This paper is aimed at identifying a linear time-invariant dynamical model (LTI model with lumped parameters) of an activated sludge process. Such a system is characterized by stiff dynamics, nonlinearities, time-vari...
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This paper is aimed at identifying a linear time-invariant dynamical model (LTI model with lumped parameters) of an activated sludge process. Such a system is characterized by stiff dynamics, nonlinearities, time-variant parameters, recycles, multivariability with many cross-couplings and wide variations in the inflow and the composition of the incoming wastewater. In this simulation study, a discrete-time identification approach based on subspace methods is applied in order to estimate a nominal MIMO state-space model around a given operating point, by probing the system in open-loop with multi-level random signals. Six subspace algorithms are used and their performances are compared based on adequate quality criteria, taking into account identification/validation data. As a result, the selected model is a very low-order one and it describes the complex dynamics of the process well. Important issues concerning the generation of the data set and the estimation of the model order are discussed. (C) 2002 Elsevier Science Ltd. All rights reserved.
A nonlinear observer, with the feedback gain weighted by the sensitivity of the output with respect to the state, is developed for systems with nonlinear output map.. The observer can be obtained from the extended Kal...
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A nonlinear observer, with the feedback gain weighted by the sensitivity of the output with respect to the state, is developed for systems with nonlinear output map.. The observer can be obtained from the extended Kalman filter by a special choice of time-varying weighting matrices. It is shown that the estimation error dynamics are asymptotically stable and a region of attraction is derived. The observer is applied to the top blown converter process for estimating the content of impurities in the liquid metal. Using plant data from the converter at SSAB Oxelosund AB, the observer is shown to provide accurate estimates of the carbon content. (C) 2003 Elsevier Science Ltd. All rights reserved.
In this contribution, the identification problem for the control of nonlinear simulated moving bed (SMB) chromatographic processes is addressed. For processcontrol the flow rates of extract, desorbent, and recycle of...
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In this contribution, the identification problem for the control of nonlinear simulated moving bed (SMB) chromatographic processes is addressed. For processcontrol the flow rates of extract, desorbent, and recycle of the SMB process, and the switching time are the manipulated variables. But these variables influence the process in a strongly coupled manner. Therefore, a new set of input variables is introduced by a nonlinear transformation of the physical inputs, such that the couplings are reduced considerably. The front positions of the axial concentration profile are taken as model outputs. Multilayer feedforward neural networks (NN) are utilized as approximating models of the nonlinear input-output behavior. The gradient distribution of the model outputs with respect to the inputs is used to determine their structural parameters and the network size is chosen by the SVD method. To illustrate the effectiveness of the identification method, a laboratory scale SMB process is used as an example. The simulation results of the identified model confirm a very good approximation of the first principles models and exhibit a satisfactory long-range prediction performance. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper presents a new criterion to design plant experiments for model-based real-time optimization (RTO) systems. Experiments are designed by maximizing total expected profit over a future horizon. The objective f...
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This paper presents a new criterion to design plant experiments for model-based real-time optimization (RTO) systems. Experiments are designed by maximizing total expected profit over a future horizon. The objective function for designing plant experiments includes the gain in profit by reducing the offset and variability after the experiments and loss in profit before and during experimentation. Therefore, large plant movement from the optimum during experimentation and too many experiments are penalized by the profit loss term. By solving the experimental design problem, the time to start the experiments, the number of experiments and the experimental operating conditions are determined. The proposed experimental design approach is applied to the simulated Williams-Otto reactor, where it yields higher profit for several cases than achieved by standard RTO without experimentation. (C) 2002 Elsevier Science Ltd. All rights reserved.
This article is concerned with a classical problem of how to design an inferential control system for enhancing the performance of distillation composition control. In this article, a new inferential control, termed &...
