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Determining the optimal operating mode (batch or continuous-flow) for chemical manufacturing remains a pressing open problem. Focusing on reactors, an accurate determination must account for fundam...

This paper makes several first-of-a-kind contributions toward “double intensification” (static/dynamic) of distillation systems, focusing in particular on the energy-intensive separation of a mixtu...

Light alkanes from shale resources can potentially be converted to an easy-to-transport liquid hydrocarbon product by catalytic dehydrogenation followed by catalytic oligomerization. The chemical s...

Step and pulse responses have long been used to identify process dynamics and design control systems such as PID (proportional-integral-derivative) and model predictive control. They are simple and...

Over the past two decades, the pharmaceutical and specialty chemical industries have led a considerable effort toward transitioning from existing batch to continuous flow processes. The transition ...

This paper applies the concept of dynamic intensification (defined as changes to the dynamics, operation strategy, and/or control of a process that lead to a substantially more efficient processing path) to binary distillation columns. The resulting strategy consists of manufacturing a target product as a blend of two auxiliary products, both having lower energy demands than a reference value, which corresponds to producing the target product(s) in a column operating at steady state. A discussion of the appropriate control structures and switching strategies between the two auxiliary products is provided. An extensive case study concerning the separation of a methanol–1-propanol mixture was carried out, demonstrating that energy savings in the order of 1.4% are possible with no disruption in product quality or production rate.

Many methods are available to tune proportional integral (PI) controllers for first order plus time delay (FOPTD) models of overdamped processes. The two asymptotes for small and large ratios of time delays over time constants are easily calculated. These two asymptotes can be used to evaluate and provide guidelines for the performance and application ranges of PI controller tuning rules. By matching these two asymptotes, a simple analytic tuning rule is suggested. For some overdamped processes whose transfer functions have large zero terms, half-order plus time delay (HOPTD) models are found to yield better results than the FOPTD models. Applying the technique of matching two asymptotes, a simple analytic PI controller tuning rule has also been proposed for the HOPTD models. To apply these tuning rules to high order processes with known transfer functions, model reduction methods to obtain the FOPTD and HOPTD models are investigated. Simulation results for empirical and full models of processes show the ...

The first-order plus time delay (FOPTD) model-based method is a standard approach to tune proportional–integral (PI) controllers in plants. The FOPTD model can be obtained easily from step responses. However, because of their structural limitations, FOPTD models suffer from difficulties in approximating step responses for some processes including processes with overshoot, resulting in PI controllers with unacceptable performance. To remove these drawbacks, models combining two FOPTD models that can be obtained easily from step responses are proposed to tune PI controllers. Several simulations and experimental examples are given, illustrating the improved performance of the proposed method.

Tight integration between the amine scrubbing plant, coal-fired power plant, and enhanced oil recovery (EOR) facility creates process design and control challenges for post-combustion CO2 capture. A dynamic model of an advanced amine scrubbing process and a steady state model of a 550 MWe power plant were used to examine four bounding cases that represent different system components dominating the regulatory process control strategy. Satisfying the operational and economic objectives of one system component resulted in unfavorable dynamic performance for the remainder of the system. When a step change decrease occurred in either the CO2 delivery rate from the capture plant to the EOR facility or the steam extraction rate from the power plant to the capture plant, more than 3 h was required for the amine plant to reach a new steady state. Attempting to control the CO2 removal rate from the flue gas can not be satisfactorily achieved through regulatory control alone. Controlling stripper temperature and pre...

A low-order amine scrubbing model was developed for an intercooled absorber and advanced flash stripper configuration with piperazine solvent. The low-order lumped parameter model uses semi-empirical thermodynamics and rate-based mass transfer and embeds reaction kinetics in a constant overall transfer coefficient. The predicted off-design steady state of this model is compared to a high-order Aspen Plus simulation for 100–85% power plant load. The difference in CO2 removal rate between the two models is less than 1% when power plant load is greater than 94%. The removal rate is systematically overpredicted in the low-order model because a constant CO2 mass transfer coefficient in the absorber leads to an overprediction of absorber performance at part-load operation. Compared to pilot plant data, the low-order model captures the dynamic response of a step change in the stripper pressure control valve. The characteristic time of the total CO2 inventory is found to be 77 min, compared to a total liquid resi...

