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Related Concept Videos

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Reaction Mechanisms: The Steady-State Approximation01:26

Reaction Mechanisms: The Steady-State Approximation

The steady-state approximation, also referred to as the quasi-steady-state approximation to differentiate it from a true steady state, is a widely used method for simplifying calculations in complex reaction mechanisms. This approach is particularly useful when dealing with multi-step reactions that involve reverse reactions or several steps, which can significantly increase mathematical complexity and make the reactions nearly unsolvable analytically.The steady-state approximation operates on...
Reaction Mechanisms: Rate-limiting Step Approximation01:29

Reaction Mechanisms: Rate-limiting Step Approximation

The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...
Fast Reactions01:27

Fast Reactions

Fast reactions occurring in times shorter than the time needed to mix reactants pose a unique challenge for investigation. In a liquid-phase continuous-flow system, reactants A and B are swiftly pushed into the mixing chamber, where mixing occurs within 1 ms. The reaction mixture then flows through an observation tube, and one measures light absorption to determine species concentrations at various points of the tube. This method is most appropriate when relatively large volumes of reactants...
Multi-Step Reactions02:31

Multi-Step Reactions

Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...

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Related Experiment Video

Updated: Jun 16, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Random Attention Mechanism for Deep Quality Modeling and Online Prediction in Chemical Processes.

Xie Ma1, Lingjian Ye2,3, Zhiqiang Ge4

  • 1Ningbo University of Finance & Economics, Ningbo 315175, China.

ACS Omega
|June 15, 2026
PubMed
Summary

This study introduces a random attention mechanism for deep latent variable models to improve industrial quality prediction. The novel approach enhances feature extraction from latent spaces, significantly reducing prediction errors.

Related Experiment Videos

Last Updated: Jun 16, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

  • Chemical Engineering
  • Artificial Intelligence
  • Process Control

Background:

  • Deep latent variable models (DLVMs) show promise for industrial quality prediction due to their interpretable structure.
  • Efficient feature extraction from DLVM latent spaces for quality prediction remains a challenge.

Purpose of the Study:

  • To develop an effective method for extracting features from latent spaces for quality prediction.
  • To enhance the performance of deep latent variable regression models in industrial settings.

Main Methods:

  • A random attention mechanism was developed and integrated into a DLVM regression framework.
  • Latent variables were extracted from various deep model layers.
  • Random attention was applied to these variables to construct diverse submodels.
  • An information fusion strategy combined predictions from these submodels.

Main Results:

  • The proposed random attention mechanism significantly improved quality prediction accuracy.
  • Reductions in root-mean-square error (RMSE) of 18% and 31% were observed on debutanizer and CO2 absorption columns, respectively.
  • The method demonstrated superior performance compared to regular deep learning approaches.

Conclusions:

  • The random attention mechanism offers an efficient strategy for latent space feature extraction in DLVMs.
  • This approach enhances the accuracy of industrial quality prediction models.
  • The findings suggest a promising direction for improving process monitoring and control.