Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Analysis01:11

Regression Analysis

6.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

110
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...
110
Variation01:19

Variation

7.4K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.4K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.9K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

123
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
123

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interfacial-Electronegativity-Induced Near-Surface Tetrahedral Reconstruction Enables One-Step Upcycling of Spent LiFePO<sub>4</sub> for High-Rate and Long-Life Pouch Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Comparison of subxiphoid and lateral intercostal approaches for video-assisted thoracoscopic extended thymectomy: a retrospective cohort study.

Frontiers in surgery·2026
Same author

Physics-inspired perspective on synergistic optimization: a deep receding-horizon optimization strategy for denitrification and ammonia slip suppression in waste incineration.

Bioresource technology·2026
Same author

Multidimensional Heteromorphic Bi<sub>2</sub>WO<sub>6</sub> Anchored With Au-Bi Bimetallic Nanodots Toward Photocatalytic Acetaldehyde Degradation.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Synthesis of alumina ceramic meta-fibers with tensile super-plasticity.

Nature communications·2026
Same author

Fly ash yield prediction-enabled optimization of municipal solid waste incineration: Reducing fly ash generation, disposal costs, and carbon emissions.

Waste management (New York, N.Y.)·2026

Related Experiment Video

Updated: Oct 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

548

Generalized prediction and optimal operating parameters of PCDD/F emissions by explainable Bayesian support vector

Shijian Xiong1, Yaqi Peng1, Shengyong Lu1

  • 1State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, PR China.

Waste Management (New York, N.Y.)
|October 7, 2021
PubMed
Summary

A new explainable Bayesian support vector regression (E-BSVR) model accurately predicts and minimizes polychlorinated dibenzo-p-dioxins and furans (PCDD/F) emissions from incinerators. This method offers reliable guidance for optimizing incinerator operations to reduce harmful emissions.

Keywords:
Bayesian algorithmModel interpretationOptimal operating parametersPCDD/FSupport vector regression

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K

Related Experiment Videos

Last Updated: Oct 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

548
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K

Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Machine Learning Applications

Background:

  • Existing models for predicting polychlorinated dibenzo-p-dioxins and furans (PCDD/F) emissions from incineration exhibit high deviation and lack generalizability.
  • Current models fail to provide quantitative operational guidance for full-scale municipal solid waste incinerators, hindering effective emission control.

Purpose of the Study:

  • To establish an explainable Bayesian support vector regression (E-BSVR) model for generalized prediction of PCDD/F emissions.
  • To develop a method for maximizing the reduction of PCDD/F emissions through optimized incinerator operation.
  • To provide accurate and interpretable insights into factors influencing PCDD/F formation and reduction.

Main Methods:

  • Collected 42 PCDD/F samples and measured input features including 1,2,4-trichlorobenzene (1,2,4-TrCBz), CO, SO2, oxynitride, particulate matter, fluoride, and HCl over a year.
  • Applied Box-Cox transformation normalization and hyperparameter tuning to the Bayesian Support Vector Regression (BSVR) model.
  • Utilized Local Interpretable Model-agnostic Explanations (LIME) and partial dependence plots to identify optimal operating parameters.

Main Results:

  • The E-BSVR model achieved high accuracy with R-Squared = 0.983 and RMSE = 0.044, demonstrating superior performance compared to other regression models.
  • Excellent generalization capability was confirmed on a dataset with high PCDD/F emissions (R-Squared = 0.992).
  • Key findings indicate that reducing organic chlorine content in waste and inhibiting the deacon reaction are crucial for minimizing PCDD/F emissions. Optimal parameters include 1,2,4-TrCBz < 0.098 ug/m³ and fluoride > 0.452 mg/m³.

Conclusions:

  • The developed E-BSVR method provides a reliable and accurate approach for predicting and reducing PCDD/F emissions from incinerators.
  • The model offers valuable, interpretable guidance for optimizing incinerator operations to achieve maximum emission reduction.
  • This approach enhances the environmental management of waste incineration processes.