Artificial intelligence applications in solid waste management: A systematic research review

Upcycling biowaste into advanced carbon materials via low-temperature plasma hybrid system: applications, mechanisms, strategies and future prospects

This review focuses on the recent advances in the sustainable conversion of biowaste to valuable carbonaceous materials. This study summarizes the significant progress in biowaste-derived carbon materials (BCMs) via a plasma hybrid system. This includes systematic studies like AI-based multi-coupling systems, promising synthesis strategies from an economic point of view, and their potential applications towards energy, environment, and biomedicine. Plasma modified BCM has a new transition lattice phase and exhibits high resilience, while fabrication and formation mechanisms of BCMs are reviewed in plasma hybrid system. A unique 2D structure can be designed and formulated from the biowaste with fascinating physicochemical properties like high surface area, unique defect sites, and excellent conductivity. The structure of BCMs offers various activated sites for element doping and it shows satisfactory adsorption capability, and dynamic performance in the field of electrochemistry. In recent years, many studies have been reported on the biowaste conversion into valuable materials for various applications. Synthesis methods are an indispensable factor that directly affects the structure and properties of BCMs. Therefore, it is imperative to review the facile synthesis methods and the mechanisms behind the formation of BCMs derived from the low-temperature plasma hybrid system, which is the necessity to obtain BCMs having desirable structure and properties by choosing a suitable synthesis process. Advanced carbon-neutral materials could be widely synthesized as catalysts for application in environmental remediation, energy conversion and storage, and biotechnology.

A mini-review for identifying future directions in modelling heating values for sustainable waste management

Global estimations suggest energy content within municipal solid waste (MSW) is underutilized, compromising efforts to reduce fossil CO2 emissions and missing the opportunities for pursuing circular economy in energy consumption. The energy content of the MSW, represented by heating values (HVs), is a major determinant for the suitability of incinerating the waste for energy and managing waste flows. Literature reveals limitations in traditional statistical HV modelling approaches, which assume a linear and additive relationship between physiochemical properties of MSW samples and their HVs, as well as overlook the impact of non-combustible substances in MSW mixtures on energy harvest. Artificial intelligence (AI)-based models show promise but pose challenges in interpretation based on established combustion theories. From the variable selection perspectives, using MSW physical composition categories as explanatory variables neglects intra-category variations in energy contents while applying environmental or socio-economic factors emerges to address waste composition changes as society develops. The article contributes by showing to professionals and modellers that leveraging AI technology and incorporating societal and environmental factors are meaningful directions for advancing HV prediction in waste management. These approaches promise more precise evaluations of incinerating waste for energy and enhancing sustainable waste management practices.

Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran

Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Choosing the most effective machine learning algorithms is very important in accurately predicting bioavailability of PTEs. Therefore, four machine learning (ML) models including decision tree regression (DTR), random forest regression (RFR), multi-layer perceptron regression (MLPR) and support vector regression (SVR), were used and compared for estimating the selected PTEs bioavailability. For these models, 9 variables (total concentration, pH, EC, OM and five chemical forms F1 to F5 obtained by sequential extraction) in 100 sediment samples were considered. The results showed that contamination level decreases from Zn and Cu to Pb and Mo, but the order of the mobility coefficient of the elements in the sediment follows the trend of zinc > copper > molybdenum > lead, and variation coefficient indicated more variability of spatial distribution for Zn and Cu. Among the four tested models, DTR and RFR performed the best for predicting PTEs bioavailability variations (with roc_auc>0.9, R2 > 0.8 and MSE>0.5), followed by MLPR and SVR. Furthermore, the relevance of the factors controlling the metals availability, evaluated using the RFR-based feature importance method and Pearson correlation, revealed that the most important physicochemical property for Zn, Cu and Mo bioavailability was pH, whereas for Pb, EC was the determinant factor. In the case of chemical speciation, F5 had an inverse correlation with the target, while F1 and F2 had a direct correlation. These fractions contributed significantly to the prediction results. This study represents the potential successful application of ML to PTEs risk control in sediments and early warning for the surrounding water PTEs contamination.

Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues

This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pretreated harvest residue types (maize stover, sunflower stalk and soybean straw) and manure were anaerobically co-digested, measuring biogas and methane yield alongside eight thermal preprocessing and biomass covariates. These were the inputs to an ensemble machine learning approach for biogas and methane yield prediction, employing three feature selection approaches. The Support Vector Machine prediction with the Recursive Feature Elimination resulted in the highest prediction accuracy, achieving the coefficient of determination of 0.820 and 0.823 for biogas and methane yield prediction, respectively. This study demonstrated an extreme dependency of prediction accuracy to input dataset properties, which could only be mitigated with ensemble machine learning and strongly suggested that the split-sample approach, often used in previous studies, should be avoided.

Living review framework for better policy design and management of hazardous waste in Australia

The significant increase in hazardous waste generation in Australia has led to the discussion over the incorporation of artificial intelligence into the hazardous waste management system. Recent studies explored the potential applications of artificial intelligence in various processes of managing waste. However, no study has examined the use of text mining in the hazardous waste management sector for the purpose of informing policymakers. This study developed a living review framework which applied supervised text classification and text mining techniques to extract knowledge using the domain literature data between 2022 and 2023. The framework employed statistical classification models trained using iterative training and the best model XGBoost achieved an F1 score of 0.87. Using a small set of 126 manually labelled global articles, XGBoost automatically predicted the labels of 678 Australian articles with high confidence. Then, keyword extraction and unsupervised topic modelling with Latent Dirichlet Allocation (LDA) were performed. Results indicated that there were 2 main research themes in Australian literature: (1) the key waste streams and (2) the resource recovery and recycling of waste. The implication of this framework would benefit the policymakers, researchers, and hazardous waste management organisations by serving as a real time guideline of the current key waste streams and research themes in the literature which allow robust knowledge to be applied to waste management and highlight where the gap in research remains.

Prophet time series modeling of waste disposal rates in four North American cities

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

Data Science Applications in Circular Economy: Trends, Status, and Future

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

Smart-sight: Video-based waste characterization for RDF-3 production

A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition

To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several deep learning methods based on RGB images to achieve waste detection and recognition. Considering the limitations of color images, we propose an efficient multi-modal learning solution for pavement waste detection and recognition. Specifically, we construct a high-quality outdoor pavement waste dataset called OPWaste, which is more in line with real needs. Compared to other waste datasets, OPWaste dataset not only has the advantages of rich background and high diversity, but also provides color and depth images. Meanwhile, we explore six different multi-modal fusion methods and propose a novel multi-modal multi-scale network (MM-Net) for RGB-D waste detection and recognition. MM-Net introduces a novel multi-scale refinement module (MRM) and multi-scale interaction module (MIM). MRM can effectively refine critical features using attention mechanisms. MIM can gradually realize information interaction between hierarchical features. In addition, we select several representative methods and perform comparative experiments. Experimental results show that MM-Net based on the image addition fusion method outperforms other deep learning models and reaches 97.3% and 84.4% on mAP0.5 and AR metrics. In fact, multi-modal learning plays an important role in intelligent waste recycling. As a promising auxiliary tool, our solution can be applied to intelligent cleaning robots for automatic outdoor waste management.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Green loops and clean skies: Optimizing municipal solid waste management using data science for a circular economy

The interplay between Municipal Solid Waste (MSW) Management and data science unveils a panorama of opportunities and challenges, set against the backdrop of rising global waste and evolving technological landscapes. This article threads through the multifaceted aspects of incorporating data science into MSW management, unearthing key findings, novel knowledge, and instigating a call to action for stakeholders (e.g. policymakers, local authorities, waste management professionals, technology developers, and the general public) across the spectrum. Predominant challenges like the enigmatic nature of "black-box" models and tangible knowledge gaps in the sector are scrutinized, ushering in a narrative that emphasizes transparent, stakeholder-inclusive, and policy-adaptive approaches. Notably, a conscious shift towards "white-box" and "grey-box" data science models has been spotlighted as a pivotal response to transparency issues. Furthermore, the discourse highlights the necessity of crafting data science solutions that are specifically moulded to the nuanced challenges of MSW management, and it underscores the importance of recalibrating existing policies to be reflexive to technological advancements. A resolute call echoes for stakeholders to not just adapt but immerse themselves in a continuous learning trajectory, championing transparency, and fostering collaborations that hinge on innovative, data-driven methodologies. Thus, as the realms of data science and MSW management entwine, the article sheds light on the potential transformation awaiting waste management paradigms, contingent on the nurtured amalgamation of technological advances, policy alignment, and collaborative synergy.

