Soft Sensor of Biomass in Fermentation Process Based on Robust Neural Network

Yang, Qiangda; Yan, Fusheng

doi:10.1007/978-3-642-31968-6_33

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In [62], studied the development procedure of PSO-NN soft-sensing model. In [63], the authors proposed the robust NN based soft-sensing modeling method for the biomass concentration in the process of fermentation. K-nearest neighbors (KNN) algorithm used for the calculation of the anomaly degree of each modeling data set and the weight of each modeling data sets are decided by the computed degrees of the anomaly.…”

Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

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Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

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Soft-Sensing Estimation of Optical Density for PHA Production Using Multilayer Perceptron Neural Network

Mamat

Noor

Soh

et al. 2020

J. Phys.: Conf. Ser.

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Biomass concentration is an important indicator of production rate in polyhydroxyalkanoates (PHA) fermentation process. In current practice, measurement of biomass concentration is done off-line by laboratory analysis that is unsuitable for online process monitoring and control. Soft-sensor is often used as an alternative that provides an estimate of hard to measure parameters from easy to measure process data. However, most of these studies use simulated data or data generated from mathematical model that was developed without full consideration of process and measurement uncertainty. In this study, a soft-sensor is developed from real production data for PHA fermentation in pilot-scale bioreactor with the appropriate data pre-processing techniques applied to process data that was obtained from this system. Multilayer perceptron (MLP) neural network is used to estimate biomass concentration using secondary process parameters such as dissolved oxygen (DO), temperature, pH and agitation speed as inputs. Different models are developed based on different batches of production data and various network architecture in order to study the appropriate integration of process data and network topology that gives the best model accuracy. Results indicate that the biomass soft-sensor developed using MLP-ANN provides a better estimate of biomass in comparison to radial basis function (RBF) neural network and support vector regression (SVR) methods. The developed soft-sensor can be further used in monitoring and control of production output.

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Soft Sensor of Biomass in Fermentation Process Based on Robust Neural Network

Cited by 2 publications

References 10 publications

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Soft-Sensing Estimation of Optical Density for PHA Production Using Multilayer Perceptron Neural Network

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