Predictive control of transport-reaction processes

This work focuses on the development of computationally efficient predictive control algorithms for nonlinear parabolic and hyperbolic PDEs with state and control constraints arising in the context of transport-reaction processes. We first consider a diffusion-reaction process described by a nonlinear parabolic PDE and address the problem of stabilization of an unstable steady-state subject to input and state constraints. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDE, which are subsequently used for controller design. Various model predictive control (MPC) formulations are constructed on the basis of the finite dimensional approximations and are demonstrated, through simulation, to achieve the control objectives. We then consider a convection-reaction process example described by a set of hyperbolic PDEs and address the problem of stabilization of the desired steady-state subject to input and state constraints, in the presence of disturbances. An easily implementable predictive controller based on a finite dimensional approximation of the PDE obtained by the finite difference method is derived and demonstrated, via simulation, to achieve the control objective.     

Predictive control of transport-reaction processes

This work focuses on the development of computationally efficient predictive control algorithms for nonlinear parabolic and hyperbolic PDEs with state and control constraints arising in the context of transport-reaction processes. We first consider a diffusion-reaction process described by a nonlinear parabolic PDE and address the problem of stabilization of an unstable steady-state subject to input and state constraints. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDE, which are subsequently used for controller design. Various model predictive control (MPC) formulations are constructed on the basis of the finite dimensional approximations and are demonstrated, through simulation, to achieve the control objectives. We then consider a convection-reaction process example described by a set of hyperbolic PDEs and address the problem of stabilization of the desired steady-state subject to input and state constraints, in the presence of disturbances. An easily implementable predictive controller based on a finite dimensional approximation of the PDE obtained by the finite difference method is derived and demonstrated, via simulation, to achieve the control objective.     

Linear model predictive control for transport‐reaction processes

The article deals with systematic development of linear model predictive control algorithms for linear transport‐reaction models emerging from chemical engineering practice. The finite‐horizon constrained optimal control problems are addressed for the systems varying from the convection dominated models described by hyperbolic partial differential equations (PDEs) to the diffusion models described by parabolic PDEs. The novelty of the design procedure lies in the fact that spatial discretization and/or any other type of spatial approximation of the process model plant is not considered and the system is completely captured with the proposed Cayley‐Tustin transformation, which maps a plant model from a continuous to a discrete state space setting. The issues of optimality and constrained stabilization are addressed within the controller design setting leading to the finite constrained quadratic regulator problem, which is easily realized and is no more computationally intensive than the existing algorithms. The methodology is demonstrated for examples of hyperbolic/parabolic PDEs. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2644–2659, 2017     

Model predictive control of a second-order hyperbolic transport-reaction process

The model predictive controller (MPC) design is developed for a tubular chemical reactor, considering a second-order hyperbolic partial differential equation as the model of the transport-reaction process with boundary actuation. Without loss of generality, closed–closed boundary conditions and relaxed total flux are assumed. At the same time, the model is discretized in time by the Cayley–Tustin method, and, under the assumption that only the reactor's output is measurable, the observer design for the state reconstruction is addressed and integrated with the MPC design. The Luenberger observer gain is obtained by solving the operator Ricatti equation in the discrete-time setting, while the MPC accounts for constrained and optimal control. The simulations show that the output-based MPC design stabilizes the system under the input and output constraints satisfaction. In addition, to address the models' disparities, the results for both parabolic and hyperbolic equations are presented and discussed.     

Approximate dynamic programming based control of hyperbolic PDE systems using reduced-order models from method of characteristics

Approximate dynamic programming (ADP) is a model based control technique suitable for nonlinear systems. Application of ADP to distributed parameter systems (DPS) which are described by partial differential equations is a computationally intensive task. This problem is addressed in literature by the use of reduced order models which capture the essential dynamics of the system. Order reduction of DPS described by hyperbolic PDEs is a difficult task as such systems exhibit modes of nearly equal energy. The focus of this contribution is ADP based control of systems described by hyperbolic PDEs using reduced order models. Method of characteristics (MOC) is used to obtain reduced order models. This reduced order model is then used in ADP based control for solving the set-point tracking problem. Two case studies involving single and double characteristics are studied. Open loop simulations demonstrate the effectiveness of MOC in reducing the order and the closed loop simulations with ADP based controller indicate the advantage of using these reduced order models.     

Feedback control of hyperbolic distributed parameter systems

Hyperbolic distributed parameter systems (DPS) represent a large number of industrial processes with spatially nonuniform operating variable profiles. Research has been conducted to develop high-performance control strategies for these systems by exploiting their high-fidelity models. In this paper, a feedback control method that yields improved performance is proposed for DPS modelled by first-order hyperbolic partial differential equations (PDEs) using the method of characteristics. Simulation results show that this method can provide effective control for the systems modelled by a scalar PDE as well as a system of PDEs. Further, it can efficiently compensate the effect of model-plant mismatch and effectively reject the disturbances.     