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.

Uncovering environmental performance patterns of plastic packaging waste in high recovery rate countries: An example of EU-27

Plastic consumption and its end-of-life management pose a significant environmental footprint and are energy intensive. Waste-to-resources and prevention strategies have been promoted widely in Europe as countermeasures; however, their effectiveness remains uncertain. This study aims to uncover the environmental footprint patterns of the plastics value chain in the European Union Member States (EU-27) through exploratory data analysis with dimension reduction and grouping. Nine variables are assessed, ranging from socioeconomic and demographic to environmental impacts. Three clusters are formed according to the similarity of a range of characteristics (nine), with environmental impacts being identified as the primary influencing variable in determining the clusters. Most countries belong to Cluster 0, consisting of 17 countries in 2014 and 18 countries in 2019. They represent clusters with a relatively low global warming potential (GWP), with an average value of 2.64 t CO2eq/cap in 2014 and 4.01 t CO2eq/cap in 2019. Among all the assessed countries, Denmark showed a significant change when assessed within the traits of EU-27, categorised from Cluster 1 (high GWP) in 2014 to Cluster 0 (low GWP) in 2019. The analysis of plastic packaging waste statistics in 2019 (data released in 2022) shows that, despite an increase in the recovery rate within the EU-27, the GWP has not reduced, suggesting a rebound effect. The GWP tends to increase in correlation with the higher plastic waste amount. In contrast, other environmental impacts, like eutrophication, abiotic and acidification potential, are identified to be mitigated effectively via recovery, suppressing the adverse effects of an increase in plastic waste generation. The five-year interval data analysis identified distinct clusters within a set of patterns, categorising them based on their similarities. The categorisation and managerial insights serve as a foundation for devising a focused mitigation strategy.

A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems

Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.

Exploring the industrial dynamics of waste management and recycling: A call for research and a proposed agenda

The waste management sector is undergoing profound transformations that challenge its structures and institutions. The function and position of waste management and recycling companies have been changing, and this process accelerates as the circular economy consolidates as part of the strategy to implement green shifts. This article argues that scholars, practitioners and policymakers interested in waste management could benefit from building bridges with the field of industrial dynamics. Industrial dynamics is concerned with the driving forces of economic transformation, with focus on not just outcomes but processes and structures. This type of research is crucial in face of transformations going on in the sector. Three crucial themes for cross-disciplinary investigation are: (i) industry evolution and institutions, (ii) business organization and management, and (iii) technological change, innovation and entrepreneurship. Waste management is a lively, complex and diverse sector, whose process of reinvention present the opportunity to research profound industrial transformations in real time. By systematically investigating the industrial dynamics of waste management, it becomes possible to uncover the structural changes underpinning the transformation of waste into resources, their driving forces and the directions to which they point, while mindful of the evolving discourses and the wider technological and institutional landscape.

Modelling of demolition waste generation: Application to Greek residential buildings

The construction sector in Europe is among the biggest waste generators, producing 370 million tonnes of construction and demolition waste (CDW) every year, which contain important secondary materials. Quantification of CDW is important from their circular management and environmental impact point of view. Thus, the overall objective of this study was to develop a modelling methodology for estimating demolition waste (DW) generation. The volumes (m3) of individual construction materials contained in 45 residential buildings in Greece were accurately estimated using computer-aided design (CAD) software and the materials were classified according to European List of Waste. These materials will become waste upon demolition, with a total estimated generation rate of 1590 kg m-2 of top view area and with concrete and bricks representing 74.5% of total. Linear regression models were developed to predict the total and individual amounts of 12 different building materials based on structural building characteristics. To test the accuracy of the models, the materials of two residential buildings were quantified and classified and the results were compared with the model predictions. Depending on the model used, the % differences between models' predictions and CAD estimates for total DW averaged 11.1% ± 7.4% for the first case study and 2.5% ± 1.5% for the second. The models can be used for accurate quantification of total and individual DW and their management within the framework of circular economy.

