Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions

Munir, Muhammad Tajammal; Li, Bing; Naqvi, Muhammad

doi:10.1016/j.fuel.2023.128548

Cited by 16 publications

(4 citation statements)

References 102 publications

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“…Addressing waste management challenges requires urgent innovation, including the development of target functions and the identification of key factors such as the relationship between bale length and energy consumption in bale presses. As studies suggest, the integration of advanced machine learning techniques can significantly enhance waste sorting processes, optimize routing efficiency, and improve predictive maintenance, thereby offering substantial benefits over the long term [33].…”

Section: Predictive Machine Learningmentioning

confidence: 99%

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies

Maus,

Zengeler,

Sänger

et al. 2024

Sensors

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In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

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Section: Predictive Machine Learningmentioning

confidence: 99%

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies

Maus,

Zengeler,

Sänger

et al. 2024

Sensors

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“…However, their energy efficiency is low, and their pollutantemission rate is high under unstable running status [7]. Technological innovation emerges as a key element in achieving this equilibrium, fostering the green and standardized management of MSW through techniques [8,9]. This will propel MSW management toward a more sustainable trajectory.…”

Section: Introductionmentioning

confidence: 99%

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Pan,

Tang,

Xia

et al. 2023

Sustainability

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The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during unstable operating conditions. Moreover, fluctuations in the physical and chemical characteristics of MSW contribute to variable combustion statuses, accelerating internal furnace wear and ash accumulation. Tackling the challenges of pollution, wear, and efficiency in the MSW incineration (MSWI) process necessitates the automatic online recognition of combustion status. This article introduces a novel online recognition method using deep forest classification (DFC) based on convolutional multi-layer feature fusion. The method entails several key steps: initial collection and analysis of flame image modeling data and construction of an offline model utilizing LeNet-5 and DFC. Here, LeNet-5 trains to extract deep features from flame images, while an adaptive selection fusion method on multi-layer features selects the most effective fused deep features. Subsequently, these fused deep features feed into DFC, constructing an offline recognition model for identifying combustion status. Finally, embedding this recognition system into an existing MSWI process data monitoring system enables online flame video recognition. Experimental results show remarkable accuracies: 93.80% and 95.08% for left and right grate furnace offline samples, respectively. When implemented in an online flame video recognition platform, it aptly meets recognition demands.

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“…In the global comparison, Poland ranks 11th in the world in terms of MSW generated, with each Pole generating about 1 kg of MSW per day (STATISTA, 2023). Municipal waste management requires coordination of multiple processes that include generation, segregation, storage, collection, transportation, processing and recovery, and disposal (Rada et al, 2013;Bui et al, 2023;Kanh et al, 2023, Munir et al, 2023. The smooth flow of these processes is the responsibility of municipalities, which aim to create a cost-effective integrated municipal waste management system (Weng and Fujiwara, 2011).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Municipal Waste Collection: a Case Study of a City in Poland

Roman,

Waśkiewicz

2023

SLW

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The main problem of waste management is the increasing amount of municipal waste, and one of the key processes generating high costs is the waste collection process. The aim of the article was to optimize the route of a garbage truck using information technology (IT) software in one of the most populated Polish cities. The article tests the study hypothesis: the use of route optimization software will reduce the route length traveled by the garbage truck of the MZO in Pruszków. The data for the study was made available with the consent of the Municipal Treatment Plant in Pruszków. The received materials included information on, among others, Global Positioning System (GPS) readings of the garbage truck, including route start and end times, route length, average speed, driving time, and time of stops, points selected by the planners to collect waste along the route, information on the amount of waste collected during the implementation of the route, technical data on the moving vehicle and characteristics of the sorting plant were received. The article proposes the optimization of the routes of collection and transportation of municipal waste using the traveling salesman problem (TSP). The minimization of route length was assumed as the optimization criterion. All calculations were made in the Routimo program dedicated to route planning and optimization. As a result of the optimization, the route length was reduced by nearly 32%, and the working time by 9%. Thus, the research hypothesis stated in the article was positively verified.

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Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions

Cited by 16 publications

References 102 publications

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Optimizing Municipal Waste Collection: a Case Study of a City in Poland

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