A Supervisory Control and Data Acquisition System to Mitigate Fugitive Methane Emission in Landfills

Abstract

Landfills are 3rd largest sources of anthropogenic methane (CH4) emissions. Currently, as landfill gas (LFG) CH4 measurements are inconsistent and untimely, inadvertent fugitive emissions go undetected; problems are realized late. So, there is an inherent need to monitor LFG CH4 continuously via “Smart” systems. The goal is to design and develop a Supervisory Control and Data Acquisition (SCADA) system for real-time CH4 detection, prediction, and remote mitigation. System includes (i) Fugitive Emissions Mitigator (FEM) with programmable WiFi microcontroller connected to gas, and environmental sensors; (ii) continuous wireless data transmission to interactive cloud through unified codes; (iii) descriptive and diagnostic analytics in cloud dashboard to inform historical events, (iv) predictive and prescriptive analytics via Machine Learning (ML) algorithms to forecast CH4 emissions, and (v) long-distance LFG mitigation. To test SCADA system, two aspects, which influenced the magnitude of fugitive emissions in the real world were studied in lab, namely, CH4 Transport in Soil, and CH4 Generation conditions in waste. Per results, CH4 transport rate was inversely proportional to soil moisture. However, CH4 generation was directly proportional to moisture content in wastes. To further explain the complex CH4-to-moisture relationship, a 5th-order Polynomial ML equation with 86% accuracy and greatest curve-fit was derived. Finally, LFG mitigation was achieved via a separate component, which allowed for remote pump activation to extract CH4. Overall, this cost effective IoT solution helps solve existing and emerging fugitive CH4 issues via real-time measurements, prediction, and mitigation to help US reduce 45% greenhouse gases by 2030.