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R² = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. The key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

Decarbonization in waste recycling industry using digitalization to promote net-zero emissions and its implications on sustainability

Digitalization and sustainability have been considered as critical elements in tackling a growing problem of solid waste in the framework of circular economy (CE). Although digitalization can enhance time-efficiency and/or cost-efficiency, their end-results do not always lead to sustainability. So far, the literatures still lack of a holistic view in understanding the development trends and key roles of digitalization in waste recycling industry to benefit stakeholders and to protect the environment. To bridge this knowledge gap, this work systematically investigates how leveraging digitalization in waste recycling industry could address these research questions: (1) What are the key problems of solid waste recycling? (2) How the trends of digitalization in waste management could benefit a CE? (3) How digitalization could strengthen waste recycling industry in a post-pandemic era? While digitalization boosts material flows in a CE, it is evident that utilizing digital solutions to strengthen waste recycling business could reinforce a resource-efficient, low-carbon, and a CE. In the Industry 4.0 era, digitalization can add 15% (about USD 15.7 trillion) to global economy by 2030. As digitalization grows, making the waste sector shift to a CE could save between 30% and 35% of municipalities' waste management budget. With digitalization, a cost reduction of 3.6% and a revenue increase of 4.1% are projected annually. This would contribute to USD 493 billion in an increasing revenue yearly in the next decade. As digitalization enables tasks to be completed shortly with less manpower, this could save USD 421 billion annually for the next decade. With respect to environmental impacts, digitalization in the waste sector could reduce global CO₂ emissions by 15% by 2030 through technological solutions. Overall, this work suggests that digitalization in the waste sector contributes net-zero emission to a digital economy, while transitioning to a sustainable world as its social impacts.

Cleaner production auditing for plastic recycling industry in Pakistan: A baseline study

Recycling plastics is a good alternative to manage the plastic waste generated in Pakistan. Unfortunately, the country lacks efficient system to manage or recycle the plastic waste it generates. Lack of government support, absence of standard operating procedures, negligence towards health and safety of workers, increasing costs of raw materials and poor quality of the recyclates are some of the issues currently faced by plastic recyclers in Pakistan. Considering the need of cleaner production audits in plastic recycling industries, this study was carried out to establish an initial reference benchmark. Production processes in 10 recycling industries were evaluated from cleaner production perspective. The study showed the average water consumption of a recycling industry as high as 3315 L/ton. All the consumed water is wasted in the nearby community sewer while, only 3 recyclers recycled between 70 and 75% the treated wastewater. In addition, a recycling facility on an average consumed 172.5 kWh of power for processing 1 ton of plastic waste. The average temperature was observed to be 36.5 °C and noise levels exceeded the permissible limits. Moreover, the industry is male-dominated, workers are mostly underpaid and have no access to good healthcare facilities. Recyclers lack standardization and have no national guidelines to follow. Guidelines and standardization of recycling process, wastewater treatment, use of renewable energy, water reuse etc, are direly needed for uplifting this sector and reducing its impacts on the environment.

A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment

The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.

A multi-model forecasting approach for solid waste generation by integrating demographic and socioeconomic factors: a case study of Prayagraj, India

Projecting municipal solid waste generation and identifying socioeconomic factors affecting waste generation is crucial for integrated waste management strategies. The present research work focuses on the projection of municipal solid waste (MSW) generation in Prayagraj, India, based on demographics and socioeconomic factors, using long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and incremental increase models (IIM). The model was integrated with nine socioeconomic variables to improve accuracy. The influence of socioeconomic variables on MSW generation was evaluated using correlation and fuzzy logic approaches. Waste generation data collected from the Central Pollution Control Board (CPCB) from 1997 to 2015 were used to train the models. The results of the correlation study indicate that population growth, employment, and households have a substantial impact on waste generation rates. Root mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R²) suggest that LSTM is the best model to forecast MSW generation in Prayagraj, India. The R² value indicates that the LSTM is more accurate (0.92) than ARIMA (0.72) and IIM (0.70). LSTM projection indicates that the city will have a population of 1.6 million by 2031, and waste generation will increase by 70.6% in 2031.

