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

Zhu et al., 2024 - Google Patents

Domain compensation-assisted quality inference enhancement of chemical processes with distributed outputs

Zhu et al., 2024

Document ID
13385255690764245887
Author
Zhu J
Dai Y
Guo W
Deng H
Liu Y
Publication year
Publication venue
Industrial & Engineering Chemistry Research

External Links

Snippet

The prediction performance of data-driven soft sensors for chemical processes with distributed outputs tends to degrade when distribution discrepancies exist. To meet this challenge, an offset compensation Gaussian process regression model is proposed for the …
Continue reading at pubs.acs.org (other versions)

Classifications

    • 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
    • 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
    • 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
    • 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
    • G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
    • 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
    • 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
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce

Similar Documents

Publication Publication Date Title
Saito et al. Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation
Ghosh et al. Hybrid modeling approach integrating first-principles models with subspace identification
Liu et al. Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes
Srinivasan et al. Control loop performance assessment. 2. Hammerstein model approach for stiction diagnosis
Kaneko et al. Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship
Choi et al. Fault detection based on a maximum-likelihood principal component analysis (PCA) mixture
Han et al. Melt index modeling with support vector machines, partial least squares, and artificial neural networks
Fan et al. Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development
Liu et al. Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor
Sorourifar et al. Physics-enhanced neural ordinary differential equations: Application to industrial chemical reaction systems
Zheng et al. Nonlinear dynamic soft sensor development with a supervised hybrid CNN-LSTM network for industrial processes
Chu et al. Parameter set selection via clustering of parameters into pairwise indistinguishable groups of parameters
Luo et al. Batch process monitoring with tensor global–local structure analysis
Liu et al. Active selection of informative data for sequential quality enhancement of soft sensor models with latent variables
Zhu et al. Domain compensation-assisted quality inference enhancement of chemical processes with distributed outputs
Patel et al. Integrating data-driven modeling with first-principles knowledge
Minh et al. Global sensitivity analysis and uncertainty quantification of crude distillation unit using surrogate model based on Gaussian process regression
Luo et al. Fuzzy phase partition and hybrid modeling based quality prediction and process monitoring methods for multiphase batch processes
Jiang et al. Locally weighted canonical correlation analysis for nonlinear process monitoring
Feng et al. Building quantitative structure–activity relationship models using Bayesian additive regression trees
Wu et al. Novel quality-relevant process monitoring based on dynamic locally linear embedding concurrent canonical correlation analysis
Zhu et al. Dynamic data reconciliation for improving the prediction performance of the data-driven model on distributed product outputs
Alighardashi et al. Expectation maximization approach for simultaneous gross error detection and data reconciliation using Gaussian mixture distribution
Audus et al. Leveraging theory for enhanced machine learning
López-Flores et al. Process systems engineering tools for optimization of trained machine learning models: Comparative and perspective