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   Articles with "soft sensor" as a keyword



Soft‐sensors application for automated feeding control in high‐throughput mammalian cell cultures

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recommendations! Published in 2022 at "Biotechnology and Bioengineering"

DOI: 10.1002/bit.28032

Abstract: The ever‐increasing demand for biopharmaceuticals has created the need for improving the overall productivity of culture processes. One such operational concept that is considered is fed‐batch operations as opposed to batch operations. However, optimal fed‐batch… read more here.

Keywords: culture; cell; high throughput; soft sensor ... See more keywords

A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica

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recommendations! Published in 2022 at "Biotechnology and Bioengineering"

DOI: 10.1002/bit.28310

Abstract: Microbial oils produced by Yarrowia lipolytica offer an environmentally friendly and sustainable alternative to petroleum as well as traditional lipids from animals and plants. The accurate measurement of fermentation parameters, including the substrate concentration, dry… read more here.

Keywords: neural network; concentration; soft sensor; fermentation ... See more keywords

A frequency‐localized recursive partial least squares ensemble for soft sensing

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recommendations! Published in 2018 at "Journal of Chemometrics"

DOI: 10.1002/cem.2999

Abstract: We report the use of a frequency‐localized adaptive soft sensor ensemble using the wavelet coefficients of the responses from the physical sensors. The proposed method is based on building recursive, partial least squares soft sensor… read more here.

Keywords: soft sensor; least squares; recursive partial; wavelet ... See more keywords

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

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recommendations! Published in 2024 at "Journal of Chemometrics"

DOI: 10.1002/cem.3554

Abstract: The just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling.… read more here.

Keywords: time learning; least squares; random mapping; partial least ... See more keywords
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Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

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recommendations! Published in 2020 at "Engineering in Life Sciences"

DOI: 10.1002/elsc.201900076

Abstract: In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design… read more here.

Keywords: soft sensor; real time; biomass; process data ... See more keywords

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

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recommendations! Published in 2022 at "Engineering in Life Sciences"

DOI: 10.1002/elsc.202100091

Abstract: The accuracy and precision of soft sensors depend strongly on the reliability of underlying model inputs. These inputs (particularly readings of hardware sensors) are frequently subject to faults. This study aims to develop an adaptive… read more here.

Keywords: adaptive soft; ensemble based; biomass; soft sensor ... See more keywords

Sensor-fault tolerance in a wastewater treatment plant by means of ANFIS-based soft sensor and control reconfiguration

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recommendations! Published in 2017 at "Neural Computing and Applications"

DOI: 10.1007/s00521-017-2901-3

Abstract: Abstract This work presents a sensor-fault-tolerant design applied to a decentralized dissolved oxygen control in an activated sludge process subject to sensor faults such as bias and slow drifts. The core idea is to use… read more here.

Keywords: fault; soft sensor; sensor fault; control ... See more keywords

The soft sensor of the molten steel temperature using the modified maximum entropy based pruned bootstrap feature subsets ensemble method

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recommendations! Published in 2018 at "Chemical Engineering Science"

DOI: 10.1016/j.ces.2018.05.037

Abstract: Abstract The molten steel temperature in ladle furnace is a significant variable, but it is hard to be measured by real-time detection, which has some bad effects on productions. Soft sensors are alternative and effective… read more here.

Keywords: molten steel; soft sensor; temperature; entropy ... See more keywords
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An adaptive soft sensor method of D-vine copula quantile regression for complex chemical processes

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recommendations! Published in 2021 at "Chemical Engineering Science"

DOI: 10.1016/j.ces.2020.116210

Abstract: Abstract Non-linear and non-Gaussian properties are challenging topics in the soft sensor modeling of chemical processes, and fluctuations in the environmental conditions of chemical plants will also affect the accuracy of soft sensor models. This… read more here.

Keywords: soft sensor; adaptive soft; sensor method; chemical ... See more keywords

Enhanced just-in-time soft sensor calibration method using data density estimation

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recommendations! Published in 2017 at "Chemometrics and Intelligent Laboratory Systems"

DOI: 10.1016/j.chemolab.2016.12.015

Abstract: Abstract Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables. In practical application scenarios, however, the target feedback cycle is usually larger than that of process variables… read more here.

Keywords: calibration method; method; soft sensor; data density ... See more keywords

Highly-overlapped, recursive partial least squares soft sensor with state partitioning via local variable selection

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recommendations! Published in 2018 at "Chemometrics and Intelligent Laboratory Systems"

DOI: 10.1016/j.chemolab.2018.02.006

Abstract: Abstract We report the use of a soft sensor ensemble based on recursive partial least squares with a large number of overlapping models. The proposed method uses process memory attenuation in the ensemble by varying… read more here.

Keywords: state partitioning; sensor; soft sensor; variable selection ... See more keywords