.     

Discrete element reduced‐order modeling of dynamic particulate systems

One of the key technical challenges associated with modeling particulate processes is the ongoing need to develop efficient and accurate predictive models. Often the models that best represent solids handling processes, like discrete element method (DEM) models, are computationally expensive to evaluate. In this work, a reduced‐order modeling (ROM) methodology is proposed that can represent distributed parameter information, like particle velocity profiles, obtained from high‐fidelity (DEM) simulations in a more computationally efficient fashion. The proposed methodology uses principal component analysis (PCA) to reduce the dimensionality of the distributed parameter information, and response surface modeling to map the distributed parameter data to process operating parameters. This PCA‐based ROM approach has been used to model velocity trajectories in a continuous convective mixer, to demonstrate its applicability for pharmaceutical process modeling. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3184–3194, 2014     

Ionic liquid recovery alternatives in ionic liquid‐based three‐phase partitioning (ILTPP)

Ionic liquid‐based three‐phase partitioning (ILTPP) is a promising technique to recover high‐added value proteins at the liquid–liquid interface. Its economic and environmental performance highly depends on the net ionic liquid consumption. Alternatives to maximize the fraction of ionic liquid that can be recycled are studied. It is demonstrated that the addition of extra salt, previously proposed in literature, has a very limited effect on ionic liquid recovery for relatively high protein concentrations in the feed stream, and that it may even lead to an increase of the ionic liquid losses under certain conditions. However, small additions of salt are shown to be effective and profitable from an economic point of view. Vacuum evaporation is shown to allow for the complete ionic liquid and salt recovery, reinforcing the sustainability and viability of ILTPP processes. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3577–3586, 2014     

Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling

Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐space form effectively represent nominal variations of processes, some of which are potentially correlated to quality variables and hence help improving the prediction performance of soft sensors. An efficient expectation maximum algorithm is proposed to estimate parameters of the probabilistic model, which turns out to be suitable for analyzing massive process data. Two criteria are also proposed to select quality‐relevant SFs. The validity and advantages of the proposed method are demonstrated via two case studies. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4126–4139, 2015     

Integer‐Valued GARCH Process

Abstract. An integer‐valued analogue of the classical generalized autoregressive conditional heteroskedastic (GARCH) (p,q) model with Poisson deviates is proposed and a condition for the existence of such a process is given. For the case p = 1, q = 1, it is explicitly shown that an integer‐valued GARCH process is a standard autoregressive moving average (1, 1) process. The problem of maximum likelihood estimation of parameters is treated. An application of the model to a real time series with a numerical example is given.     

Generic roll‐to‐roll compatible method for insolubilizing and stabilizing conjugated active layers based on low energy electron irradiation

Irradiation of organic multilayer films is demonstrated as a powerful method to improve several properties of polymer thin films and devices derived from them. The chemical cross‐linking that is the direct result of the irradiation with ～100 keV electrons is fast and has a penetration power compatible with thin plastic foils of one to two hundreds of microns typical of devices explored in organic electronics. We demonstrate here that active layers and complete devices can be subjected to electron irradiation‐induced cross‐linking thus facilitating multilayer solvent processing and morphological stability. The method is fast, generic, contactless, and fully compatible with high‐speed roll‐to‐roll processing of i.e. polymer solar cells at web speeds in excess of 60 m min^?1. We employ fully printed, flexible, and foil‐based indium‐tin‐oxide free polymer solar cells in this study to demonstrate the technique. We also demonstrate that polymer solar cells are exceptionally stable towards ionizing radiation and find that doses as high as 100 kGy can be used before any significant decrease in performance is observed. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40795. Together with Mokarian‐Tabari et al., J. Appl. Polym. Sci. (2014) 131 , 40798, doi: 10.1002/app.40798 , this article is part of a Special Issue on Polymers for Microelectronics. The remaining articles appear in J. Appl. Polym. Sci. (2014) volume 131 , issue 24. This note was added on 1st July 2014.     

