Waste Segregation Using Smart Bin and Optimization of Collection Routes

Due to the Earth's population rapid growth along with the modern lifestyle the urban waste constantly increases. People consume more and the products are designed to have shorter lifespans. Recycling is the only way to make a sustainable environment. The process of recycling requires the separation of waste materials, which is a time consuming procedure. However, most of the proposed research works found in literature are neither budget-friendly nor effective to be practical in real world applications. In this paper, we propose a solution: a low-cost and effective Smart Recycling Bin that utilizes the power of cloud to assist with waste classification. A centralized Information System (IS) collects measurements from smart bins that are deployed all around the city and classifies the waste of each bin using Artificial Intelligence and neural networks. Our implementation is capable of classifying different types of waste with an accuracy of 93.4% while keeping deployment cost and power consumption very low.

The research aimed to provide an optimized method for the determination of phosphorous concentration in different types of solid waste using inductive coupled plasma mass spectrometry (ICP-MS) technique. The analyzed waste matrices are: a) vegetable waste (P1), b) ash from the incineration of medical waste (P2) c) sewage sludge (P3) and d) sludge from the meat processing industry (P4). The results obtained by the ICP-MS method were compared with the UV-VIS spectrophotometric method for the determination of total phosphorus. In the case of using the colorimetric method by UV-VIS technique, lower results were obtained compared to the ICP-MS method due to the interferences given by the reagents used to determine the total phosphorus. The interferences given by other elements (metals) were also investigated and the performance parameters were determined such as detection limit, quantification limit, recovery and expandend incertainty using ICP-MS technique.

The entry of illegally discharged waste into the sewage system can cause serious damage to sewage pipes and harm the public domain. Besides industrial and communal sources, discharges from clandestine laboratories are of special forensic interest. Aim of this study is to investigate the possibility to detect clandestine discharges of possibly hazardous waste directly at the point of discharge. Tests were performed at a facility where real wastewater was pumped in a controlled way through an open sewage pipe above ground. Chloride, ammonia, pH and conductivity electrodes were investigated for their ability to detect discharges of different types of waste. Waste samples were diluted up to 50 times in a static wastewater environment and pH, conductivity and chloride electrodes were able to distinguish all waste dilutions from blank wastewater. These three electrodes were then used for dynamic tests by placing them inside flowing sewage water and discharging different types of liquid waste ten meters upstream of them. Parameters of the waste discharges like volume, time interval and speed of discharge were varied and the responses of the electrodes were collected. The dynamic tests showed that these three electrodes were able to pick up different waste discharges in a real wastewater environment. It was found that a high sampling rate of the sensors would be required to connect a certain discharge to a specific household connection. These findings highlight the possibility to locate illegal discharges, coming from a variety of sources, using the waste discharge itself.

One of the major challenges that has been growing and has evolved in the last few decades is the production of waste. The types of waste produced has changed over the years and in the present day and age finding a constructive system for efficient and effective waste disposal has become a centre of concern. Application of AI, Machine Learning and other smart technologies have become focal points that play an extremely vital role in the management of different types of waste. This approach has optimized the process of handling waste. The methodology used in this work is the systematic literature review (SLR) to identify, evaluate, and synthesize studies on the application of artificial intelligence in waste management. The two major key contributing methods in this approach being-Neural Networks and Advanced Machine Learning Algorithms. The use of neural networks plays a crucial role in predicting the amount of waste produced. Whereas, the use of advanced machine learning algorithm will attain optimization in waste collection. This process of integration has reduced the waste quantity produced by 90%, landfill by 40% and transport costs by 15%. Implementation of AI and smart technologies in waste management process has reduced industrial waste by 30-50%, food waste by 40%, medical waste by 30% and has enhanced the recycling of plastics by 50%. AI has no doubt revolutionized the waste management process in multiple ways like reduction in waste, recycling of waste, smarter resource utilization and so forth. However, this reliance has to be limited to a considerable extent and major control of the whole process has to be taken care of with human aid. This study will play a crucial role in understanding the present trends and innovation, the effectiveness of this integration process and will help address environmental impact in a more efficacious approach.