Artificial intelligence for waste management in smart cities: a review

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

The transition journey of EU vs. NON-EU countries for waste management

This study aims to identify macroeconomic indicators that can be used as predictors of waste management on the European continent. The study was conducted taking in account the intensification of urbanizations, the increase of standard of leaving that fuels to consumerism phenomenon, and imposed challenges for waste management. The research focuses on the interval from 2010 to 2020 for 37 European countries grouped according to EU15/EU28/non-EU and EU/non-EU members. As macroeconomic indicators, human development index (HDI), GDP/capita. GNI/capita, general government expenditure with environment protection, people at risk of poverty or social exclusion, population by educational attainment level, sex, and age (%)-less than primary, primary and lower secondary education (levels 0-2) were used. A multilinear regression model with collinearity diagnosis was applied to find out the direction and intensity of the contribution of independent variables and to hierarchy the predictors of waste management.. For multiple comparison between and inside of each grouping of countries, statistical inference methods were used: one-way ANOVA with Bonferroni post hoc test multiple comparisons and independent samples Kruskal-Wallis test with Dunn's post hoc test. The main conclusions of the study are that EU15 countries have the highest average values for most indicators of waste management, comparative with EU28 and with non-EU countries, followed by a group of EU28 countries. For indicators of recycling rate of packaging waste by type of packaging-metallic and recycling rate of e-waste, the non-EU countries have the highest values of mean compared with the EU15 and EU28 groups of countries. This can be explained by the high level of development of the some non-euro area countries (Iceland, Norway, Switzerland, Liechtenstein) that have intense concerns about waste recycling and have the necessary financial strength to carry out complex environmental protection programs.

Value Addition Employing Waste Bio-Materials in Environmental Remedies and Food Sector

Overall, combating food waste necessitates a multifaceted approach that includes education, infrastructure, and policy change. By working together to implement these strategies, we can help reduce the negative impacts of food waste and create a more sustainable and equitable food system. The sustained supply of nutrient-rich agrifood commodities is seriously threatened by inefficiencies caused by agricultural losses, which must be addressed. As per the statistical data given by the Food and Agriculture Organisation (FAO) of the United Nations, nearly 33.33% of the food that is produced for utilization is wasted and frittered away on a global level, which can be estimated as a loss of 1.3 billion metric tons per annum, which includes 30% cereals, 20% dairy products 35% seafood and fish, 45% fruits and vegetables, and 20% of meat. This review summarizes the various types of waste originating from various segments of the food industry, such as fruits and vegetables, dairy, marine, and brewery, also focusing on their potential for developing commercially available value-added products such as bioplastics, bio-fertilizers, food additives, antioxidants, antibiotics, biochar, organic acids, and enzymes. The paramount highlights include food waste valorization, which is a sustainable yet profitable alternative to waste management, and harnessing Machine Learning and Artificial Intelligence technology to minimize food waste. Detail of sustainability and feasibility of food waste-derived metabolic chemical compounds, along with the market outlook and recycling of food wastes, have been elucidated in this review.

An optimal model for municipal solid waste management using hybrid dual faster R-CNN

Economic development, population growth, and rapid urbanization are the reasons for an increasing generation of waste all over the world. Recently, the statistics showed that 2.1 million of tons municipal solid waste (MSW) was produced in 2016, which is estimated to enhance by 3.4 million tons in 2050. In recent times, municipal solid waste generation is dramatically increasing due to factors such as rapid urbanization, altered living standard, and increased population. These factors make the municipal solid waste management system complex and break the pollution-controlling strategies. So it necessitates the system to accurately predict the waste composition. Based on the waste classification, a suitable decomposition technique is preferred. Therefore, this paper proposed CMSOA optimized dual faster R-CNN based waste management system to accurately classify the waste composition. The proposed system is formed by hybridizing dual faster RCNN along with complex-valued encoding multi-chain seeker optimization algorithm (CMSOA). Various evaluation measures, namely accuracy, precision, recall, F-measure, RMSE, MAE, and MAPE metrics, are computed, and the case study analysis is conducted on the major five cities of Maharashtra. The comparative analysis is carried out for various approaches, and from the analysis, the results revealed that the proposed method provides better classification results than other methods.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Evaluating the collection and composition of plastic waste in the digital waste bank and the reduction of potential leakage into the ocean