Plant-wide oscillations generated in a single unit can negatively affect the overall control performance of the process; thus, it is necessary to detect them and diagnose their root cause. However, the interference of noise and the need for oscillation propagation routes pose more challenges for process engineers. In this paper, the concept spectral transfer entropy is proposed and its connection to the spectral Granger causality is derived. Moreover, an information transfer method incorporating spectral envelope algorithm and spectral transfer entropy is applied to provide new diagnostic guidance, whose feasibility and effectiveness have been demonstrated by both simulated and industrial case studies. Compared with current methods, the new procedure enjoys the following advantages: (a) performing oscillation detection and diagnosis within a targeted frequency range and mitigating the effects of measurement noise outside the bandwidth; (b) provides an nominal causal map reflecting the oscillation propagat...

Steam methane reforming is a mature and complex process extensively used worldwide for hydrogen production from methane. The process takes place in a steam methane reformer (SMR), with the endothermic reforming reactions being carried out in catalyst-filled tubes placed in a gas-fired furnace. The SMR is an energy-intensive process unit, and maximizing energy efficiency is of primary interest. However, the high-temperature conditions and large physical scale of the process (hundreds of tubes and burners) pose several operational challenges related to distributed sensing, actuation, and feedback control. Various efforts have been reported on optimization of furnace operation using rigorous computational fluid dynamics (CFD)-based models but, being computationally intensive, these models are unsuitable for real-time optimization. In this paper, we present an integrated framework that relies on the use of advanced temperature sensors, soft sensors, and reduced-order and rigorous SMR CFD models for distribute...

This paper proposes a model-based detection and isolation (FDI) system based on nonlinear state estimation that can be applied to nonlinear systems. The proposed FDI system uses an extended Kalman filter (EKF), in which conditions based on high filtering are defined to best serve the FDI objectives. A better understanding of the residual trends, calculated from the difference between measurements and the EKF estimates, can be obtained when a fault occurs by developing a model that is able to predict the behavior of the residuals. This model is utilized as the basis for detection and isolation of single and multiple faults. Comparisons with data driven techniques, specifically principal component analysis (PCA) and Kernel PCA, show superior isolation results, having the advantage of distinguishing single and multiple faults from a diverse array of possible faults, a common occurrence in complex processes. The proposed approach is validated using an experimental air heater.

Very simple proportional-integral-derivative (PID) controller tuning rules for a wide range of stable processes are available. However, for unstable processes, the design trend is for controllers to be more complex for better performances. Here, the design concept of “simplicity” is extended to unstable processes. Simple desired closed-loop transfer functions for the direct synthesis method and simple approximations of the process time delay are utilized for unstable processes. Very simple tuning rules for PID controllers and set-point filters are obtained, yielding similar or even improved performances over previous more complicated PID controller tuning methods.

This paper presents an integrated virtual metrology (VM) and quality excursion detection framework for online implementation on a plasma etch tool. Traditional external metrology have inherent dela...

High-mix manufacturing in the semiconductor industry has driven the development of several nonthreaded state estimation methods. These methods share information among different manufacturing contex...

The SIMC method by Skogestad (J. Process Control 2003, 13, 291–309) to tune the PID controller is revisited, and a new method (K-SIMC) is proposed. The proposed K-SIMC method includes modifications of model reduction techniques and suggestions of new tuning rules and set point filters. Effects of such modifications are illustrated through simulations for a wide variety of process models. The proposed modifications permit the SIMC method to be applied with more confidence.

The issue of dynamic batch profile synchronization is addressed. By converting synchronization into a dynamic optimization problem, dynamic time warping (DTW) and derivative DTW (DDTW) show the best performance by far. To deal with the singularity point and numerical derivative estimation problems of DTW and DDTW in the presence of noise, a robust DDTW algorithm is proposed by combining the Savitzky–Golay filter and DDTW algorithm together. A comparative analysis of robust DDTW and available methods is performed on simulated and real chemical plant data.

