Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms

Area of Science:

• Environmental Science
• Chemical Engineering
• Indoor Air Quality

Background:

• Accurate identification of indoor pollution sources is crucial for maintaining healthy environments.
• Estimating source strength is fundamental for effective source identification and control strategies.

Purpose of the Study:

• To propose an optimized deconvolution method for inverse calculation of source strength.
• To evaluate and compare the performance of various deconvolution algorithms for source identification.

Main Methods:

• Defined and utilized the concept of time resolution (Tr) in deconvolution.
• Analyzed the impact of filtering algorithms (e.g., Butterworth) and positions on noise reduction.
• Evaluated nine deconvolution algorithms, including Tikhonov regularization, iterative methods, and hybrid approaches, under simulated and experimental conditions.

Main Results:

• Butterworth filtering demonstrated superior performance, with filtering position having minimal impact.
• Optimal time resolution (Tr) values were determined as 0.667% for simulations and 1.33% for experiments.
• The simultaneous algebraic reconstruction technique (SART) algorithm outperformed other methods in accuracy and stability, achieving typical relative errors below 25%.

Conclusions:

• The proposed optimization scheme enhances the accuracy and stability of indoor pollution source strength identification.
• SART algorithm is recommended for its robust performance in deconvolution inverse calculations for source identification.