Determination of biogeochemical parameters in eutrophication models with simultaneous dynamic optimization approaches

This work addresses a parameter estimation problem in an ecological water quality model through a simultaneous dynamic optimization approach. The model is based on first principles and has a large number of parameters, which must be estimated based on data collected in the water body under study. Gradients of state variables are considered along the water column, rendering a partial differential equation problem, which is transformed into a differential algebraic (DAE) one by spatial discretization in several water layers. Within a simultaneous approach, the DAE constrained optimization problem is transformed into a large-scale nonlinear programming problem, with a weighted least squares objective function. Main biogeochemical parameters have been obtained, which allow a close representation of the lake dynamics, as it is shown in the numerical results.     

Extending explicit and linearly implicit ODE solvers for index-1 DAEs

Nonlinear differential-algebraic equations (DAE) are typically solved using implicit stiff solvers based on backward difference formula or RADAU formula, requiring a Newton–Raphson approach for the nonlinear equations or using Rosenbrock methods specifically designed for DAEs. Consistent initial conditions are essential for determining numeric solutions for systems of DAEs. Very few systems of DAEs can be solved using explicit ODE solvers. This paper applies a single-step approach to system initialization and simulation allowing for systems of DAEs to be solved using explicit (and linearly implicit) ODE solvers without a priori knowledge of the exact initial conditions for the algebraic variables. Along with using a combined process for initialization and simulation, many physical systems represented through large systems of DAEs can be solved in a more robust and efficient manner without the need for nonlinear solvers. The proposed approach extends the usability of explicit and linearly implicit ODE solvers and removes the requirement of Newton–Raphson type iteration.     

Sensitivity analysis of differential-algebraic equations and partial differential equations

Sensitivity analysis generates essential information for model development, design optimization, parameter estimation, optimal control, model reduction and experimental design. In this paper we describe the forward and adjoint methods for sensitivity analysis, and outline some of our recent work on theory, algorithms and software for sensitivity analysis of differential-algebraic equation (DAE) and time-dependent partial differential equation (PDE) systems.     

Identifiability of Non‐Linear Differential Algebraic Systems via a Linearization Approach

A system is identifiable if there exists a unique relationship between its input‐output behaviour and the parameter values. Differential‐Algebraic Equation (DAE) systems have an input‐output behaviour that is described by a parameterized set of ordinary differential and algebraic equations. Methods proposed in the literature to test the identifiability of DAE systems are based on the tools of differential algebra and rely on time‐differentiation of model equations. As a result, even when dealing with a few states and parameters, the calculations required for these methods may become intractable. An alternative, which we propose, is to linearize the non‐linear DAE system about some rest point and then test the identifiability properties of the linearized system. In this work, we show that strong local identifiability of the linearized DAE system provides a sufficient condition for the strong local identifiability of the original non‐linear DAE system.     

Studies of distributed parameter systems: Decoupling the state-parameter estimation problem

The problem of state-parameter estimation is considered in terms of decoupling the estimation procedure. First, the theoretical preliminaries necessary for the mathematical statement of the problem are defined. Then using the extended Kalman filter (EKF) approach, the state and parameter are estimated by applying the solution techniques to a distributed parameter system. Next, the state estimation problem is decoupled from the parameter estimation problem and by using a numerical example, the advantage of this decoupling procedure is demonstrated. The numerical results show that convergence can be improved when this decoupling procedure is employed. The effect of the location of the measurements on the estimation problem is also analysed in this work. The results show that the convergence of the problem depends on the location as well as the number of measurements.     

Model parameter estimation and feedback control of surface roughness in a sputtering process

