Machine learning modeling and genetic algorithm-based optimization of a novel pilot-scale thermosyphon-assisted falling film distillation unit

Battisti, Rodrigo; Claumann, Carlos Alberto; Manenti, Flavio; Machado, Ricardo Antônio Francisco; Marangoni, Cintia

doi:10.1016/j.seppur.2020.118122

Cited by 18 publications

(4 citation statements)

References 52 publications

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“…The study and research of tensors have attracted great attention. In recent years, more and more people use it in computer vision [29], machine learning [30], signal processing [31], pattern recognition [32], and other fields. However, due to technical limitations, the tensors we observe in real life are usually incomplete.…”

Section: Introductionmentioning

confidence: 99%

Quantization and application of low-rank tensor decomposition based on the deep learning model.

Zhao¹

2023

3C TIC

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Watching the presentation of a large-scale network is very important for network state tracking, performance optimization, traffic engineering, anomaly detection, fault analysis, etc. In this paper, we try to develop deep learning technology to solve the defect problem of tensor filling based on inner product interaction. To solve the limitations of the existing tensor-filling algorithms, a new neural tensor-filling (NTC) model is proposed. NTC model can effectively type the third-order communication between data landscapes through outer creation operation. It creates the third-order interaction mapping tensor. On this basis, the interaction between local features of the 3D neural network is studied. In this paper, another fusion neural tensor filling (Fu NTC) model is proposed to solve the problem that the NTC model can only extract the nonlinear complex structural information between potential feature dimensions. In the framework of the neural network, the NTC model and tensor decomposition model share the same potential feature embedding. It can effectively extract nonlinear feature information and linear feature information at the same time. It achieves higher precision data recovery.

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Section: Introductionmentioning

confidence: 99%

Quantization and application of low-rank tensor decomposition based on the deep learning model.

Zhao¹

2023

3C TIC

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“…Ferrández et al 17 also successfully replaced constrained mono‐objective problems with MOO for high‐pressure thermal processes in food treatment, as the latter gave a better, adequate set of parameters with less computation time. Several studies have been dedicated to MOO through GA 16,18,19 and hybrid ANN‐MOGA 20–24 . The utilization of ANN and GA can be efficient for multivariate modeling and optimization in the case of non‐linear variables.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been dedicated to MOO through GA 16,18,19 and hybrid ANN-MOGA. [20][21][22][23][24] The utilization of ANN and GA can be efficient for multivariate modeling and optimization in the case of non-linear variables.…”

Section: Introductionmentioning

confidence: 99%

Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm

Khuntia,

Mitra

2023

JSFA Reports

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BackgroundsThe polyelectrolyte complex (PEC) formation using chitosan (Cs) and alginate (Alg) has been employed to carry out Vitamin C (VC) nanoencapsulation because of its instability in adverse conditions. This method includes VC nanoencapsulation using physical cross‐linking. The effect of the mixing order as well as the different mass ratios of both the polymer and the VC content, were investigated on the size, pdi, zeta potential, encapsulation efficiency (EE%), and yield%. Hence, considering different mass ratios of both polymer (4:1–1:4) and VC content (10%–30% w/w of Cs) as independent variables, PECs were formed using 0.1% (w/v) of Cs and Alg solution.ResultThe result showed that, for equal mass ratio (1:1) of Cs and Alg, the addition of Alg solution to Cs solution led to lower particle size, while higher particle size was observed with reverse order of addition. However, no significant effect of polymer mass ratios was observed on particle size and NPs stability, while EE varied with VC concentration. Artificial neural network (ANN) was applied to train the experimental parameters Cs: Alg ratios (X1:X2) and VC content (X3) for the output variables. Moreover, process optimization was carried out using multi‐objective genetic algorithm (MOGA) with the goal of lowering particle size and increasing EE while increasing nanoparticle yield.ConclusionThe PEC method effectively encapsulated VC as obtained from higher EE. The Cs: Alg ratio of 4:2.1 and 18% VC content (w/w of Cs) was found optimum for nanoencapsulation. The final ANN‐GA model showed an acceptable agreement with experimental data.

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“…Likewise, in [13], a predictive model of supervised machine learning was presented based on artificial neural networks (ANN), in which the authors evaluated the optimal operating conditions, achieving a distillate flow of 4.9 kg h −1 , with 50.6% wt of enriched ethanol and a recovery of 84.9%; however, they presented a layer with ten neurons to achieve this objective, and this requires a certain computational capacity, generating slow response times. Great advances have been made with neural networks; one of the works illistrtating this is [14], in which the authors implemented an ANN to improve the adjustment of nominal startup parameters.…”

Section: Introductionmentioning

confidence: 99%

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Ramos-Martinez,

Torres-Cantero,

Ortiz-Torres

et al. 2023

Mathematics

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This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.

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Machine learning modeling and genetic algorithm-based optimization of a novel pilot-scale thermosyphon-assisted falling film distillation unit

Cited by 18 publications

References 52 publications

Quantization and application of low-rank tensor decomposition based on the deep learning model.

Quantization and application of low-rank tensor decomposition based on the deep learning model.

Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

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