D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process

Wang, Jie Sheng; Guo, Qiu

doi:10.1016/j.asoc.2012.12.018

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…The intention is to illuminate general differences; in a real production case the mapping of needs must be more detailed and specific to be useful . When the analytical needs listed in Table are provided with target values a much more precise map of the manufacturer needs can be stated . This allows ranking the importance of analytical needs, which would facilitate design of the analytical system .…”

Section: Mapping Manufacturer Needs Of the Process Analytical Systemmentioning

confidence: 99%

“…22 When the analytical needs listed in Table 4 are provided with target values a much more precise map of the manufacturer needs can be stated. 16 This allows ranking the importance of analytical needs, which would facilitate design of the analytical system. 38 With support of the mapping the boundaries for the design of the analytical system are set.…”

Section: Hardware Signals Soft Sensor Model Action Examples Of Estimatesmentioning

confidence: 99%

“…By that, soft sensors become useful for monitoring and control applications in the process industry but may also, due to the generality of its principle, be used in any continuous process or transformation where sensor signals are available for modelling. In the more than 1000 soft sensor reports appearing in the engineering literature applications to commodity chemicals and metallurgical processes, unit operation control, energy and environment dominate, while examples of use in biotechnology are relatively few.…”

Section: Introductionmentioning

confidence: 99%

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Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

Mandenius

Gustavsson

2014

J of Chemical Tech & Biotech

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This mini‐review discusses how soft sensors can contribute to accomplish FDA's process analytical technology (PAT) ambitions in the manufacture of bio‐therapeutics. Focus is on applications with protein‐based drugs (proteins, antibodies), but also gene therapy vectors as well as cell cultures are considered where chemical and bio‐analytical as well as mathematical and statistical methods are used as tools. An overview of existing soft sensor alternatives and how these can be configured to meet typical industrial needs is provided. It is noted how several of these needs coincide with the PAT regulatory incentives but do also address process economic aspects of bio‐therapeutic manufacture. Evaluation of soft sensor alternatives is highlighted in relation to the production targets, quality attributes and the specification of these as well as shortcomings and needs for further improvements. © 2014 Society of Chemical Industry

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Section: Mapping Manufacturer Needs Of the Process Analytical Systemmentioning

confidence: 99%

Section: Hardware Signals Soft Sensor Model Action Examples Of Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

Mandenius

Gustavsson

2014

J of Chemical Tech & Biotech

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“…Dynamic fuzzy neural network (DFNN) is a hybrid model of fuzzy theory and neural network method, whose function is equivalent to the TSK fuzzy system [6][7][8][9][10]. Based on the problem complexity and precision demand, D-FNN model can be constructed combining the system prior knowledge.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…The error criterion can be described as follows. For the ith observation point ( , ), where is the input vector and is the desired output, compute the overall D-FNN output according to (7). Define the error as follows:…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Gao

Yang

Wang

2014

Mathematical Problems in Engineering

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PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

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Soft Sensor Design for Bioreactor Monitoring and Control

Mandenius

Gustavsson

2016

Bioreactors

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D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process

Cited by 15 publications

References 17 publications

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Soft Sensor Design for Bioreactor Monitoring and Control

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