A new approach for flare monitoring is proposed so that flare combustion efficiency can be predicted online in industrial plants. Multivariate image analysis (MIA), which is based on principal component analysis (PCA) and projection to latent structures (PLS), has been applied to flare combustion systems in order to predict their resulting combustion efficiencies, as a function of the crosswind velocity, using simulated results, and as a function of steam or air flow rates, using experimental tests of a full-size flare. The results show that a multivariate regression model based on flare color images can be used to predict the flare performance over a range of operating conditions for steam-assisted flares. Therefore, simple two-dimensional color images of industrial flares may be a fast, accurate, and inexpensive approach for online monitoring of these industrial combustion systems. This would allow for developing effective flare control and mitigation strategies.

The relay feedback system often shows an asymmetric response because of initial transient states, disturbances, and process nonlinearity. To restore a symmetric response of the relay feedback system, an iterative adjustment of input or output bias is required. Instead of trying to obtain a symmetric response, the asymmetric response can be analyzed to estimate the ultimate data or a parametric model of process. However, the asymmetric response causes additional errors in estimating the ultimate properties of the process. An area method is proposed to reduce these errors. Because integrals (areas) of the relay responses reduce the effects of the high-order harmonic terms significantly, the proposed method shows better accuracy in obtaining frequency response data and parametric models compared with previous approaches.

Industrial process monitoring tools require robust and efficient estimation techniques that maintain a high service factor by remaining online during abnormal operating conditions, such as during loss of measurements, changes in control status, or maintenance. Constraints incorporate additional process knowledge into estimation by bounding estimated disturbances within feasibility limits thereby providing robustness to faulty measurements or conditions that violate process models. Moving horizon estimation (MHE) and unscented Kalman filtering (UKF) are two estimation techniques that permit incorporation of constraints prior to evaluating the a priori estimate. This paper evaluates both constrained nonlinear estimators versus the extended Kalman filter (EKF) using industrial process data provided by ExxonMobil Chemical Company. Results provide short-term insight into the fouling process, and parameter estimates produced by UKF and MHE are shown to be more accurate than EKF.

In situ adaptive tabulation (ISAT) is applied to dynamic process simulators for reducing computational run-time. Several enhancements of previous approaches are presented here, including a method for estimating the sensitivities using input−output data, along with different strategies for record distribution. A modified version of the original algorithm (mISAT) to improve performance of ISAT is also suggested. Case studies for first-principles and data-driven models using ISAT are performed to generate accurate trajectories, which are essentially the same as those obtained by direct integration. Computational speed-up using ISAT is also shown for these studies.

The amplitude and period of the relay feedback oscillation are used to obtain the ultimate parameter of a dynamic process and to tune a proportional−integral−derivative (PID) controller automatically. Equations for the ultimate gain and period are based on ignoring higher harmonic terms in the relay feedback response. The integral of the relay feedback response can suppress the higher harmonic terms, and the amplitude of this integral has been found to be better for estimating the ultimate gain of a process. As extensions, simpler methods that use several area calculations of the relay feedback response are proposed and shown to provide more accurate frequency responses, ultimate gain and period, and parametric models.

The effectiveness of closed-loop insulin infusion algorithms is assessed for three different mathematical models describing insulin and glucose dynamics within a Type I diabetes patient. Simulations are performed to assess the effectiveness of proportional plus integral plus derivative (PID) control, feedforward control, and a physiologically-based control system with respect to maintaining normal glucose levels during a meal and during exercise. Control effectiveness is assessed by comparing the simulated response to a simulation of a healthy patient during both a meal and exercise and establishing maximum and minimum glucose levels and insulin infusion levels, as well as maximum duration of hyperglycemia. Controller effectiveness is assessed within the minimal model, the Sorensen model, and the Hovorka model. Results showed that no type of control was able to maintain normal conditions when simulations were performed using the minimal model. For both the Sorensen model and the Hovorka model, proportional control was sufficient to maintain normal glucose levels. Given published clinical data showing the ineffectiveness of PID control in patients, the work demonstrates that controller success based on simulation results can be misleading, and that future work should focus on addressing the model discrepancies.