The increase of waste generation and its disposal is a major problem and challenge faced by the most of the countries today.The uncontrolled disposal of different types of waste into environment is a great threat for the future of all living beings in the world. Therefore, it is essential to pay more attention for the waste management and disposal to avoid the detrimental impact on the environment. For the development of a proper treatment or disposal systems for waste management for any organization or municipality, it is essential to have the information with respect to waste composition, generation rate and its variation with time of that organization. Therefore, in this study, it was attempted to evaluate the composition and the variation of waste generation rate in the hostels of the undergraduates of University of Ruhuna with their activities.The study was conducted in the premises and surroundings of the hostels of the undergraduates for a period of one year (September to August). The waste samples were collected from each and every hostel once in two months. The waste samples were separated into different components (such as foods, paper, plastics, etc), weighed and determined the percentages of different components. The different types of waste generation rate per head were calculated based on the waste generation for the total study period. It was found that food remains, papers, plastics and polythene are the major components. The types of waste found in hostels of girls were less in numbers than that of boys. Leather, textiles, metals, etc could not be found in hostels of girls.Over ~95 % of waste found in girls’ hostels were food remains and boys generate food waste about ~71%. Per capita food waste generation by girls was ~ 207 g/day while it was ~23 g/day by boys. The generation of other types of waste was higher in hostels of boys than that of girls’ hostels. It appears that the amount of waste generation varied in different months basically due to examinations and other activities of the students. During examination periods, it seems that the undergraduates may change their food habits. Based on this study, it can be concluded that major component of waste found in undergraduate hostels was food wastes and the generation rate slightly varies with the activities of undergraduates. When designing of a waste management systems for higher education institutes where students reside, higher priority should be given for the management of food waste

Waste segregation is the process of sorting and separating different types of waste to permit recycling and proper disposal. Technology businesses are attempting to address the garbage problem from a variety of angles, including improving recycling systems and developing novel materials for compostable single-use items. However, many people today do not adequately separate their waste, particularly in congested areas where people and transactions move at a rapid pace, such as malls, airports, local supermarkets, etc. The researchers propose a project termed A.T.O.M.S., or Automated Trash Organizing Management System, which is a Multi-sensorial Self Segregating Trash Bin that will be made up of several electronic components, sensors, and computer languages, and automatically classifies the waste being disposed of into specific classifications. As part of this ongoing study, the initial testing of the prototype manages to perform its initial function by detecting the type of trash and segregating it accordingly and appropriately. The results will positively impact society since the waste management system is improved. Recommendations to modify the dimensions of some parts of the device and usage of alternative power were made. The implication of this research project is the contribution of innovative solutions toward waste management process enhancement. Keywords: Waste management, segregation, innovation, experimental research.

Globalization and industrialization have significantly impacted the dynamics of waste generation, management, collection, and processing, resulting in increasing amounts of various types of waste and serious environmental challenges. This paper analyzes the treatment and recycling of construction waste through the legislative framework and presents key challenges in the implementation of green policies in the Republic of Croatia. The methodological framework is based on a qualitative analysis of normative acts at the national level. Through statistical analysis and a detailed review of the waste categorization process, including the origin, type, and properties of waste, as well as statistical data on the produced, collected, and utilized amounts of construction waste in 2022, an insight into the current state of the construction waste management sector is provided. The example of the company “Reciklaža Mišić” demonstrates how recovered construction waste can lose its waste status and be used as recycled aggregate if the assessment and verification of the constancy of performance are carried out according to the technical specification aligned with the legal regulations governing construction products. The research results indicate the need for further familiarization of the construction sector with the procedure for removing waste status so that recycled aggregates can become a construction product and ensure market placement of materials, thereby contributing to the circular economy and reducing environmental burden. In conclusion, the research shows that achieving sustainable construction waste management requires further strengthening of the economic sector, better integration of ecological principles, and active involvement of the local community in the waste management process.

The garbage is collected in dustbin bins. Home and human society trash from all around the world. Our system is set up in such a way that it runs indefinitely gathers real-time data in order to optimize operating time and efficiency. This data will be sent through a wireless mesh network. We offer Trash Management via a Smart Bin System that detects the quantity of waste in the bin in this article. The Smartbin technology was put to the test in the open air. The information gathered sense-making techniques were used to apply data from the trash to improve the efficiency of our smart bin It also contains the information on seasonality is sent to the Municipal Corporation on a daily basis. This allows them to gather data in a more efficient and structured manner. Plastic, recyclable, and organic trash are all types of waste. Human civilization is made of a range of waste products on a regular basis, the majority of which is dry trash, plastic garbage, and recyclable waste are all types of waste. Sewage wastes, household wastes, and so forth. At every corner of the street, in outdoor trash cans and organizations that the municipality maintains. Household Dustbins are used to gather a family's trash, which is then disposed of ensure the garbage is collected and disposed of in the public trash cans society. Because trash in roadside containers is not regulated and collected, our primary goal is to gather correctly most of the time to effectively arrange and manage outdoor trash cans for a clean environment and a sanitary atmosphere.

