Search Results (15)

Municipal solid waste (MSW) accumulation is a critical global challenge for society and governments, impacting environmental and social sustainability. Efficient separation of MSW is essential for resource recovery and advancing sustainable urban management practices. However, manual classification remains a slow and inefficient practice. In response, advances in artificial intelligence, particularly in machine learning, offer more precise and efficient alternative solutions to optimize this process. This research presents the development of a light deep neural network called R3sNet (three “Rs” for Reduce, Reuse, and Recycle) with residual modules trained end-to-end for the binary classification of MSW, with the capability for faster inference. The results indicate that the combination of processing techniques, optimized architecture, and training strategies contributes to an accuracy of 87% for organic waste and 94% for inorganic waste. R3sNet outperforms the pre-trained ResNet50 model by up to 6% in the classification of both organic and inorganic MSW, while also reducing the number of hyperparameters by 98.60% and GFLOPS by 65.17% compared to ResNet50. R3sNet contributes to sustainability by improving the waste separation processes, facilitating higher recycling rates, reducing landfill dependency, and promoting a circular economy. The model’s optimized computational requirements also translate into lower energy consumption during inference, making it well-suited for deployment in resource-constrained devices in smart urban environments. These advancements support the following Sustainable Development Goals (SDGs): SDG 11: Sustainable Cities and Communities, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Action. Full article

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and $R^2$ is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and $R^2$ is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations. Full article

Around the world, managing and classifying solid waste is one of the most important challenges to sustaining economic growth and preserving the environment. The objective of this paper is to propose the use of Smart Ecological Points as a strategy to address the problem of solid waste management systems at the source, which has become one of the biggest problems globally, and Colombia is no exception. This article describes the current state of the problem in the country and presents a prototype of a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and machine learning algorithms, which will reduce the time necessary for the classification of recyclable and non-recyclable waste, increasing the percentage of waste that can be reused and minimizing health risks by reducing the probability of being contaminated at the source, an aspect that is very common when waste is sorted manually. According to the results obtained, it is evident that the proposed prototype made an adequate classification of waste, generating the possibility of it being manufactured with existing technology in order to promote adequate waste classification at the source. Full article

IoT-based smart e-waste management is an emerging field that combines technology and environmental sustainability. E-waste is a growing problem worldwide, as discarded electronics can have negative impacts on the environment and public health. In this paper, we have proposed a smart e-waste management system. This system uses IoT devices and sensors to monitor and manage the collection, sorting, and disposal of e-waste. The IoT devices in this system are typically embedded with sensors that can detect and monitor the amount of e-waste in a given area. These sensors can provide real-time data on e-waste, which can then be used to optimize collection and disposal processes. E-waste is like an asset to us in most cases; as it is recyclable, using it in an efficient manner would be a perk. By employing machine learning to distinguish e-waste, we can contribute to separating metallic and plastic components, the utilization of pyrolysis to transform plastic waste into bio-fuel, coupled with the generation of bio-char as a by-product, and the repurposing of metallic portions for the development of solar batteries. We can optimize its use and also minimize its environmental impact; it presents a promising avenue for sustainable waste management and resource recovery. Our proposed system also uses cloud-based platforms to help analyze patterns and trends in the data. The Autoregressive Integrated Moving Average, a statistical method used in the cloud, can provide insights into future garbage levels, which can be useful for optimizing waste collection schedules and improving the overall process. Full article

Recycling is very important for a sustainable and clean environment. Developed and developing countries are both facing the problem of waste management and recycling issues. On the other hand, the Internet of Things (IoT) is a famous and applicable infrastructure used to provide connection between physical devices. It is an important technology that has been researched and implemented in recent years that promises to positively influence several industries, including recycling and trash management. The impact of the IoT on recycling and waste management is examined using standard operating practices in recycling. Recycling facilities, for instance, can use IoT to manage and keep an eye on the recycling situation in various places while allocating the logistics for transportation and distribution processes to minimize recycling costs and lead times. So, companies can use historical patterns to track usage trends in their service regions, assess their accessibility to gather resources, and arrange their activities accordingly. Additionally, energy is a significant aspect of the IoT since several devices will be linked to the internet, and the devices, sensors, nodes, and objects are all energy-restricted. Because the devices are constrained by their nature, the load-balancing protocol is crucial in an IoT ecosystem. Due to the importance of this issue, this study presents an energy-aware load-balancing method for IoT-based smart recycling machines using an artificial chemical reaction optimization algorithm. The experimental results indicated that the proposed solution could achieve excellent performance. According to the obtained results, the imbalance degree (5.44%), energy consumption (11.38%), and delay time (9.05%) were reduced using the proposed method. Full article

