A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities

Mathematical optimization of waste management systems: Methodological review and perspectives for application

The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders.

Forecasting contamination in an ecosystem based on a network model

This paper aims to predict heavy metal pollution based on ecological factors with a new approach, using artificial neural networks (ANNs), by significantly removing typical obstacles like time-consuming laboratory procedures and high implementation costs. Pollution prediction is crucial for the safety of all living things, for sustainable development, and for policymakers to make the right decisions. This study focuses on predicting heavy metal contamination in an ecosystem at a significantly lower cost because pollution assessment still primarily relies on conventional methods, which are recognized to have disadvantages. To accomplish this, the data collected for 800 plant and soil materials have been utilized in the production of an ANN. This research is the first to use an ANN to predict pollution very accurately and has found the network models to be very suitable systemic tools for modelling in pollution data analysis. The findings appear are promising to be very illuminating and pioneering for scientists, conservationists, and governments to swiftly and optimally develop their appropriate work programs to leave a functioning ecosystem for all living things. It has been observed that the relative errors calculated for each of the polluting heavy metals for training, testing, and holdout data are significantly low.

The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW)

Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.

Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.

Advancing the bioconversion process of food waste into methane: A systematic review

With the continuous rise of food waste (FW) throughout the world, a research effort to reveal its potential for bioenergy production is surging. There is a lack of harmonized information and publications available that evaluate the state-of-advance for FW-derived methane production process, particularly from an engineering and sustainability point of view. Anaerobic digestion (AD) has shown remarkable efficiency in the bioconversion of FW to methane. This paper reviews the current research progress, gaps, and prospects in pre-AD, AD, and post-AD processes of FW-derived methane production. Briefly, the review highlights innovative FW collection and optimization routes such as AI that enable efficient FW valorization processes. As weather changes and the FW sources may affect the AD efficiency, it is important to assess the spatio-seasonal variations and microphysical properties of the FW to be valorized. In that case, developing weather-resistant bioreactors and cost-effective mechanisms to modify the raw substrate morphology is necessary. An AI-guided reactor could have high performance when the internal environment of the centralized operation is monitored in real-time and not susceptible to changes in FW variety. Monitoring solvent degradation and fugitive gases during biogas purification is a challenging task, especially for large-scale plants. Furthermore, this review links scientific evidence in the field with full-scale case studies from different countries. It also highlights the potential contribution of ADFW to carbon neutrality efforts. Regarding future research needs, in addition to the smart collection scheme, attention should be paid to the management and utilization of FW impurities, to ensure sustainable AD operations.

Digitalization of waste management: Insights from German private and public waste management firms

Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste management. While these advances promise an increasingly digitalized future for collecting, sorting, and recycling waste material, little is known about the current extent of digitalization by waste management firms. Available studies focus on firms' digitalization intentions, largely neglecting the level of actual adoption of digital technologies, and do not differentiate the level of digitalization alongside different steps of the waste management value chain. Our study reports on a cross-sectional descriptive survey that captures current digitalization efforts and strategies of 130 public and private waste management firms in Germany. We analyze their levels of digitalization along with different steps of the waste management value chain, explore their different objectives, approaches, and transformational measures with regard to digitalization. Our findings reveal that while the perceived importance of digitalization in the waste management sector continues to grow, the actual adoption of advanced digital technologies falls notably behind intentions reported in 2016 and 2017. We explore the reasons for this gap, point out so far largely ignored research opportunities, and derive recommendations for waste management firms and associations.

The first mile problem in the circular economy supply chains - Collecting recyclable textiles from consumers

This paper presents the first mile problem of the circular economy supply chains: organizing the collection of used materials from individual consumers. Efficient solving of this problem is a prerequisite for recycling, as it ensures that adequate volumes of used materials, in this study end-of-life textiles, can be transformed into high-quality raw materials. Textile features affect the collection system design and make used textiles differ from other household waste fractions, such as glass or plastic. The aim is to investigate how to collect textile waste from consumers in a way that addresses the specific features of textiles, and the scarce resources of the collector organizations, but results in the largest collected yields to eventually drive up the recycling rates of used textiles. The study takes a supply chain view to reverse flows of textiles. Key experts from a sample of textile organizations were interviewed to understand their collection practices and possible future solutions, and a data analysis model was constructed to provide accurate prediction of end-of-life textile volumes. This study proposes how the first mile problem of end-of-life textiles, the collection from households, can be modelled in a situation where no historical volume data is available. The practical contribution lies in the urgent need for textile collection solutions, as municipal actors and waste companies in Europe are searching for efficient solutions to respond to the requirement of separate used textile collection by 2025. The study demonstrates how a circular economy problem with multiple unknows and uncertainties can be modelled quantitatively.