Heat exchanger system boundary regulation

The boundary feedback regulator design for heat exchangers with delayed feedback is developed. Counter-flow/parallel-flow heat exchanger systems described by a pair of coupled transport hyperbolic partial differential equations (PDEs) with delayed boundary feedback loop modeled by the boundary time lag are considered. The coupled transport hyperbolic PDEs and boundary delay by application of boundary transformation are transformed in the corresponding linear infinite-dimensional system utilized in the regulator design. The regulator design initially addresses a full state feedback controller realization augmented by the observer design to achieve simultaneously output exponential stabilization as well as tracking and disturbance rejection of polynomial and/or harmonic type of reference signals. The simulations studies demonstrate the proposed design for counter-flow and parallel-flow heat exchangers, two common configurations present in industrial practice.     

EIGENVALUE SPECTRA AND MODAL CONTRIBUTIONS FOR COUNTERFLOW REACTOR MODELS

Counterflow reactor models fall into three classes of partial differential equations: hyperbolic, parabolic, and mixed hyperbolic-parabolic. These have been analyzed to determine the behavior of their eigenvalues and their modal contributions. Using an asymptotic analytical technique (WKB theory), hyperbolic p.d.e. systems and mixed p.d.e. systems with characteristics similar to hyperbolic systems were found to have a “defective” internal structure, making them generally undesirable for modeling or control applications requiring low-order models. Parabolic systems, or mixed systems with characteristics similar to parabolic systems, were found to be “well-behaved”. Hence, where it is possible to choose the type of model to apply to a specific reactor, the choice of the parabolic form is strongly suggested to mitigate potential structural problems.     

On the Evaluation of the Exit Boundary Condition for the Axial Dispersion Bioreactor System

Employment of the axial dispersion theory to model chemical or biochemical processes often results in coupled parabolic partial differential equations (PDEs). The classical Danckwerts boundary conditions have been widely used to solve these PDEs despite the fact that artificial suppression of the exit concentration gradient to zero may be physically unrealistic and may cause numerical instability. In this study, a recently developed exit boundary condition is shown to be inapplicable to model processes which demonstrate significant differences in the dynamics of components – typically found in biochemical processes. Using an activated sludge process and a pilot-scale subsurface flow (SSF) constructed wetland as case studies, we demonstrated in this study that a time-dependent exit boundary condition is more appropriate for use with the Danckwerts inlet boundary condition and the axial dispersion theory to model a biological system operating at near-plug flow conditions. Instead of using steady-state results we found that evaluation of alternative exit boundary condition using dynamic simulation results is more realistic.     

Long range pipeline leak detection and localization using discrete observer and support vector machine

A realistic pipeline modeled by a nonlinear coupled first-order hyperbolic partial differential equations (PDEs) system is studied for the long transportation pipeline leak detection and localization. Based on the so-called water hammer equation, a linear distributed parameter system is obtained by linearization. The structure and energy preserving time discretization scheme (Cayley–Tustin) is used to realize a discrete infinite-dimensional hyperbolic PDEs system without spatial approximation or model order reduction. In order to reconstruct pressure and mass flow velocity evolution with limited measurements, a discrete-time Luenberger observer is designed by solving the operator Riccati equation. Based on this distributed observer system, data on different normal and leakage conditions (various leak amounts and positions) are generated and fed to train a support vector machine model for leak detection, amount, and position estimation. Finally, the leak detection, amount estimation, and localization effectiveness of the developed method are proved by a set of simulations. © 2019 American Institute of Chemical Engineers AIChE J, 65: e16532 2019     

烟气脱硫传质-反应阻力特性分析

主要分析脱硫反应中形成产物层之前的传质反应特性.研究发现,可用蒂勒（Thiele）数来描述以同时反映化学反应速率、扩散速率的影响.脱硫剂颗粒形成产物层之前的转化率仅由蒂勒数决定,随其增加而降低,与此阶段持续时间无关.分析表明,颗粒半径、孔隙率、孔隙分布方式等都可能会改变在产物层形成过程中的转化率和过程特性.粒径较小时,产物层形成过程的转化率随粒径增大而降低;粒径增大到一定程度后,在较大粒径范围内转化率基本不随粒径变化而变化;孔隙率越大,形成产物层时转化率越高.     

