[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhang, 2009 - Google Patents

Software sensors and their applications in bioprocess

Zhang, 2009

Document ID
6347420318392962600
Author
Zhang H
Publication year
Publication venue
Computational intelligence techniques for bioprocess modelling, supervision and control

External Links

Snippet

Industrial bioprocesses present a very difficult challenge to control engineers. Problems associated with the nature of the organisms in the process and difficulties related to obtaining accurate information regarding the progression of the process make controlling …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Sharma et al. A hybrid science‐guided machine learning approach for modeling chemical processes: A review
Von Stosch et al. Hybrid semi-parametric modeling in process systems engineering: Past, present and future
Dai et al. Fault diagnosis of batch chemical processes using a dynamic time warping (DTW)-based artificial immune system
Zhou et al. Semi-supervised PLVR models for process monitoring with unequal sample sizes of process variables and quality variables
He et al. Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square
Zhang Software sensors and their applications in bioprocess
Corrigan et al. Developing accurate data-driven soft-sensors through integrating dynamic kernel slow feature analysis with neural networks
Kim et al. Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of E. coli
Boillereaux et al. A new approach for designing model-based indirect sensors
Selişteanu et al. Microbial production of enzymes: Nonlinear state and kinetic reaction rates estimation
Irani et al. Approximate predictive control of a distillation column using an evolving artificial neural network coupled with a genetic algorithm
Galvanauskas et al. Hybrid modeling of biochemical processes
Zhu et al. Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder
Golobič et al. Software sensor for biomass concentration monitoring during industrial fermentation
Tuveri Bayesian Estimators for Bioprocess Monitoring Under Uncertainty
Dewasme et al. Monitoring of bioprocesses: mechanistic and data-driven approaches
Zhang et al. A comparative study of soft-sensing methods for fed-batch fermentation processes
Guo et al. Modelling for multi-phase batch processes using steady state identification and deep recurrent neural network
Bousson et al. From a rule-based to a predictive qualitative model-based approach using automated model generation: Application to the monitoring and diagnosis of biological processes
Roux et al. Control and Monitoring of Industrial Fermentations: An Industrial Perspective and Recent Trends
Juuso Intelligent dynamic simulation of fed-batch fermentation processes
Kabbaj et al. Analytical and knowledge based approaches for a bioprocess supervision
Zhang et al. Multi-way optimal control of a benchmark fed-batch fermentation process
Juuso Intelligent dynamic simulation of a fed-batch enzyme fermentation process
Carlos-Hernandez et al. Design and real-time implementation of a TS fuzzy observer for anaerobic wastewater treatment plants