A dynamic rate-based model was developed for stripping in CO2 capture from coal-fired power plants with 30 wt % monoethanolamine (MEA). The model, created in a flow sheet of Aspen Custom Modeler, was based on the film theory for liquid and vapor phases. It takes into account the impact of equilibrium reactions on the mass transfer, thermodynamic nonidealities, and the hydraulics of the structured packing. With this model, steady state analyses were carried out for the stripper to understand the effect of the lean loading and the height of the packing on total equivalent work and find optimum operating conditions that minimize power plant lost work. Two dynamic strategies with control configurations are proposed to run the stripper in a flexible operation during the period of electricity peak load and prices. Open loop responses demonstrated some differences in dynamic behavior and steady state values for proposed dynamic strategies. One of the approaches increased the CO2 removal by 1% at the reduced stea...

A mathematical model describing glucose-dependent pH swelling and insulin release is developed for pH-sensitive cationic hydrogels in which glucose oxidase and catalase have been immobilized and insulin imbibed. Glucose based swelling and insulin release are simulated for intravenously injected particles at various design conditions. The effects of particle size, the number of injected particles, insulin loading, enzyme loading, monomer functional group loading and pK(a), and hydrogel crosslinking ratio on insulin release and glucose sensitivity are investigated in order to optimally design the device for use. Increased insulin infusion is shown to result from increasing the number of circulating gels, increasing the collapsed particle size, or by decreasing the crosslinking ratio of the system. Release duration is shown to be dependent only upon the particle size and the achievable diffusion coefficient of the system. Glucose sensitivity, as measured by gluconic acid production and by the system pH, are functions of glucose oxidase loading and the concentration and pK(a) of the monomer used in the hydrogel.The necessarily submicron particle size results in very rapid device insulin depletion. When the device is designed without considering constraints, the resulting release profile resembles that of an on/off switching mechanism. Future work will focus on simulations of swelling and release when the device is implanted in an alternative administration site.

Design of experiments (DOE) for parameter estimation in dynamic systems is receiving more attention from process system engineers. In this paper, a principal component analysis (PCA)-based optimal criterion (P-optimal) for model-based DOE is proposed that combines PCA with information matrix analysis. The P-optimal criterion is a general form that encompasses most widely used optimal design criteria such as D-, E-, and SV-optimal, and it can automatically choose the optimal objective function (criterion) to use for a specific differential and algebraic (DAE) system. Two engineering examples are used to validate the algorithms and assumptions. The advantages of P-optimal DOE include ease of reducing the scale of the optimization process by choosing parameter subsets to increase estimation accuracy of specific parameters and avoid an ill-conditioned information matrix.

Single wafer rapid thermal processing (RTP) has become one of the key technologies in semiconductor manufacturing due to its faster wafer processing with reduced thermal budget and precise control of processing conditions. As the standard size of the silicon wafer grows and integration of integrated circuits increases, better control to improve the processing time, uniformity, and repeatability of processing is required. In RTP, identification and control are complicated because of high nonlinearity, drift, and time varying nature of the wafer dynamics. Physical models for the wafer dynamics have been available, but they are not utilized fully for identification and control. Here, based on a physical model of wafer, a practical identification method and an analytic linearizing control method are proposed.

A fundamental dynamic model was successfully used in the implementation of multiple control methodologies via a novel inferential control strategy using HYSYS to treat missing process measurements. Results from steady state and dynamic testing of an azeotropic distillation system of methanol, normal pentane, and cyclohexane were obtained. Steady-state equilibrium and nonequilibrium models were developed and validated with experimental data from a packed distillation unit operated at finite reflux. Dynamic multicomponent distillation experiments were also carried out, and experimental process data were collected using the pilot scale experimental unit. Two different variable pairings were studied, and the results from individual control loop configurations were compared with a multivariable control strategy using the model predictive control (MPC) software Predict Pro.

Multiloop control systems are often used for industrial multivariable processes because of their simplicity. To design multiloop control systems, single-input single-output (SISO) methods that guarantee specified closed-loop characteristics can be applied. Because the design equations are nonlinear due to loop interactions and may cause computational difficulties, a continuation method is proposed to obtain the solutions. By choosing the interaction level as a continuation parameter, we can design multiloop control systems without worrying about numerical difficulties such as divergence. To illustrate the approach, the modified Ziegler−Nichols method is applied to design multiloop control systems.