Most ocean plastic pollution results from leakage from waste management activities on land, mainly in coastline communities. In this research, the digitalization of waste management will be evaluated to improve the prevention of leakage. The digitalization means introducing mobile apps into the waste bank that can improve waste management efficiency while providing reliable data. The data on waste management were gained from Griya Luhu App which has been used in 13 villages around Gianyar, while the waste generation was calculated from 97 samples. Then, the villages were categorized by their potential risk of waste leakage based on their distances from the shore. First, the growth of digital waste banks based on the number of units, the number of customers and the amount of waste-managed was analyzed. Second, the composition of waste collected was evaluated. Last, inorganic waste generation (IWG) from digital waste banks was reduced. The results showed that digital waste banks and the customers had grown rapidly in 1 year. The number of waste bank units grew from 0 to 80 with an increase to a total of 5500 customers during the same period with a maximum of 20 tons of waste managed per month. In general, digital waste banks have shown promising performance in preventing waste leakage into the ocean with a 54.04% reduction of IWG. Compared to this reduction percentage, Tulikup as a high-risk village has a considerably low reduction (30.30%) and should be prioritized. Furthermore, the ability to manage a village with a high population/number of customers should be improved.

Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant

Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m³/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner.

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.

Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis

Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA-k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA-k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m-2), 993.54 (kg·m-2) and 991.80 (kg·m-2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions.

Dynamic routing for efficient waste collection in resource constrained societies

Waste collection in developing nations faces multi-fold challenges, such as resource constraints and real-time changes in waste values, while finding the optimal routes. This paper attempts to address these challenges by modeling real-time waste values in smart bins and Collection Vehicles (CV). Further, waste value prioritized routes for coordinated CV, during various time intervals are modeled in a multi-agent environment for finding good routes. The CV, as agents, implement the formulated linear program to maximize the collected waste while minimizing the distance to the central depot. The city of Chandigarh, India, was divided into regions and the model was implemented to achieve significantly better performance in terms of waste collected in less distance and total bins covered when compared to the existing scenario. The stakeholders can use the outcomes to effectively plan the resources for better collection practices, which will have a positive impact on the environment.

Multiple-criteria decision analysis to substantiate the prospects of industrial and solid municipal wastes as slurry fuel components

This article investigates the recovery of typical wastes (coal slime, sawdust, cardboard and tire pyrolysis residue) as part of high-moisture slurry fuels. Using a laboratory furnace, the ignition and combustion characteristics of fuels as well as NO_x and SO_x emissions were determined. Using multiple-criteria decision-making (MCDM) methods and experimental results, we access the performance of four different slurry fuels in comparison with bituminous coal. The novelty of the study is based on the following features: we consider a unique set of parameters of the fuels (economic, environmental, safety and energy indicators), as well as three countries for their potential use (the USA, India and Russia); three different methods for calculating the efficiency indicator of each fuel were used. Despite rather low energy performance, the summarizing efficiency indicator of waste-based slurries was 53-93% higher than that of coal. The use of cardboard in the composition of a fuel blend showed the best complex result (the increase in the efficiency indicator was 80-93% relative to coal). The least promising additive was the pyrolysis residue of automobile tires. Its addition resulted in a 10-15% decrease in overall efficiency relative to a slurry without additives. The research results are useful for optimizing the component composition of waste-based slurries, technical and economic development of projects for the incineration of various wastes in the form of high-moisture fuel slurries.

Leverage of resource efficiency over environmental emissions: Case of a megacity in China

Material metabolism in a Chinese megacity, Shanghai, was investigated with an integrated approach. Production-based raw material input, city-wide waste output and carbon emissions were compiled for the period 1995-2020, by computing hundreds of products and by-products. Decoupling of these resource and environmental flows from economic development was assessed, and the socio-economic and technical drivers were decomposed. The research demonstrated a hypothesis that flows of primary resources, waste, and carbon emissions displayed a certain level of synchronicity in the past decades. An order effect was seen with waste indicators usually performing better than carbon indicators, and carbon indicators are better than resource indicators in terms of material/environmental intensity and decoupling. There might be a resource leverage leading to the synchronicity of environmental emissions. Improvement in resource efficiency was decomposed as the most significant driver to urban metabolism, bringing about >33 % of resource reduction, 32 % of carbon mitigation, and 30 % of waste diminution from the 2010 values. A greater extent in emission reduction than resource use was attributed to the decrease of fossil fuels share in total resource use and carbon intensity per energy consumption. Continuous increase in post-use waste flows caused a rebound of waste indicators in the recent five-year period (2016-2020) and broke up the synchronicity. This potentially foresees the shift of material metabolism from production to consumption side in major cities in China and calls for reforms of environmental policies.