Optimal boundary control of a diffusion–convection-reaction PDE model with time-dependent spatial domain: Czochralski crystal growth process

In this paper the optimal boundary control problem for diffusion–convection-reaction processes modeled by partial differential equations (PDEs) defined on time-dependent spatial domains is considered. The model of the transport system with time-varying domain arises in the context of high energy consuming Czochralski crystal growth process in which the crystal temperature regulation must successfully account for the change in the crystal spatial domain due to the crystal growth process realized by the pulling crystal out of melt. Starting from the first principles of continuum mechanics and transport theorem the time-varying parabolic PDE describing temperature evolution is derived and represented as a nonautonomous parabolic evolution system on an appropriately defined function space which is exactly transformed in the infinite-dimensional boundary control problem for which a boundary linear quadratic regulator is proposed. Properties of the solution of the time-varying parabolic PDEs given by the two-parameter evolutionary system are utilized in the synthesis of the optimal boundary regulator, and the control law is applied to the model given by a two-dimensional partial differential equation in the cylindrical coordinates representing the Czochralski crystal growth process with one-dimensional growth direction. Finally, numerical results demonstrate optimal stabilization of the two-dimensional temperature distribution in the crystal.     

Nonlinear dimension reduction based neural modeling for distributed parameter processes

Many chemical processes are nonlinear distributed parameter systems with unknown uncertainties. For this class of infinite-dimensional systems, the low-order model identification from process data is very important in practice. The dimension reduction with a principal component analysis (PCA) is only a linear approximation for nonlinear problem. In this study, a nonlinear dimension reduction based low-order neural model identification approach is proposed for nonlinear distributed parameter processes. First, a nonlinear principal component analysis (NL-PCA) network is designed for the nonlinear dimension reduction, which can transform the high-dimensional spatio-temporal data into a low-dimensional time domain. Then, a neural system can be easily identified to model this low-dimensional temporal data. Finally, the spatio-temporal dynamics can be reproduced using the nonlinear time/space reconstruction. The simulations on a typical nonlinear transport-reaction process show that the proposed approach can achieve a better performance than the linear PCA based modeling approach.     

Approximate ODE models for population balance systems

We propose an approximate polynomial method of moments for a class of first-order linear PDEs (partial differential equations) of hyperbolic type, involving a filtering term with applications to population balance systems with fines removal terms. The resulting closed system of ODEs (ordinary differential equations) represents an extension to a recently published method of moments which utilizes least-square approximations of factors of the PDE over orthogonal polynomial bases. An extensive numerical analysis has been carried out for proof-of-concept purposes. The proposed modeling scheme is generally of interest for control and optimization of processes with distributed parameters.     

Predictive control of parabolic PDEs with boundary control actuation

This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with boundary control actuation subject to input and state constraints. Under the assumption that measurements of the PDE state are available, various finite-dimensional and infinite-dimensional predictive control formulations are presented and their ability to enforce stability and constraint satisfaction in the infinite-dimensional closed-loop system is analyzed. A numerical example of a linear parabolic PDE with unstable steady state and flux boundary control subject to state and control constraints is used to demonstrate the implementation and effectiveness of the predictive controllers.     

The computer-derivation of thermodynamic equations Part I. First and Second Derivatives for Simple Systems

An algorithm is described for the systematic derivation of expressions for first and second thermodynamic derivatives involving the use of Jacobians. The algorithm is implemented by means of a list-processing language in conjunction with a commercially available symbolic mathematical system for use with a microcomputer. The algorithm is intended to replace tables and procedures currently used “by hand”. Attention is limited in this paper to simple systems with two degrees of freedom, but the algorithm is to be extended ultimately to chemical systems subject to equilibrium constraints. Examples are given to illustrate the procedure.     

Modeling intra-particle convection in heterogeneous non-catalytic reacting systems

A dynamic mechanistic model of a multi-phase heterogeneous batch reactor processing a non-catalytic fluid-solid reaction is developed and numerically solved. The model takes into account both intra-particular convection and diffusion phenomena, as well as chemical reaction between the fluid and the solid. By addressing the until now ignored case of convection originated by the continuous increase of the particle porosity, this work enables a more realistic modeling of many non-catalytic fluid-solid reaction systems. The dependency of the convective term on the chemical reaction rates gives rise to a space and time dependency of the intra-particle velocity thus increasing the mathematical complexity of the problem. The model is characterized by a set of PDEs coupled with adequate boundary conditions that are solved by the method of lines. An example of application of great importance to the chemical pulping of wood is used, emphasizing the differences with the typical situation where only diffusion is considered.     

Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

Many chemical and industrial processes are complex, and the dynamics of such processes cannot be explained using a partial differential equation (PDE) or a system of PDEs with constant coefficients. Parametric PDEs, that is, PDEs with their coefficients varying across time or space, are utilized for this purpose. The non-availability of data at all spatial locations and partially available process knowledge add to the complexity of modelling such processes. This paper proposes a framework to discover parametric PDEs using data-driven and hybrid modelling approaches with the temperature dynamics of steam-assisted gravity drainage (SAGD) process in an oil reservoir as the system under study. We utilize an ensemble of 200 realizations of the temperature dynamics generated using the variogram for the PDE discovery. Permeability, which is one of the oil reservoir's petrophysical properties, is used to develop the hybrid models. We infer that utilizing partial process knowledge aids in improving the model's accuracy.