Thermo‐hydraulic analysis of refinery heat exchangers undergoing fouling

A complete, systematic approach is presented for the analysis and characterization of fouling and cleaning in refinery heat exchangers. Bringing together advanced thermo‐hydraulic dynamic models, some new formulations, and a method for dynamic analysis of plant data, it allows: extracting significant information from the data; evaluating the fouling state of the units based on thermal measurements and pressure drops, if available; identifying the range of deposit conductivity leading to realistic pressure drops, if pressure measurements are unavailable; estimating key fouling and ageing parameters; estimating the effectiveness of cleaning and surface conditions after a clean; and predicting thermal and hydraulic performance with good accuracy for other periods/exchangers operating in similar conditions. An industrial case study demonstrates the performance prediction in seamless simulations that include partial and total cleanings for over 1000 days operation. The risks of using thermal effects alone and the significant advantages of including pressure drop measurements are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 63: 984–1001, 2017     

High‐frequency sampling and kernel estimation for continuous‐time moving average processes

Interest in continuous‐time processes has increased rapidly in recent years, largely because of high‐frequency data available in many applications. We develop a method for estimating the kernel function g of a second‐order stationary Lévy‐driven continuous‐time moving average (CMA) process Y based on observations of the discrete‐time process Y^Δ obtained by sampling Y at Δ, 2Δ, …, nΔ for small Δ. We approximate g by g^Δ based on the Wold representation and prove its pointwise convergence to g as Δ → 0 for continuous‐time autoregressive moving average (CARMA) processes. Two non‐parametric estimators of g^Δ, on the basis of the innovations algorithm and the Durbin–Levinson algorithm, are proposed to estimate g. For a Gaussian CARMA process, we give conditions on the sample size n and the grid spacing Δ(n) under which the innovations estimator is consistent and asymptotically normal as n → ∞. The estimators can be calculated from sampled observations of any CMA process, and simulations suggest that they perform well even outside the class of CARMA processes. We illustrate their performance for simulated data and apply them to the Brookhaven turbulent wind speed data. Finally, we extend results of Brockwell et al. (2012) for sampled CARMA processes to a much wider class of CMA processes.     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

Regression‐based analysis of multivariate non‐Gaussian datasets for diagnosing abnormal situations in chemical processes

This article presents a regression‐based monitoring approach for diagnosing abnormal conditions in complex chemical process systems. Such systems typically yield process variables that may be both Gaussian and non‐Gaussian distributed. The proposed approach utilizes the statistical local approach to monitor parametric changes of the latent variable model that is identified by a revised non‐Gaussian regression algorithm. Based on a numerical example and recorded data from a fluidized bed reactor, the article shows that the proposed approach is more sensitive when compared to existing work in this area. A detailed analysis of both application studies highlights that the introduced non‐Gaussian monitoring scheme extracts latent components that provide a better approximation of non‐Gaussian source signal and/or is more sensitive in detecting process abnormities. © 2013 American Institute of Chemical Engineers AIChE J, 60: 148–159, 2014     

Practical improvements to autocovariance least‐squares

Identifying disturbance covariances from data is a critical step in estimator design and controller performance monitoring. Here, the autocovariance least‐squares (ALS) method for this identification is examined. For large industrial models with poorly observable states, the process noise covariance is high dimensional and the optimization problem is poorly conditioned. Also, weighting the least‐squares problem with the identity matrix does not provide minimum variance estimates. Here, ALS method to resolve these two challenges is modified. Poorly observable states using the singular value decomposition (SVD) of the observability matrix is identified and removed, thus decreasing the computational time. Using a new feasible‐generalized least‐squares estimator that approximates the optimal weighting from data, the variance of the estimates is significantly reduced. The new approach on industrial data sets provided by Praxair is successfully demonstrated. The disturbance model identified by the ALS method produces an estimator that performs optimally over a year‐long period. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1840–1855, 2015     

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Tight integration through material and energy recycling is essential to the energy efficiency and economic viability of process and energy systems. Equation‐oriented (EO) steady‐state process simulation and optimization are key enablers in the optimal design of integrated processes. A new process modeling and simulation concept based on pseudo‐transient continuation is introduced. An algorithm for reformulating the steady‐state models of process unit operations as differential‐algebraic equation systems that are statically equivalent with the original model is presented. These pseudo‐transient models improve the convergence of EO process flowsheet simulations by expanding the convergence basin. This concept is used to build a library of pseudo‐transient models for common process unit operations, and this modeling concept seamlessly integrates with a previously developed time‐relaxation optimization algorithm. Two design case studies are presented to validate the proposed framework. © 2014 American Institute of Chemical Engineers AIChE J 60: 4104–4123, 2014