Enhancing Eco-Friendly Air Pollutant Control in Coal-Powered Plants Using AI

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Aug 9 2023

In a paper published in the journal Environmental Pollution, researchers proposed assessing and enhancing an existing air pollutant of the coal-powered plant control setup using a reconstructed untreated flue gas dataset. An AI-driven superstructure model identifies the optimal configuration, integrating reburning, seawater desulphurization, and dry electrostatic precipitator. Monte Carlo simulation (MCS) evaluates scenarios under stringent discharge limits, revealing the favorable outcomes of the proposed sustainable system: annual cost of 44.1 × 106 USD/year, environmental quality index of 0.67, and reliability indices of 0.87.

Background

Amid the remarkable zenith of technological progress in human civilization, an exigent concern has emerged: the surging demand for energy to fuel daily human activities. The pervasive usage of coal as a primary energy source has triggered grave environmental repercussions due to the mining and combustion processes, leading to energy scarcities and ecological harm. While the global shift toward renewable energy sources strives to avert energy scarcities, an accelerated transition is imperative to counteract the escalating impact of global warming. It's evident that coal-fired power plants' emissions of air pollutants, causing extensive health risks and cross-border pollution, demand effective solutions. 

The present paper adopts an industrial-oriented approach to address these pressing challenges, proposing an eco-friendly air pollutant removal system that harmonizes economic, environmental, and reliability objectives.

Proposed methodology

The research framework aims to optimize air pollutant removal configurations across four key categories: treated and untreated flue gases, superstructure models, and sustainable pollutant control. Real-time air pollutant data from three source points of the coal-fired plants were collected, comprising 17,520 data points per pollutant over a year. The dataset was restored into untreated flue gas form using imputation, filtering, and Monte Carlo simulation. Additionally, system deterioration was identified through Fourier extrapolation. Anomaly detection and data augmentation techniques were employed to assess the configuration's performance under various conditions.A superstructure optimization model was developed based on essential APR data, leading to a P-graph model representing various pollutant control configurations. These configurations underwent evaluation for economic, environmental, and reliability targets, identifying the most sustainable approach via the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The performance of the chosen configuration was then benchmarked against augmented data, with scenario evaluations carried out to accommodate tightened environmental regulations. The research culminated in proposing a sustainable pollutant control setup through a rigorous assessment process. The model aims to enhance air pollutant control's economic, environmental, and reliability dimensions. 

The reliability index for each air pollutant removal technology was determined through MCS, and a variational autoencoder (VAE) was employed to enhance untreated flue gas data by generating feasible outputs from probability distributions. Utilizing assessment criteria, TOPSIS guides multi-criteria decision-making for identifying the most sustainable solution among proposed options. Equal weight is assigned to all criteria, ensuring fair consideration of their importance.

Experimental results and analysis

Air pollutant data collected from sensors installed at three chimney discharge points for a year were initially controlled by the default APR setup (Selective Catalytic Reduction (SCR) for nitrogen oxide (NO_x), Flue Gas Desulfurization (FGD) for sulfur dioxide (SO_x), and dry-type electrostatic precipitator for Particulate Matter (PM)). Adjusting for seasonal changes linked to plant operations, these data were transformed back to untreated states using the previously mentioned method.

Data decomposition was performed on controlled pollutant data, involving normalization, signal reconstruction, Fast Fourier Transform (FFT), residual analysis, and linear trend extraction. The anomaly detection approach utilized Principal Component Analysis (PCA), Hotelling T2, and clustering. A pool of air pollution control setups was proposed by a P-graph model driven by a mathematical optimization model. The reliability of these configurations was assessed through 10,000 MCS iterations.

With mounting environmental concerns, scenarios were examined where tightened regulations modified original discharge limits (135 ppm NO_x, 169 ppm SO_x, and 52 mg/Nm³ PM), varying from 90% to 10% stringency. The evaluation of the framework, aiming to enhance air pollutant removal system sustainability, was carried out through Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. This assessment highlights the aspects of the framework in terms of strengths (comprehensive technology overview, sustainable route emphasis), weaknesses (neglect of potential drawbacks), opportunities (guideline potential for research and policy), and threats (potential deterrence from alternative energy investments).

Conclusion

This groundbreaking study presents a remarkable upgrade to an existing air pollutant removal setup, guided by a telemonitoring system collecting NO_x, SO_x, and PM data from the case study. The transformed untreated flue gas, meticulous degradation evaluation, and data anomaly analysis are executed precisely. Augmented untreated pollutants are assessed with the optimal control configuration (reburning, seawater desulphurization, and dry electrostatic precipitator), showing an annual cost of 44.1 × 106 USD/year, an environmental quality index of 0.67, and reliability indices of 0.87.

This research offers an avenue for existing heavily polluting plants, like coal-fired power plants, to upgrade their air pollutant control system, adapting to various scenarios. While this framework is a significant stride toward sustainability, further investigation, including kinetic modeling, retirement planning, and environmental impact assessment, is recommended for comprehensive validation.