Speed-up of Iterative Real-Time Optimization by Estimating the Steady States in the Transient Phase using Nonlinear System Identification

Cadavid, José; Hernández, Reinaldo; Engell, Sebastian

doi:10.1016/j.ifacol.2017.08.1626

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…This can be realized by reformulating the gradient estimation problem as a time-varying parameter estimation, which enables the use of estimation techniques such as recursive extended least-squares (RELS) algorithms. 66,67 Another possibility is the use of system identification methods; 68 their applicability to the presented case study has already been demonstrated in simulation studies. 69…”

Section: Discussionmentioning

confidence: 92%

“…A possible approach to overcome these limitations, which is currently being considered, is the estimation of the steady state gradient (or the steady-state itself) in the transient phase. This can be realized by reformulating the gradient estimation problem as a time-varying parameter estimation, which enables the use of estimation techniques such as recursive extended least-squares (RELS) algorithms. , Another possibility is the use of system identification methods; their applicability to the presented case study has already been demonstrated in simulation studies …”

Section: Discussionmentioning

confidence: 99%

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Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) has gained growing attention during the past few years as a useful approach to boost process performance while safety and environmental constraints are satisfied. Despite the increasing acceptance of RTO in traditional industries such as petrochemical and refineries, its application to novel chemical processes remains limited. This can be partially explained by the fact that only inaccurate models are available and the performance of the traditional RTO scheme suffers in the presence of plant-model mismatch. During the past few years, the so-called modifier-adaptation schemes for real-time optimization have been gaining popularity as an efficient tool to handle plant-model mismatch. So far, there are only few published works regarding experimental implementations. In this contribution, a reliable RTO scheme that is able to deal with model uncertainty and measurement noise is applied to a novel transition metal complex catalyzed process that is performed in a continuously operated miniplant. The experimental results show that the proposed scheme is able to drive the process to an improved operation despite significant plant-model mismatch demonstrating the applicability of the method to real processes.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

Self Cite

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show abstract

“…Several other approaches which take advantage of the use of transient measurements, most of which utilize previous measurements as the basis for the estimation of the steady state gradient at the current operating point. These include, but are not limited to, Dynamic MAWQA Cadavid et al, 2017) which utilizes the MAWQA framework with steady state estimation techniques during the transient to improve convergence rates.…”

Section: Gradient Estimationmentioning

confidence: 99%

Real-time optimization via modifier adaptation of closed-loop processes using transient measurements

Speakman

François

2020

Computers & Chemical Engineering

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Real-time optimization (RTO) has the ability to boost the performance of a process whilst satisfying the constraints by using process measurements, driving the operating conditions towards optimality. Modifier adaptation (MA) is a methodology of RTO which can find the optimal operating point of a process even in the presence of plant-model mismatch. This work presents an extension to MA through the combination of two established frameworks, allowing for the optimization of a controlled process using transient measurements whilst using a steady-state open-loop model. In addition, an approach for model-based gradient estimation, despite the mismatch between the degrees of freedom of the closed-loop plant and the available open loop model is suggested that does not necessitate amending the model. The proposed scheme is illustrated on a case study of a CSTR and a distillation column, detailing how the gradient can be estimated.

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“…Modifier adaptation-based iterative RTO methods require steady-state process measurements to compute a new input that provides a lower limit to the time between iterates. This can be alleviated by the use of transient process measurements [27][28][29][30][31], but this is outside the scope of this paper. In addition to the time required to reach a steady-state, the convergence to a process optimum may also be slowed down due to delays in obtaining measurement information.…”

Section: Introductionmentioning

confidence: 99%

Handling Measurement Delay in Iterative Real-Time Optimization Methods

Mukkula

Engell

2021

Processes

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This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradient correction terms that are computed from measured variables at the steady-states of the plant. This implies that the sampling time of the iterative RTO scheme is lower-bounded by the time to reach a new steady-state after the previously computed inputs were applied. If analytical process measurements (PAT technology) are used to obtain the steady-state responses, time delays occur due to the measurement delay of the PAT device and due to the transportation delay if the samples are transported to the instrument via pipes. This situation is quite common because the PAT devices can often only be installed at a certain distance from the measurement location. The presence of these time delays slows down the iterative real-time optimization, as the time from the application of a new set of inputs to receiving the steady-state information increases further. In this paper, a proactive perturbation scheme is proposed to efficiently utilize the idle time by intelligently scheduling the process inputs taking into account the time delays to obtain the steady-state process measurements. The performance of the proposed proactive perturbation scheme is demonstrated for two examples, the Williams–Otto reactor benchmark and a lithiation process. The simulation results show that the proposed proactive perturbation scheme can speed up the convergence to the true plant optimum significantly.

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Speed-up of Iterative Real-Time Optimization by Estimating the Steady States in the Transient Phase using Nonlinear System Identification

Cited by 9 publications

References 14 publications

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Real-time optimization via modifier adaptation of closed-loop processes using transient measurements

Handling Measurement Delay in Iterative Real-Time Optimization Methods

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