Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco G.; Vega, Jorge Rubén; Ortiz, Oscar A.; Scaglia, Gustavo

doi:10.1155/2017/9391879

Cited by 9 publications

(7 citation statements)

References 54 publications

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“…In process control, ANN is typically adapted with other control techniques. Furthermore, it has also been widely used for process modeling [75][76][77][78] or soft-sensor development [55,79,80]. In other areas, ANN has a significant development in pattern recognition [81] and classification [82,83].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Fermentation Process Control and Optimization

et al. 2022

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Process control and optimization is important in ensuring a process in sustaining profitable income while maintaining the required quality. The fermentation process has attracted attention from food processing, pharmaceutical energy, and waste treatment industries due to its lower impact on the environment and lower operating cost than the conventional chemical process. However, its control and monitoring are relatively complex as living cell activity is not easily comprehended and highly nonlinear. Various control techniques applied in the fermentation process are reviewed, including a summary of the control objective, manipulated and controlled variables chosen, and their strengths and limitations in determining the optimal fermentation process.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Fermentation Process Control and Optimization

et al. 2022

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“…Different variants of neural network are useful in different cases. Use of radial basis function neural network for fed batch bioreactor gave satisfactory performance in case of time varying parameters, uncertain non-linear disturbances and unmodeled dynamics [76]. Researchers have demonstrated the prediction of fungal biomass through Multiphase Artificial Neural Network (MANN) model during the lag, log, and stationary growth phase.…”

Section: Neural Network-based Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

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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.

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“…To address these issues, Rómoli et al. [ 17 ] proposed an online state estimator based on a Radial Basis Function (RBF) neural network that feeds information to a controller, which was derived via a linear algebra-based design strategy.…”

Section: Introductionmentioning

confidence: 99%

“…The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties are important challenges for the control of such processes. To address these issues, R omoli et al [17] proposed an online state estimator based on a Radial Basis Function (RBF) neural network that feeds information to a controller, which was derived via a linear algebra-based design strategy.…”

Section: Introductionmentioning

confidence: 99%

Application of a GA-Optimized NNARX controller to nonlinear chemical and biochemical processes

Medi

Asadbeigi

2021

Heliyon

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Chemical and biochemical processes generally suffer from extreme nonlinearities with respect to internal states, manipulated variables, and also disturbances. These processes have always received special technical and scientific attention due to their importance as the means of large-scale production of chemicals, pharmaceuticals, and biologically active agents. In this work, a general-purpose genetic algorithm (GA)-optimized neural network (NNARX) controller is introduced, which offers a very simple but efficient design. First, the proof of the controller stability is presented, which indicates that the controller is bounded-input bounded-output (BIBO) stable under simple conditions. Then the controller was tested for setpoint tracking, handling modeling error, and disturbance rejection on two nonlinear processes that is, a continuous fermentation and a continuous pH neutralization process. Compared to a conventional proportional-integral (PI) controller, the results indicated better performance of the controller for setpoint tracking and acceptable action for disturbance rejection. Hence, the GA-optimized NNARX controller can be implemented for a variety of nonlinear multi-input multi-output (MIMO) systems with minimal a-priori information of the process and the controller structure.

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Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Cited by 9 publications

References 54 publications

Fermentation Process Control and Optimization

Fermentation Process Control and Optimization

Bioprocess Control: Current Progress and Future Perspectives

Application of a GA-Optimized NNARX controller to nonlinear chemical and biochemical processes

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