This paper outlines a method to implement nonlinear model predictive control (NMPC) in real-time control applications. Nonlinear model identification is generally seen as a major obstacle to implementing NMPC. However, once an accurate nonlinear model is identified the computational effort is often too great to implement the model in a real-time application. The approach in this paper is a two step process, model reduction followed by computational reduction. Model reduction is accomplished by computing balanced empirical gramians. Computational reduction is accomplished by using the method of in situ adaptive tabulation (ISAT) which was previously developed for computational reduction of turbulent flame direct numerical simulations and is extended to the sequential NMPC framework in this work. A case study is performed with a binary distillation column model with 32 states. By computing balanced empirical gramians the number of states is reduced to five. With ISAT, the computational speed is 85 times faster than the original NMPC while maintaining the accuracy of the nonlinear model. Since ISAT is a storage and retrieval method, it is compared to artificial neural networks in another case study. This case study is performed with a dual CSTR model with 6 states. Open loop and closed loop step tests are performed to demonstrate the superior quality of ISAT in extrapolating outside of the training domain.

This paper investigates issues related to the numerical simulation of rate-based models for catalytic distillation processes. Special emphasis is placed on spatial discretization in the solution of steady-state models describing packed reactive distillation columns. A general rate-based model for packed reactive distillation is briefly presented, along with its underlying assumptions. This mathematical model is then discretized along its spatial dimensions using different finite-difference schemes. In this paper, steady-state simulations are considered to assess various properties of the different discretization methods. Using a tert-amyl methyl ether (TAME) packed reactive distillation as a case study, we show that a cell-based approach, similarly to a first-order finite-difference approximation, is inefficient in converging to the solution of the mathematical model. To address this problem, a higher-order discretization scheme is used, and its advantages are illustrated. This latter method is of interes...

We present some insights on the relative error matrix between the process transfer function matrix and its diagonal matrix, which has been used in analyzing dynamic interactions by several authors. Proposed interaction measures can be interpreted in terms of differences of the complementary sensitivity function matrices when loops are closed, extending the concept of gain changes in the relative gain array. To employ the interaction measures, control systems are designed in advance using internal model control. With changes in the closed-loop time constant, proper pairings based on closed-loop response speeds can be obtained.

Existing strategies for the solution of the nonlinear dynamic data reconciliation problem use the process model as a constraint which is expressed as a differential−algebraic equation system. Modeling a process using conservation laws may require a considerable number of equations to obtain an accurate representation of the system. It is possible to model a process using commercial dynamic simulation software. However, this also requires the solution of a large number of equations interfaced to reliable optimization software in order to perform data reconciliation. This paper focuses on two new approaches for dynamic data reconciliation using model identification tools and commercial dynamic simulation software. The first one is based on an analogy to the nonlinear dynamic data reconciliation method developed by Liebman et al.1 The second approach uses time series analysis to generate a simplified model of the plant. A simplified process model is generated by a model identification method to replace the s...

A steady-state equilibrium model and a rate-based model were developed and compared for packed reactive distillation columns for the production of tert-amyl methyl ether (TAME) and methyl acetate. For the methyl acetate system, both models yield good agreement with experimental data. The results predicted by the equilibrium and rate-based models are similar with very few differences found under all simulation conditions. However, the rate-based model is much more complicated than the equilibrium model and also more difficult to converge. The influence of the reflux ratio, the operating pressure, the catalyst amount, and the heat loss was studied. It was found that reactive distillation behaves very differently from ordinary distillation. The existence of an optimal reflux ratio and an optimal pressure is predicted by both models.

This paper presents a computational approach to determine a reduced order model of a nonlinear system. The procedure is closely related to balanced model reduction and introduces the concept of covariance matrices for local controllability and observability analysis of a nonlinear system. These covariance matrices are an extension of gramians of a linear system and are used to determine unobservable and uncontrollable parts of the system for a given operating region. Additionally, an algorithm is introduced that eliminates these nonminimal parts of the model and can further reduce the model, i.e., the number of state variables. This minimal realization/model reduction procedure is simple to implement and can be applied locally to any stable system without making any assumptions about observability and controllability. Examples are presented to demonstrate the procedure. When the algorithm is applied to linear systems, it reduces to well-known techniques for minimal realization and balanced model reduction.