AerialWaste dataset for landfill discovery in aerial and satellite images

Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts' expertise and build automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. Most positive instances are characterized by metadata: the type of waste, its storage mode, the type of the site, and the evidence and severity of the illicit. The dataset has been technically validated by building an accurate landfill detector and is accompanied by a visualization and annotation tool.

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R²) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R² 0.889, RPD 3.00), and ANN-logistic (R² 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.

Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm

Machine learning (ML) is a novel method of data analysis with potential to overcome limitations of traditional composting experiments. In this study, four ML models (multi-layer perceptron regression, support vector regression, decision tree regression, and gradient boosting regression) were integrated with genetic algorithm to predict and optimize heavy metal immobilization during composting. Gradient boosting regression performed best among the four models for predicting both heavy metal bioavailability variations and immobilization. Gradient boosting regression-based feature importance analysis revealed that the heavy metal initial bioavailability factor, total phosphorus, and composting duration were the determinant factors for heavy metal bioavailability variations (together contributing >75%). After genetic algorithm optimization, the maximum immobilization rates of Cu, Zn, Cd, As, and Cr were 79.53, 31.30, 14.91, 46.25, and 66.27%, respectively, superior to over 90% of the measured data. These findings demonstrate the potential application of ML to risk-control for heavy metals in livestock manure composting.

Recent advances in applications of artificial intelligence in solid waste management: A review

Efficient management of solid waste is essential to lessen its potential health and environmental impacts. However, the current solid waste management practices encounter several challenges. The development of effective waste management systems using advanced technologies is vital to overcome the challenges faced by the current approaches. Artificial Intelligence (AI) has emerged as a powerful tool for applications in various fields. Several studies also reported the applications of AI techniques in the management of solid waste. This article critically reviews the recent advancements in the applications of AI techniques for the management of solid waste. Various AI and hybrid techniques have been successfully employed to predict the performance of various methods used for the generation, segregation, storage, and treatment of solid waste. The key challenges that limit the applications of AI in solid waste are highlighted. These include the availability and selection of applicable data, poor reproducibility, and less evidence of applications in real solid waste. Based on identified gaps and challenges, recommendations for future work are provided. This review is beneficial for all stakeholders in the field of solid waste management, including policy-makers, governments, waste management organizations, municipalities, and researchers.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R² in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R² in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.

Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review

Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.

PortiK: A computer vision based solution for real-time automatic solid waste characterization - Application to an aluminium stream

In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable development of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to supervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demonstrated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review

Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.

Challenges and Priorities of Municipal Solid Waste Management in Cambodia

Municipal solid waste (MSW) management is one of the utmost challenges for Cambodia’s city and district centers. The unsound management of MSW has detrimentally affected the environment and human health. In the present study, an attempt has been made to provide a comprehensive insight into the generation and characteristics, policies and legislation frameworks, management arrangement, collection, treatment, and disposal of MSW. The experience of developed and developing countries and the challenges and priorities of MSW management in Cambodia are also highlighted. In Cambodia, about 4.78 million tons of MSW were generated in 2020, with a 0.78 kg/capita/day generation rate. Only 86% of cities and districts have access to MSW collection services. The current practice of MSW management is reliance on landfill (44%). There are 164 landfills operating countrywide, receiving about 5749 tons of MSW per day. Recycling, incineration, and composting share 4%, 4%, and 2% of MSW generation, respectively. In 2021, the total revenue that was recovered from recyclables was USD 56M. The study concludes several major challenges and proposes valuable suggestions, which may be beneficial for the improvement of the current system to support the sustainable management of MSW in Cambodia.

Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm

Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that "Pendik" is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other critical multi-dimensional problems related to COVID-19 and support sustainability and circular economy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10479-022-04822-0.