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This article is concerned with a classical problem of how to design an inferential control system for enhancing the performance of distillation composition control. In this article, a new inferential control, termed "predictive inferential control," is proposed. In the predictive inferential control system, future compositions predicted from on-line measured process variables are controlled instead of the estimates of current compositions. The key concept of the predictive inferential control is to realize feedback control with a feedforward effect by the use of the inherent nature of a distillation column. The detailed dynamic simulation results show that the proposed predictive inferential control scheme integrated with cascade control works considerably better than other control schemes. Furthermore, the improvement of the control performance through iterative modeling or control-relevant identification is also demonstrated. (C) 2002 Elsevier Science Ltd. All rights reserved.
control of reactive distillation columns is a challenging task due to the complex dynamics arising from the coupling of reaction and separation. In this paper, asymptotically exact input/output-linearization is applie...
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control of reactive distillation columns is a challenging task due to the complex dynamics arising from the coupling of reaction and separation. In this paper, asymptotically exact input/output-linearization is applied in simulation studies to ail industrial reactive distillation column which is operated by Bayer AG. The resulting control law is rather general and can be easily adopted for other reactive distillation columns. This control scheme requires knowledge of the complete state of the process and therefore an observer is designed. Asymptotically exact input/output-linearization inherits robust stability from a robust observer. It is intuitively argued that the proposed observer is robust w.r.t. both model structure and parameter errors. In order to compensate for steady state observer offsets an outer control loop with simple PI-controllers is implemented. Simulation studies evidence that in comparison with a well-tuned linear controller the nonlinear controller shows a superior performance with respect to setpoint-changes and disturbances, even in the presence of unknown input delays. (C) 2002 Elsevier Science Ltd. All rights reserved.
A new approach to defining a dynamic RGA (DRGA) is presented. The approach assumes the availability of a dynamic process model which is used to design a proportional output optimal controller. The new DRGA is defined ...
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A new approach to defining a dynamic RGA (DRGA) is presented. The approach assumes the availability of a dynamic process model which is used to design a proportional output optimal controller. The new DRGA is defined based on the resulting controller gain matrix. Two examples in which the traditional RGA gives the wrong pairings and an inaccurate indication of the amount of interaction present are discussed. One example involves transfer function models and the other an industrial recycle/reactor system. In both cases the new DRGA indicates the best pairings to use and it accurately assesses the extent of interaction present. (C) 2002 Elsevier Science Ltd. All rights reserved.
In this paper a nonlinear robust controller design task for longitudinal dynamics of autonomous road vehicles is considered. This work presents a longitudinal controller that covers the nonlinearities of the vehicle d...
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New pump-dump optimal control strategy applicable to the systems of moderate complexity has been developed which allows to drive the system to the desired objective in the ground state via electronically excited state...
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New pump-dump optimal control strategy applicable to the systems of moderate complexity has been developed which allows to drive the system to the desired objective in the ground state via electronically excited state. The strategy is based on the concept of the intermediate target in the excited state whose role is to select appropriate Franck-Condon window at a given time delay between pump and dump pulses in order to reach optimally the objective. The concept was applied on the isomerization process in Na3F2 employing our ab initio Wigner distribution approach. The pump and dump pulses have been found which suppress the radiationless transition through the conical intersection and drive the system via electronically excited state to the second isomer in the ground state with the maximal yield.
The impact of control field fluctuations on the optimal manipulation of quantum dynamics phenomena is investigated. The presence of significant field fluctuations is shown to break down the evolution into a sequence o...
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ISBN:
(纸本)081944975X
The impact of control field fluctuations on the optimal manipulation of quantum dynamics phenomena is investigated. The presence of significant field fluctuations is shown to break down the evolution into a sequence of partially coherent robust steps. Robustness occurs because the optimization process reduces sensitivity to noise-driven quantum system fluctuations. This process takes advantage of the observable expectation value being bilinear in the evolution operator and its adjoint. The consequences of this. inherent robustness bodes well for the future success of closed loop quantum optimal control experiments.
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