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

Kang et al., 2017 - Google Patents

Fast and reliable computational strategy for developing a rigorous model-driven soft sensor of dynamic molecular weight distribution

Kang et al., 2017

Document ID
12730309206592198803
Author
Kang J
Shao Z
Chen X
Gu X
Feng L
Publication year
Publication venue
Journal of Process Control

External Links

Snippet

Key polymer properties are substantially directly related to the polymer molecular weight distribution (MWD). On-line monitoring and prediction of dynamic MWD profiles are highly important for on-line quality control of polymerization processes. In this study, a fast and …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
    • G06F19/702—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for analysis and planning of chemical reactions and syntheses, e.g. synthesis design, reaction prediction, mechanism elucidation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/41—Molding

Similar Documents

Publication Publication Date Title
Kang et al. Fast and reliable computational strategy for developing a rigorous model-driven soft sensor of dynamic molecular weight distribution
Kiparissides Challenges in particulate polymerization reactor modeling and optimization: A population balance perspective
Ng et al. Hybrid neural network—prior knowledge model in temperature control of a semi-batch polymerization process
Zavala et al. Interior-point decomposition approaches for parallel solution of large-scale nonlinear parameter estimation problems
Zavala et al. Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control
Shi et al. Optimization of grade transitions in polyethylene solution polymerization process under uncertainty
Koller et al. A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty
Kaneko et al. Novel soft sensor method for detecting completion of transition in industrial polymer processes
Shenoy et al. Practical issues in state estimation using particle filters: Case studies with polymer reactors
Zhu et al. A novel nonlinear functional expansion based PLS (FEPLS) and its soft sensor application
Zavala et al. Large-scale parameter estimation in low-density polyethylene tubular reactors
Biegler Nonlinear programming strategies for dynamic chemical process optimization
Nikolić et al. Nonlinear frequency response analysis of forced periodic operation of non-isothermal CSTR using single input modulations. Part I: Modulation of inlet concentration or flow-rate
Sharma et al. 110th anniversary: an effective methodology for kinetic parameter estimation for modeling commercial polyolefin processes from plant data using efficient simulation software tools
Ekpo et al. Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate
Cheng et al. Optimal online soft sensor for product quality monitoring in propylene polymerization process
Wu et al. Gray-box modeling and control of polymer molecular weight distribution using orthogonal polynomial neural networks
Zubov et al. Bringing the on‐line control and optimization of semibatch emulsion copolymerization to the pilot plant
Lazzari et al. Toward a digital polymer reaction engineering
Alhajeri et al. On generalization error of neural network models and its application to predictive control of nonlinear processes
Florit et al. From semi-batch to continuous tubular reactors: A kinetics-free approach
Joy et al. Model-based control of continuous emulsion co-polymerization in a lab-scale tubular reactor
Luo et al. Iterative improvement of parameter estimation for model migration by means of sequential experiments
sheng Wang et al. D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process
Salas et al. A geometric observer design for a semi-batch free-radical polymerization system