It is hard to design robust control systems for interacting multivariable processes. When processes are ill-conditioned, difficulties become more serious due to sensitivities on plant variations. For robust control systems, we decompose systems to slow subsystems and fast subsystems. When the slow subsystem is detuned and maintained to be slow, the fast subsystems can be made to be robust to plant variations. On the other hand, the slow loop is sensitive to plant variations. Adaptive control technique is introduced to the slow loop to maintain performances of the slow loop. Because the slow subsystem is chosen to be single-input single-output and controlled to be slow, the adaptive control scheme is very simple. Simulations show that the proposed control system has nominal performances similar to usual multivariable control systems while maintaining better robustness.

For some ill-conditioned processes, small element errors due to the classical element-by-element identification cannot be tolerated and control systems based on models with such errors can suffer f...

In a modern chemical plant, distributed control systems provide access to a wide variety of process data that typically contain both random and gross errors. Although data reconciliation has been an often-studied topic to reduce the amount of error in measurements, applications of dynamic data reconciliation to problems in industry are virtually nonexistent. This is because standard formulations of the dynamic data reconciliation problem result in large nonlinear programs, which are thought to be too difficult to solve in real time. With increases in computing speeds and improvements in optimization technology, these concerns are diminishing. This paper describes the implementation of a dynamic data reconciliation application at an ExxonMobil Chemical Company plant. The specifics of the application will be discussed and some solutions to the practical problems encountered in an industrial setting are presented.

Some ill-conditioned processes are very sensitive to small elementwise uncertainties arising in classical element-by-element model identifications. For such processes, accurate identification of singular values and right singular vectors are more important than those of the elements themselves. Singular values and right singular vectors can be found by iterative identification methods that implement the input and output transformations iteratively. Methods based on SVD decomposition, QR decomposition, and LU decomposition are proposed and compared with Kuong and MacGregor's method. Convergence proofs are given. These SVD and QR methods use orthogonal matrices for the transformations that cannot be calculated analytically in general, and so they are hard to apply to dynamic processes, whereas the LU method uses simple analytic transformations and can be directly applied to dynamic processes.

For some practical disturbances and uncertainties, acceptable control systems can be designed even for MIMO ill-conditioned processes. A class of ill-conditioned processes has been studied where an inversion-based control system is not sensitive to the diagonal input uncertainties. We show for this class that element uncertainties arising from usual element-by-element model identification do not significantly degrade the control performance. Some useful measures for uncertainty effects on the control performance, stability, and inversion are proposed.

This paper presents a novel approach to extract and to characterize steady state interaction by means of closed-loop structural analysis. A steady state interaction measure equivalent to the relative gain array is obtained. Due to the broad derivation basis and structural significance, the new measure allows for unified characterization of steady state interaction and systematic analysis of the impact of loop interaction on decentralized control systems. Important issues, such as the absolute and relative interaction, the size and the direction of interaction, positive and negative interaction, one way interaction problem, and the usefulness as well as the limitations of steady state interaction measures are systematically addressed. The impact of steady state interaction on closed-loop performance and stability is analyzed on the basis of individual loops within a classical paradigm. The analysis applies to nonrational processes. The use of steady state interaction measures as tools for variable pairing and controller design to counteract loop interaction for closed-loop performance and stability is demonstrated.

This paper describes the experimental application of Nonlinear Model-Predictive Control (NMPC) in conjunction with on-line state and parameter estimation to control a packed distillation column. Due to mass transfer effects, the process gain changes sign within the region of operation, which precludes the use of a linear controller for closed-loop control.

The different types of packings commonly used in distillation columns possess different wetting characteristics due to their shape and the nature of their surfaces. The way interfacial area is created, renewed, and maintained in these different geometries has a marked effect on the efficiency of the packings, as well as on the response of the column to different operating conditions. We explore the effect of the interfacial area behavior of different packings on the way distillation column responds to varying loads. Implications for the design of control strategies are discussed as well. Experimental data obtained previously were used to establish the basis for the analysis