Soft-sensor for on-line estimation of ethanol concentrations in wine stills

Osorio, Daniel; Agosín, Eduardo; Cabrera, M. L.

doi:10.1016/j.jfoodeng.2008.01.011

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…In comparison, poly(Nisopropylacrylamide) hydrogels deswell in a range of 0 to 20 vol%. Almost no change in the swelling degree was observed from 20 to 40 vol%, before the hydrogels swell again within a range of 40 to 100 vol% (Richter, 2002). This so-called co-nonsolvency effect (Liu et al, 2015;Winnek et al, 1992) was described for other hydrogel systems like poly(acryloyl-L-proline methyl ester) gels (Hiroki et al, 2001).…”

Section: Sensitivity Of Hydrogel Swelling In Different Alcoholsmentioning

confidence: 87%

Hydrogel-based piezoresistive sensor for the detection of ethanol

Erfkamp

Guenther

Gerlach

2018

J. Sens. Sens. Syst.

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Abstract. This article describes a low-cost sensor for the detection of ethanol in alcoholic beverages, which combines alcohol-sensitive hydrogels based on acrylamide and bisacrylamide and piezoresistive sensors. For reproducible measurements, the reversible swelling and deswelling of the hydrogel were shown via microscopy. The response time of the sensor depends on the swelling kinetics of the hydrogel. The selectivity of the hydrogel was tested in different alcohols. In order to understand the influence of monomer and crosslinker content on the swelling degree and on the sensitivity of the hydrogels, gels with variable concentrations of acrylamide and bisacrylamide were synthesized and characterized in different aqueous solutions with alcohol contents. The first measurements of such hydrogel-based piezoresistive ethanol sensors demonstrated a high sensitivity and a short response time over several measuring cycles.

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Section: Sensitivity Of Hydrogel Swelling In Different Alcoholsmentioning

confidence: 87%

Hydrogel-based piezoresistive sensor for the detection of ethanol

Erfkamp

Guenther

Gerlach

2018

J. Sens. Sens. Syst.

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“…One can obtain the dynamic value of DF λ i , i = 1, 2, • • • , T based on different transition times T by Equations (10) and (11), realize the dynamic calculation of the DF λ value, and obtain more accurate data fusion weights.…”

Section: Amwpdd Sample Data Processingmentioning

confidence: 99%

“…Related modeling methods are generally divided into four types: multipoint input modeling [5][6][7], dynamic weighting modeling [8,9], feedback network modeling [10,11], and multimodel structure modeling [12][13][14]. Among these types of methods, multipoint input modeling boasts the advantages of simplicity, ease in implementation, and full reflection of the process characteristics.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dai

2019

Symmetry

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To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.

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“…The associative property of artificial neural networks (ANN) and their inherent ability to learn and recognize highly non-linear and complex relationships finds them applications in engineering [13]. Given sufficient neurons in the hidden layer and a large set of input-output data to learn from, ANNs can approximate any continuous function arbitrarily well.…”

Section: Ann Based Soft Sensormentioning

confidence: 99%

Generalized Regression Neural Network Based on Soft Sensor for Multicomponent Distillation Column

Patil¹,

Ghate²

2015

IJCCE

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Abstract:Reliable and accurate measurement of product compositions is one of the main difficulties in distillation column control. In this paper a soft sensor based on generalized regression neural network (GRNN) is proposed to estimate the product composition of a multicomponent distillation column on the basis of simulated time series data. The results are compared with artificial neural network (ANN) based soft sensor. From the detailed dynamic simulation results, it is found that the proposed GRNN based estimator works better than ANN based soft sensor. The performance of estimator is evaluated in the presence of noise in the input.

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Soft-sensor for on-line estimation of ethanol concentrations in wine stills

Cited by 28 publications

References 17 publications

Hydrogel-based piezoresistive sensor for the detection of ethanol

Hydrogel-based piezoresistive sensor for the detection of ethanol

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Generalized Regression Neural Network Based on Soft Sensor for Multicomponent Distillation Column

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