This work focuses on model parameter estimation and model-based output feedback control of surface roughness in a sputtering process which involves two surface micro-processes: atom erosion and surface diffusion. This sputtering process is simulated using a kinetic Monte Carlo (kMC) simulation method and its surface height evolution can be adequately described by the stochastic Kuramoto-Sivashinsky equation (KSE), a fourth-order nonlinear stochastic partial differential equation (PDE). First, we estimate the four parameters of the stochastic KSE so that the expected surface roughness profile predicted by the stochastic KSE is close (in a least-square sense) to the profile of the kMC simulation of the same process. To perform this model parameter estimation task, we initially formulate the nonlinear stochastic KSE into a system of infinite nonlinear stochastic ordinary differential equations (ODEs). A finite-dimensional approximation of the stochastic KSE is then constructed that captures the dominant mode contribution to the state and the evolution of the state covariance of the stochastic ODE system is derived. Then, a kMC simulator is used to generate representative surface snapshots during process evolution to obtain values of the state vector of the stochastic ODE system. Subsequently, the state covariance of the stochastic ODE system that corresponds to the sputtering process is computed based on the kMC simulation results. Finally, the model parameters of the nonlinear stochastic KSE are obtained by using least-squares fitting so that the state covariance computed from the stochastic KSE process model matches that computed from kMC simulations. Subsequently, we use appropriate finite-dimensional approximations of the identified stochastic KSE model to design state and output feedback controllers, which are applied to the kMC model of the sputtering process. Extensive closed-loop system simulations demonstrate that the controllers reduce the expected surface roughness by 55% compared to the corresponding values under open-loop operation.     

Subsystem decomposition and distributed state estimation of nonlinear processes with implicit time-scale multiplicity

In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems. Taking the advantage of the time scale separation, these processes are decomposed into fast subsystem and slow subsystem according to the dynamics. In the proposed method, an approach that combines the approximate solutions obtained from both the fast and slow subsystems to form a composite solution of the original system is proposed. Also, based on the fast and slow subsystems, a distributed state estimation scheme is proposed to handle the implicit time-scale multiplicity. In the proposed design, an extended Kalman filter (EKF) is designed for the fast subsystem and a moving horizon estimator (MHE) is designed for the slow subsystem. In the design, the slow subsystem is only required to send information to the fast subsystem one-directionally. The fast subsystem estimator does not send out any information. The estimators use different sampling times, that is, fast sampling of the fast state variables is considered in the fast EKF and slow sampling of the slow state variables is considered in the slow MHE. Extensive simulations based on a chemical process are performed to illustrate the effectiveness and applicability of the proposed subsystem decomposition and composite estimation architecture.     

Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

The pH neutralization process is a representative nonlinear process. If a change in feed or buffer streams is introduced, the characteristics of the titration curve are altered and the way of change in titration curve is highly nonlinear. Moreover, if the changes are introduced in the middle of operation, then the nature of the process becomes nonlinear and time-varying. This is the one of the reason why conventional PID controller may fail. Even though the use of buffer solution may alleviate the nonlinearity, the improvement may be limited. A better way to tackle this type of process is to use nonlinear model-based control techniques with online parameter estimation. However, in most cases, the measurements of the process are not adequate enough so that the full state feedback control techniques can be utilized. If the states and crucial parameters are estimated online simultaneously, the effectiveness of the nonlinear state feedback control can be greatly enhanced. Thus, in this study, the capability of simultaneous estimation of states and parameters using Extended Kalman Filter (EKF) are experimentally investigated for a pH neutralization process. The process is modelled using reaction invariants and the concentrations of reaction invariants of the effluent stream (states) and the feed concentrations (parameters) are estimated online. From the comparison of experiments and simulations, it is found that the states and parameters can efficiently be identified simultaneously with EKF so that the estimated information can be exploited by state-feedback control techniques     

A DISTRIBUTED PARAMETER APPROACH TO THE DYNAMICS OF ROTARY DRYING PROCESSES

ABSTRACT

A nonequilibrium distributed parameter model for rotary drying and cooling processes described by a set of partial differitial equations with nonlinear algebraic constraints is developed in this work. These equations arise from the multi–phase heat and mass balances on a typical rotary dryer. A computational algorithm is devekped by employing a polynonial approximation ( orthogonal collocation) with a glotal splinc technique leading to a differential–algebraic equation ( DAE) system. The numerical solution is carried out by using a standard DAE solver.

The two– phase–flow heat transfer coelficient is computed by introducing a correction factor to the commonly accepted correlations. Since interaction between the falling particles are considered in the correction factor,the results are more reliable than those computed by assuming that heat transfer between a single falling particle and the drying air is unaffected by other particles. The heat transfer computations can be further justified via a study on the analogies between heat and mass transfer.

The general model devloped in this work is mathematically more ritorous yet more flexible that the lumped parameter models established by one of the authors (Douglas et al., (1993)). The three major assumptions of an equilibrium operation, perfect mixing and constant drying raic, are removed in the distributed parameter model.

