A long short-term memory based Quasi-Virtual Analyzer for dynamic real-time soft sensing of a Simulated Moving Bed unit

Marrocos, Paulo H.; Iwakiri, Igor G.I.; Martins, Márcio A. F.; Rodrigues, Alírio E.; Loureiro, José M.; Ribeiro, Ana M.; Nogueira, Idelfonso B. R.

doi:10.1016/j.asoc.2021.108318

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…It can be written .25ex2ex z false( k false) = F false( [ z false( k − 1 false) , ... , z false( k − 1 − n normala false) , u false( k − d false) , ... , u false( k − d − n normalb false) ] false) y false( k false) = z false( k false) + v false( k false) where n a and n b are the number of past values and d is the delay. The system order ( n a and n b ) is independent of the chosen function used to approximate the true unknown F and should be carried before any parameter estimation. , To identify n a and n b , the Lipschitz coefficient method is used.…”

Section: Methodsmentioning

confidence: 99%

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Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

et al. 2023

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Model-based optimization of simulated moving bed reactors (SMBRs) requires efficient solvers and significant computational power. Over the past years, surrogate models have been considered for such computationally demanding optimization problems. In this sense, artificial neural networks�ANNs�have found applications for modeling the simulated moving bed (SMB) unit but not yet been reported for the reactive SMB (SMBR). Despite ANNs' high accuracy, it is essential to assess its capacity to represent the optimization landscape well. However, a consistent method for optimality assessment using surrogate models is still an open issue in the literature. As such, two main contributions can be highlighted: the SMBR optimization based on deep recurrent neural networks (DRNNs) and the characterization of the feasible operation region. This is done by recycling the data points from a metaheuristic technique�optimality assessment. The results demonstrate that the DRNN-based optimization can address such complex optimization while meeting optimality.

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Section: Methodsmentioning

confidence: 99%

“…It can be written where n a and n b are the number of past values and d is the delay. The system order ( n a and n b ) is independent of the chosen function used to approximate the true unknown F and should be carried before any parameter estimation. , To identify n a and n b , the Lipschitz coefficient method is used.…”

Section: Methodsmentioning

confidence: 99%

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

et al. 2023

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“…In general, this step uses fewer epochs to allow the method to explore a wider region of the hyperspace. This is a usual approach in the literature [9,13,[26][27][28]. Once the hyperparameters are defined, the epochs are increased for the training of the final structure.…”

Section: Hyperparameter Tuning-hyperbandmentioning

confidence: 99%

Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

et al. 2022

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Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time prediction of the productivity of a chemical process. The results demonstrate that the proposed methodology can yield a soft sensor based on DL that provides reliable predictions, with precision being proven by its corresponding coverage region.

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“…Woo Sung Lee and Chang Ha Lee developed mathematical dynamic models and data-driven machine learning methods using limited experimental parameters and real industrial data to evaluate the performance of SMB systems [16]. Marrocos et al proposed a deep artificial intelligence structure with a nonlinear output error (NOE) architecture and a nonlinear autoregressive with exogenous inputs (NARX) predictor for online soft sensing, providing key information regarding the main characteristics of simulated moving bed chromatography devices [17]. Hoon et al employed a data-driven Deep Q Network, a model-free reinforcement learning method, to train a control strategy for SMB processes which approaches optimality [18].…”

Section: Introductionmentioning

confidence: 99%

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

Xie,

Zhang,

Hwang

2024

Mathematics

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Although the simulated moving bed (SMB) process boasts advantages such as high productivity and low consumption, the cost of obtaining optimized parameters through practical experiments to control the separation process can be enormous due to its complex nonlinear characteristics. Consequently, the successful transformation of the SMB separation process into a mathematical dynamic model for computer simulation would greatly reduce the research costs associated with experimental studies. In this study, the Crank–Nicolson method was employed to discretize and dynamize the SMB process, enabling the simulation of processes under both linear and Langmuir isotherms. The results of the simulation experiments demonstrated the feasibility and high efficiency of this approach, thereby establishing a solid foundation for further advancements in online control strategies.

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A long short-term memory based Quasi-Virtual Analyzer for dynamic real-time soft sensing of a Simulated Moving Bed unit

Cited by 7 publications

References 39 publications

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

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