A deep learning approach based hardware solution to categorise garbage in environment

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

Assessment of urban solid waste management systems for Industry 4.0 technology interventions and the circular economy

The ongoing fourth industrial revolution, Industry 4.0 (I4.0), is transforming various industries across the globe. At the same time, resource scarcity with high consumption rates has led to the development of the circular economy. Both concepts advocate for sustainable growth and waste minimization. In developing countries, the integrated solid waste management framework is undergoing modification under I4.0 and the circular economy. Urban local bodies are often unable to measure the readiness of their waste management systems to transform under I4.0 and the circular economy. Here the novel concept of Waste 4.0 is developed. 'Waste 4.0' is a readiness assessment tool to promote the comprehensive transformation of municipal solid waste management under I4.0 and circular economy. This tool has eight determinants for assessing the municipal solid waste management of urban local bodies. To validate Waste 4.0 the urban local bodies of Indore and Sagar, India were used as case studies. The readiness index for I4.0 in the municipal solid waste management system in Indore and Sagar was 0.72 and 0.14, respectively. The readiness index for circular economy focus in Indore and Sagar was 0.65 and 0.13, respectively. The Indore urban local body was classified as an 'Experienced' player for I4.0 Intervention and a 'circular economy fast adopter' for circular economy focus under I4.0 readiness. The Sagar urban local body was classified as a 'Hesitator' player for I4.0 Intervention and achieved the 'Business as usual' tag for circular economy focus under I4.0 readiness. With the Waste 4.0 assessment results urban local bodies can better plan and thus transform their municipal solid waste management systems under I4.0 and the circular economy.

Relief Supply Chain Management Using Internet of Things to Address COVID-19 Outbreak

Nowadays, due to the COVID-19 outbreak, the most significant factor to be considered all over the world is to manage this pandemic and especially to address positive cases, efficiently and effectively. This can be achieved by simultaneously utilizing the present network with supply chain settings and also the Internet of Things (IoT). This consideration enables the accurate monitoring of suspected cases in real-time to optimize total service time. Hence, this paper firstly designs two sub-models to minimize distance and traffic while minimizing total response time. Our main contribution in this paper is to develop a dynamic scheme using IoT to deal with suspected cases. We also investigate the proposed methodology on a real case problem in Canada. A comprehensive analysis of the proposed methodology behavior has been conducted and the results showed the managerial decision-making process to address COVID-19 patients. The proposed approach establishes efficient strategies to identify suspicious COVID-19 cases and provide them with medical observance in a short time when utilized with IoT. The obtained results of the considered scenarios show 12% up to 15% improvement in the ambulance response time when using IoT.

Collection of recyclable wastes within the scope of the Zero Waste project: heterogeneous multi-vehicle routing case in Kirikkale

There is an increase in the amount of resource use due to the rise in population, urbanization, and industrialization. Also, the amount of waste increases due to an increase in consumption and resource use. Countries are developing new policies depending on both decreasing resources and environmental problems caused by waste. The "Zero Waste" project was launched to recycle waste and to reduce environmental pollution in Turkey. The project aims to separate recyclable waste at its source and recycle them. One of the problems encountered in the implementation of the project is collecting the waste from temporary storage areas. In this study, the problem of transportation of wastes from temporary warehouses to the main warehouse was discussed in Kırıkkale/Turkey. A three-step solution approach has been proposed to the solution of the problem. In the first stage, the amounts of waste generated at the addresses to collect were estimated. In the second stage, the addresses to be visited are classified with an approach based on Pareto analysis according to the calculated waste amounts. According to this classification, it is planned which addresses will be visited on which day of the week. At the last stage, the problem is modeled as a heterogeneous multi-vehicle routing problem, which also takes into account the daily working hours and vehicle capacity constraints. According to the result of the mathematical model, the number of vehicles needed for waste collection, the types of vehicles, and the routes of the vehicles were found. Considering the implementation stages of the Zero Waste project, three different case studies are handled for Kırıkkale. These case studies have been solved by considering different waste rates. According to the results, the waste collection plan was made economically by visiting fewer spots in a week.

A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances

Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.

Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review

The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - "ReWaste4.0" deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore "smart bins" with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or "digital readiness" are summarized.