Estimating energy left in discarded alkaline batteries: Evaluating consumption and recovery opportunities.

Each year, a significant number of single-use alkaline batteries with untapped energy are discarded. This study aims to analyze the usage patterns of alkaline batteries based on a dataset of 1021 used batteries, ranging from Size AA to 9V, collected from households in the State of New York. We measure the energy loss resulting from underutilized batteries and examine the corresponding environmental and economic impacts on a national scale. Discarded AA alkaline batteries maintain about 13 % of their initial energy, that results in an estimated annual energy loss of 660 MWh for all AA alkaline batteries in the U.S., and about 40 MWh in New York State. Annually in the U.S., consumers discard AA alkaline batteries with approximately $80 million worth of unused energy, including $4.8 million in New York State alone. We also show that the lifecycle impact of batteries should be multiplied by 1.25 to account for their underutilization. To address these issues, we propose actionable recommendations for improving battery consumption practices and facilitating End-of-Life/Use (EoL/U) recovery processes. The findings show the need for policy interventions to better manage battery usage and disposal toward reducing energy waste and mitigating environmental impacts.

Human activity recognition in an end-of-life consumer electronics disassembly task.

The production of electronic waste, also known as e-waste, has risen with the growing reliance on electronic products. To reduce negative environmental impact and achieve sustainable industrial processes, recovering and reusing products is crucial. Advances in AI and robotics can help in this effort by reducing workload for human workers and allowing them to stay away from hazardous materials. However, autonomous human motion/intention perception is a primary barrier in e-waste remanufacturing. To address the research gap, this study combined experimental data collection with deep learning models for accurate disassembly task recognition. Over 570,000 frames of motion data were collected from inertial measurement units (IMU) worn by 22 participants. A novel sequence-based correction (SBC) algorithm was also proposed to further improve the accuracy of the overall pipeline. Results showed that models (CNN, LSTM, and GoogLeNet) had an overall accuracy of 88-92%. The proposed SBC algorithm improved accuracy to 95%.

Cost analysis and optimization of Blockchain-based solid waste management traceability system.

As global concerns over End-of-Life (EoL) wastes released to the environment is rising, the need for enhancing the transparency of recycling systems is growing. To address the waste traceability issue, technologies such as Blockchain can be instrumental in the proper disposal and handling of wastes. In this paper, we propose a Blockchain-based Solid Waste Management (SWM) model that can help municipalities enhance the efficiency of their waste management efforts. A Blockchain framework owned and controlled by a municipality is proposed in which customer companies pay to join the platform to avail services from the suppliers managed by the municipality. The cost burdens to both supplier and consumer companies have been discussed. In addition, an optimization model is developed to determine the optimal quantity of waste that can be traded between supplier and consumer companies in order to maximize their profit based on parameters such as the number of suppliers, consumer companies, and the processing capacity of customer companies and several constraints including maximum storing capacity, storage, and transportation constraints. Further, the cost aspects associated with Blockchain implementation are estimated from several use cases obtained from companies providing Blockchain solutions.

The future of waste management in smart and sustainable cities: A review and concept paper.

The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product life-cycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed.

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

The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins.

An investigation of used electronics return flows: a data-driven approach to capture and predict consumers storage and utilization behavior.

Consumers often have a tendency to store their used, old or un-functional electronics for a period of time before they discard them and return them back to the waste stream. This behavior increases the obsolescence rate of used still-functional products leading to lower profitability that could be resulted out of End-of-Use (EOU) treatments such as reuse, upgrade, and refurbishment. These types of behaviors are influenced by several product and consumer-related factors such as consumers' traits and lifestyles, technology evolution, product design features, product market value, and pro-environmental stimuli. Better understanding of different groups of consumers, their utilization and storage behavior and the connection of these behaviors with product design features helps Original Equipment Manufacturers (OEMs) and recycling and recovery industry to better overcome the challenges resulting from the undesirable storage of used products. This paper aims at providing insightful statistical analysis of Electronic Waste (e-waste) dynamic nature by studying the effects of design characteristics, brand and consumer type on the electronics usage time and end of use time-in-storage. A database consisting of 10,063 Hard Disk Drives (HDD) of used personal computers returned back to a remanufacturing facility located in Chicago, IL, USA during 2011-2013 has been selected as the base for this study. The results show that commercial consumers have stored computers more than household consumers regardless of brand and capacity factors. Moreover, a heterogeneous storage behavior is observed for different brands of HDDs regardless of capacity and consumer type factors. Finally, the storage behavior trends are projected for short-time forecasting and the storage times are precisely predicted by applying machine learning methods.