The simulation results are compared with the operational data from an industrial sugar dryer and predictions from earlier models. The model and algorithm successfully predict the steady state behaviour of rotary dryers and collers. The generalized model can be applied to fertilizer drying processes in which the assumption of constant drying rate is no longer valid and the existing dynamic models are not applicable.     

Numerische Methoden zur Simulation verfahrenstechnischer Prozesse

Numerical Methods for Simulation of Chemical Engineering Processes. Essential fundamentals and the current state of the art in simulating the dynamic and the steady state behaviour of chemical engineering processes are discussed. It is shown that discretization of the spatial derivatives in the balance equations leads to a system of so-called DAE (differential algebraic equations), consisting of ordinary differential equations in time and algebraic equations. The paper discusses necessary steps to solve the DAE and mentions approved standard software for these steps as well as for the solution of the DAE as a whole.     

Distributed parameter estimation and identification for system with fast and slow dynamics: A tubular,fixed-bed catalytic reactor to form styrene

A simple and efficient on-line scheme is developed to estimate temperature and compositions along a packed bed reactor in which styrene is being produced by the dehydrogenation of ethylbenzene. Slowly varying catalyst activity is also identified. The system is distributed in time and axial position and is nonlinear in the states: temperature and nine compositions. The dehydrogenation rate is augmented with a catalyst activity parameter which is assumed to undergo a long-term exponential decay.Since the decline in catalyst activity is slow when compared to state dynamics, a quasi-steady-state approach is used to derive a state filter equation neglecting process state dynamics and assuming spatially uncorrelated measurements and model uncertainty. For this filter, temperature measurements are available from four locations along the reactor and compositions are measured only at the reactor exit. A second dynamic, Kalman filter is used to identify the slowly varying catalyst activity.The two filters, one for distributed, steady-state, state estimation and the other for dynamic catalyst activity identification, are tested by computer simulation using measurements with added white noise. Several cases for numbers of sensors and noise levels are studied. The overall scheme is efficient and useable for on-line implementation. The steady-state filter is readily extended to distributed systems in more than one spatial variable such as reactor models with axial and radial dependencies. For steady-state or static models, multiple measurements yield significant improvements in the quality of the optimal estimates. Internal measurement locations allow for the subdivision of the spatial domain for the problem and improved profile estimates.     

Consensus-based approach for parameter and state estimation of agro-hydrological systems

The development of advanced closed-loop irrigation systems requires accurate soil moisture information. In this work, we address the problem of soil moisture estimation for the agro-hydrological systems in a robust and reliable manner. A nonlinear state-space model is established based on the discretization of the Richards equation to describe the dynamics of the agro-hydrological systems. We consider that model parameters are unknown and need to be estimated together with the states simultaneously. We propose a consensus-based estimation mechanism, which comprises two main parts: (a) a distributed extended Kalman filtering algorithm used to estimate several model parameters; and (b) a distributed moving horizon estimation algorithm used to estimate the state variables and one remaining model parameter. Extensive simulations are conducted, and comparisons with existing methods are made to demonstrate the effectiveness and superiority of the proposed approach. In particular, the proposed approach can provide accurate soil moisture estimate even when poor initial guesses of the parameters and the states are used, which can be challenging to be handled using existing algorithms.     

Applying a dual extended Kalman filter for the nonlinear state and parameter estimations of a continuous stirred tank reactor

The extended Kalman filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the states or parameters of discrete-time nonlinear dynamical systems. In this paper, we consider the dual-estimation problem, the so-called dual EKF, in which both the states of a dynamical system and its parameters are estimated simultaneously, given only noisy observations. The main contribution of this paper is to show the efficacy of a proposed simplified dual-EKF technique (which in this work will be referred to as the dual EKF-2) in comparison with the conventional joint EKF. This has been demonstrated by conducting simulation studies on a CSTR which has been dynamically simulated using the HYSYS simulation package. Extensive analysis revealed that, not only the dual-EKF approach can achieve optimal state- and parameter-estimation performances comparable to the joint EKF, but also it has the main advantage of carrying out separate estimations of the states and parameters.     

Model predictive control for the reactant concentration control of a reactor

The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors.     

