Online predicting PCDD/F emission by formation pathway identification clustering and Box-Cox Transformation

Area of Science:

• Environmental Science
• Chemical Engineering
• Analytical Chemistry

Background:

• Municipal solid waste incineration (MSWI) operations involve changing fuel compositions and conditions, complicating real-time control of toxic emissions.
• Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) are highly toxic byproducts, and their International Toxic Equivalent Quantity (I-TEQ) requires effective monitoring and control.

Purpose of the Study:

• To develop a rapid feedback method for controlling PCDD/F emissions during long-term MSWI operations.
• To improve the accuracy and precision of PCDD/F emission predictions by addressing variations in operational conditions.

Main Methods:

• Formation Pathway Identification Clustering (FPIC) was employed to categorize PCDD/F formation routes (de novo, chlorobenzene, chlorophenol, DD/DF chlorination).
• Box-Cox Transformation (BCT) was utilized to normalize emission data (I-TEQ) and 1,2,4-trichlorobenzene measurements for effective model construction.
• Thermal desorption gas chromatography coupled to tunable-laser ionization time-of-flight mass spectrometry (TD-GC-TLI-TOFMS) was used for 1,2,4-trichlorobenzene analysis.

Main Results:

• FPIC effectively divided PCDD/F data into two clusters based on dominant formation pathways (CP-route vs. de novo/CBz-route).
• The developed method achieved high accuracy, with most errors within ±40% and absolute errors within ±0.126 I-TEQ (ng/Nm³).
• The linear model demonstrated a significant reduction in the absolute relative difference between predicted and measured I-TEQ to 20.28%.

Conclusions:

• The combined FPIC and BCT method provides an effective approach for online prediction of PCDD/F emissions in MSWI.
• This predictive capability enables rapid operational adjustments to control PCDD/F emissions, enhancing environmental safety during long-term incineration processes.