Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor

Murugan, Chitra; Pappa, N.

doi:10.1016/j.mimet.2019.02.002

Cited by 24 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have demonstrated the prediction of fungal biomass through Multiphase Artificial Neural Network (MANN) model during the lag, log, and stationary growth phase. The result indicates successful prediction of nonlinear features of fed-batch bioreactors via the MANN model [77]. Monitoring transient state performance using ANN has been shown to offer a better approach for controlling variables [78].…”

Section: Neural Network-based Controlmentioning

confidence: 82%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

View full text Add to dashboard Cite

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

show abstract

Section: Neural Network-based Controlmentioning

confidence: 82%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

View full text Add to dashboard Cite

show abstract

“…Moreover, the model can be integrated with dynamic soft sensors to facilitate online process operation. 69 This integration enables real-time monitoring and control of the system, offering valuable feedback for process adjustments and ensuring efficient and stable operation.…”

Section: Model Evaluationmentioning

confidence: 99%

Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development

Soanuch,

Pareek,

Piumsomboon

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Understanding the hydrodynamic behavior of irregular-shaped particles is vital for optimizing fluidized bed systems. In this study, the effect of binary and ternary biomass mixtures of silica sand in a gas–solid fluidized bed on pressure drop (ΔP/H) profiles was investigated experimentally as a function of superficial gas velocity (U g). It was observed that using a mixture of particles increases the overall complexity of the system. The experimental results were also used to develop an artificial neural network (ANN) model. The five dimensionless parameters were used to train the network with the expected output of the ΔP/H profiles. The statistical analyses were used to compare the performance of the model between one and two hidden layers. The results showed that the ANN model with only one hidden layer could accurately predict the fluidization behavior with an R 2 of 0.95.

show abstract

“…[12][13][14] With the development of artificial intelligence and computer technology, machine learning algorithms represented by artificial neural networks (ANNs) [15] and support vector regression (SVR) [16] have played an important role in the soft sensors of batch processes based on data-driven modelling methods. Murugan and Natarajan [17] proposed a multiphase-ANN-based soft sensor to predict the biomass concentration of the Trichoderma-fed batch fermentation process. Yuan et al [18] built a prediction model using a long short-term memory (LSTM) neural network for the penicillin concentration of the penicillin fermentation process.…”

Section: Introductionmentioning

confidence: 99%

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

et al. 2020

View full text Add to dashboard Cite

The online measurement of key quality variables based on soft sensors plays a critical role in ensuring the safety and stability of batch processes. Recently, the relevant vector machine (RVM) was introduced into soft sensors for batch processes. However, the RVM-based soft sensor has limitations in addressing the time-varying, high-dimensional, and dynamic data of batch processes. To address these issues, based on improved just-in-time learning and the relevant vector machine, an adaptive soft sensor, termed IJITL-RVM, is proposed in this paper. The IJITL-RVM integrates the IJITL algorithm and the RVM algorithm into a unified online modelling framework with the ability to perform adaptive updating and dynamic modelling. First, to enhance the performance of online prediction, an IJITL is designed to select modelling data based on the support vector data description (SVDD) algorithm and the kernel trick. Based on the comprehensive consideration of the strong nonlinearity and high dimensionality of process data, the IJITL can adaptively and accurately select the modelling data. Afterward, a local model is established by using the RVM for online prediction. Three applications, including a numerical simulation example, some UCI datasets, and a penicillin fermentation process, are provided to illustrate the superiority of the IJITL-RVM-based soft sensor.

show abstract

Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor

Cited by 24 publications

References 27 publications

Bioprocess Control: Current Progress and Future Perspectives

Bioprocess Control: Current Progress and Future Perspectives

Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

Contact Info

Product

Resources

About