This review appraised current research on enzyme-embedded biodegradable agricultural plastics and microbial degradation, given that the increased use of fossil-fuel-based plastics in agriculture involved significant environmental tradeoffs. Over 370 million tons of plastics were produced in 2019, releasing over 400 million tons of greenhouse gases during production, transportation, consumption, burning, and exposure to sunlight biodegradation. Less than 10% of bags are recycled at the end of their life, leading to environmental pollution. Thus, it is imperative to summarize studies that have suggested solutions of this problem. The scoping review approach was preferred, given that it established current practices and uncovered international evidence on bio-based solutions and conflicting outcomes. Bioplastics with low greenhouse warming potential had a small market share (approximately 1%). The accumulation of fossil-fuel-based plastics and poor post-use management releases mercury, dioxins, furans, and polychlorinated biphenyls (PCBs). Enzyme-embedded polymers degrade fast in the environment but lack the desired mechanical properties. Even though polylactic acid (PLA) and other bioplastics are better alternatives to synthetic polymers, they persist in the environment for years. Fast degradation is only practical under special conditions (elevated temperatures and humidity), limiting bioplastics’ practical benefits. The research and development of plastics that could degrade under ambient conditions through enzyme-catalyzed reactions and soil-inoculated microbes are ongoing. However, there are no guarantees that the technology would be profitable in commercial agriculture. Other limiting factors include the geographical disparities in agricultural plastic waste management. Future perspectives on the waste management of agricultural plastics require smart technologies, such as artificial intelligence (AI), machine learning (ML), and enzyme-embedded plastics that degrade under ambient conditions. The replacement of synthetic plastics with polylactic acid and polycaprolactone/Amano lipase (PCL/AL) composite films would offset the negative ecological effects. A major drawback was the slow research and development and commercial adoption of bio-based plastics. The transition to bioplastics was resource- and time-intensive. Full article

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed. Full article

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features. Full article

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries. Full article

For centuries, jigging has been a workhorse of the mineral processing industry. Recently, it has also found its way into the recycling industry, and the increasing concerns related to water usage has led to a renewed interest in dry jigging. However, the current scenario of increasing ore complexity and the advent of smart sensor technologies, such as sensor-based sorting (SBS), has established increasingly challenging levels for traditional concentration methods, such as jigging. Against this background, the current review attempts to summarize and refresh the key aspects and concepts about jigging available in the literature. The configuration, operational features, applications, types, and theoretical models of jigging are comprehensively reviewed. Three promising paths for future research are presented: (1) using and adapting concepts from granular physics in fundamental studies about the stratification phenomena in jigs; (2) implementing advanced control functions by using machine vision and multivariate data analysis and; (3) further studies to unlock the potential of dry jigs. Pursuing these and other innovations are becoming increasingly essential to keep the role of jigging as a valuable tool in future industry. Full article

Recycling is vital for a sustainable and clean environment. Developed and developing countries are both facing the problem of solid management waste and recycling issues. Waste classification is a good solution to separate the waste from the recycle materials. In this work, we propose a cloud based classification algorithm for automated machines in recycling factories using machine learning. We trained an efficient MobileNet model, able to classify five different types of waste. The inference can be performed in real-time on a cloud server. Various techniques are described and used in order to improve the classification accuracy, such as data augmentation and hyper-parameter tuning. Multiple industrial stations are supported and interconnected via custom data transmission protocols, along with security features. Experimental results indicated that our solution can achieve excellent performance with 96.57% accuracy utilizing a cloud server. Full article

A variety of cutting tool materials are used for the contact mode mechanical machining of components under extreme conditions of stress, temperature and/or corrosion, including operations such as drilling, milling turning and so on. These demanding conditions impose a seriously high strain rate (an order of magnitude higher than forming), and this limits the useful life of cutting tools, especially single-point cutting tools. Tungsten carbide is the most popularly used cutting tool material, and unfortunately its main ingredients of W and Co are at high risk in terms of material supply and are listed among critical raw materials (CRMs) for EU, for which sustainable use should be addressed. This paper highlights the evolution and the trend of use of CRMs) in cutting tools for mechanical machining through a timely review. The focus of this review and its motivation was driven by the four following themes: (i) the discussion of newly emerging hybrid machining processes offering performance enhancements and longevity in terms of tool life (laser and cryogenic incorporation); (ii) the development and synthesis of new CRM substitutes to minimise the use of tungsten; (iii) the improvement of the recycling of worn tools; and (iv) the accelerated use of modelling and simulation to design long-lasting tools in the Industry-4.0 framework, circular economy and cyber secure manufacturing. It may be noted that the scope of this paper is not to represent a completely exhaustive document concerning cutting tools for mechanical processing, but to raise awareness and pave the way for innovative thinking on the use of critical materials in mechanical processing tools with the aim of developing smart, timely control strategies and mitigation measures to suppress the use of CRMs. Full article

The rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management. In smart agricultural aviation spraying, the droplets’ distribution and deposition are important indexes for estimating effectiveness in plant protection process. However, conventional approaches are problematic, they lack adaptivity to environmental changes, and consumes non-reusable test materials. One example is that the machine vision algorithms they employ can’t guarantee that the division of adhesive droplets thereby disabling the accurate measurement of critical parameters. To alleviate these problems, we put forward an intelligent visual droplet detection node which can adapt to the environment illumination change. Then, we propose a modified marker controllable watershed segmentation algorithm to segment those adhesive droplets, and calculate their characteristic parameters on the basis of the segmentation results, including number, coverage, coverage density, etc. Finally, we use the intelligent node to detect droplets, and then expound the situation that the droplet region is effectively segmented and marked. The intelligent node has better adaptability and robustness even under the condition of illumination changing. The large-scale distributed detection result indicates that our approach has good consistency with the non-recyclable water-sensitive paper approach. Our approach provides an intelligent and environmental friendly way of tests for spraying techniques, especially for plant protection with Unmanned Aerial Vehicles. Full article

By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors. Full article