Simulation,model‐reduction,and state estimation of a two‐component coagulation process

The issue of state estimation of an aggregation process through (1) using model reduction to obtain a tractable approximation of the governing dynamics and (2) designing a fast moving‐horizon estimator for the reduced‐order model is addressed. The method of moments is first used to reduce the governing integro‐differential equation down to a nonlinear ordinary differential equation. This reduced‐order model is then simulated for both batch and continuous processes and the results are shown to agree with constant Number Monte Carlo simulation results of the original model. Next, the states of the reduced order model are estimated in a moving horizon estimation approach. For this purpose, Carleman linearization is first employed and the nonlinear system is represented in a bilinear form. This representation lessens the computation burden of the estimation problem by allowing for analytical solution of the state variables as well as sensitivities with respect to decision variables. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1557–1567, 2016     

Bifurcation analysis of index infinity DAE parabolic models describing reactors and reacting flows

We show that most steady‐state models of chemical reactors and reacting flows in which convection effects are dominant and diffusion/conduction is neglected in the flow direction but included in the transverse directions, may change from parabolic type with a unique solution to index infinity differential‐algebraic equation (DAE) type with an infinite number of steady‐state solutions depending on the values of the reaction parameters. When a model is of index infinity, standard numerical methods may find only one of the solutions corresponding to latest possible ignition. We present complete bifurcation analysis of these models, a method for finding all solutions, determine the stability and, for some simpler cases, the domain of initial conditions attracted to these states. We also demonstrate that the various steady‐state solutions of the DAE systems are best found by integrating the transient hyperbolic versions of the models with appropriately selected capacitance terms and initial conditions. © 2016 American Institute of Chemical Engineers AIChE J, 63: 295–305, 2017     

Generation of discrete first‐ and second‐order sensitivities for single shooting

In this work, an approach to the forward generation of discrete first‐ and second‐order sensitivities is proposed. For this purpose, an algorithm has been developed, which can basically be applied to general implicit differential‐algebraic equation (DAE) systems. Moreover, the approach has been tailored to both the generation of directional derivatives and sensitivities with respect to discontinuous control trajectories. The implementation of the method is discussed here for the orthogonal collocation method based on Legendre–Gauss–Radau points and considering the linear implicit DAE type, which arises in problems related to chemical engineering. Lastly, the approach has been applied to three case studies of different complexities. The corresponding performance for the generation of Jacobian and Hessian information is discussed in detail. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3110–3122, 2012     

未知输入扩展卡尔曼滤波的收敛性分析及其非线性故障诊断

Unknown input observer is one of the most famous strategies for robust fault diagnosis of linear systems, but studies on nonlinear cases are not sufficient. On the other hand, the extended Kalman filter （EKF） is wellknown in nonlinear estimation, and its convergence as an observer of nonlinear deterministic system has been derived recently. By combining the EKF and the unknown input Kalman filter, we propose a robust nonlinear estimator called unknown input EKF （UIEKF） and prove its convergence as a nonlinear robust observer under some mild conditions using linear matrix inequality （LMI）. Simulation of a three-tank system “DTS200”, a benchmark in process control, demonstrates the robustness and effectiveness of the UIEKF as an observer for nonlinear systems with uncertainty, and the fault diagnosis based on the UIEKF is found successful.     

A DISTRIBUTED PARAMETER APPROACH TO THE DYNAMICS OF ROTARY DRYING PROCESSES

A nonequilibrium distributed parameter model for rotary drying and cooling processes described by a set of partial differitial equations with nonlinear algebraic constraints is developed in this work. These equations arise from the multi-phase heat and mass balances on a typical rotary dryer. A computational algorithm is devekped by employing a polynonial approximation ( orthogonal collocation) with a glotal splinc technique leading to a differential-algebraic equation ( DAE) system. The numerical solution is carried out by using a standard DAE solver.

The two- phase-flow heat transfer coelficient is computed by introducing a correction factor to the commonly accepted correlations. Since interaction between the falling particles are considered in the correction factor,the results are more reliable than those computed by assuming that heat transfer between a single falling particle and the drying air is unaffected by other particles. The heat transfer computations can be further justified via a study on the analogies between heat and mass transfer.

The general model devloped in this work is mathematically more ritorous yet more flexible that the lumped parameter models established by one of the authors (Douglas et al., (1993)). The three major assumptions of an equilibrium operation, perfect mixing and constant drying raic, are removed in the distributed parameter model.

The simulation results are compared with the operational data from an industrial sugar dryer and predictions from earlier models. The model and algorithm successfully predict the steady state behaviour of rotary dryers and collers. The generalized model can be applied to fertilizer drying processes in which the assumption of constant drying rate is no longer valid and the existing dynamic models are not applicable.