Reusing waste as raw materials to produce other materials can entail a decrease in production costs and in the abusive use of natural resources. Furthermore, it can even improve the properties of the end product or material. In this sense, a review of the most relevant literature published in recent decades shows that numerous solutions have been proposed or implemented, such as its use to produce construction materials, catalysts, pigments, pozzolana, refractory materials, glass-ceramic products, etc. Our research group has verified the viability of using different types of waste as secondary raw materials to obtain several types of ceramic, glassy and glassceramic materials, as well as frits. This article highlights several types of industrial waste that have both non-toxic (Li, Ca and Mn) and highly toxic (Cr VI) differentiating elements that can be used in sintering and vitrification industrial processes to immobilise them or render them inert. We studied the compositions and characterised the various materials obtained, conducting toxicity and leaching tests on waste/materials designed with high amounts of chromium. A suggestion for future lines of research has been proposed.

Human activities generate wastes, some are harmful and others are not so harmful to man and the environment. Of concern however, are hospital wastes, which come in the form of hazardous wastes such as chemical, radioactive, pathological and anatomical wastes amongst others. This study therefore investigated the public health implications of prototype of healthcare waste management in Jos Metropolis, Plateau State, Nigeria. Data were collected using quantitative and qualitative methods respectively, while nine (9) key informants were interviewed, and observation was utilized as the qualitative methods. The hospital generates two types of wastes: municipal solid wastes and special healthcare wastes which in most cases are mixed up without proper segregation into different color-coded containers with specified biohazard symbol as being recommended by the international best practices. Two hundred and thirteen (61.4%) of the respondents indicated that safety boxes are used in segregating sharps, 40 (11.5%) of the respondent indicated puncture proof containers, while 72 (20.7%) indicated plastic bags. One hundred and seventy two (59%) of the respondents indicated that colour codes are used for different types of wastes, 108 (37%) of the respondents indicated no. Respondents of 114 (39%) indicated no, showing that wastes were not treated before disposal, 54 (19%) of the respondents indicated yes, that wastes are treated before disposal. A total of 172 (49%) of the respondents indicated that wastes are transported to the final disposal site with enclosed compaction vehicle, while 75 (21.6%) of the respondents indicated that wastes are transported with open vehicle. One hundred and eighty eight (54.2%) of the respondents who are mostly females indicated that diseases/infections were a major effect of wastes on health workers, patients and the residence of Jos metropolis. Three hundred and six (88.2%) of the respondents indicated that hospital waste transmit diseases caused by Staphylococcus aureus, Klebsiella pneumonia, Escherichia coli and Proteus mirabilis. Other diseases include typhoid and cholera. In general, the wastes treatment method is poor as reflected in the disposal of solid wastes in open space without incineration, which gives room for scavengers most of whom are children, whose health implication cannot be overemphasized. There is no existing laid down waste management policy, neither is there record of special training for waste handlers. Workers should imbibe the culture of proper waste management that is in line with international best practices. In addition, injuries should be reported to appropriate hospital authority, where prompt action should be taken to avert the threat of spread of disease or infection.

Due to the Earth’s population rapid growth and the modern lifestyle, the urban waste creation rate constantly increases.Organic waste, which forms a significant part of municipal solid waste, has caused increasing environmental concerns. Recycling is the only way to make a sustainable environment. According to estimations from the United States Environmental Protection Agency, around 24 percent of the total waste can be composted. Instead, most of it is land filled and incinerated. By composting organic waste,we can preserve resources and produce a valuable by-product that can be used as a locally produced fertilizer. In this paper, we propose a solution: a low-cost and effective Smart Compost Bin that utilizes modern technologies, such as environmental sensors and the LoRaWAN protocol to assist with the compost process.A centralized Information System collects measurements from smart bins that can be deployed anywhere and can further assist with the waste collection process. We have performed several experiments with our smart bin prototype, evaluated its efficiency and concluded that it is a feasible solution.

Waste management handles all kinds of waste, including household, industrial, municipal, organic, biomedical, biological, and radioactive wastes. People still face challenges in proper disposal methods for different types of waste, including landfill-bound items, recyclable materials, and biodegradable waste. Inadequate waste management poses a significant and multifaceted global challenge. The conventional method of segregating waste is a time-consuming and ineffective method that wastes human power and money. To address this issue in real time, sophisticated and sustainable waste management systems need to be implemented. The latest advancements in computer vision and deep learning offer efficient solutions for effective recycling and waste management. Existing deep learning models exhibited various limitations, such as detection accuracy and computational inefficiency, particularly when dealing with objects of varying sizes and exhibiting high degrees of visual similarity. These limitations generate various challenges in effectively capturing and representing the nuanced features of visually similar objects. To address this problem, we proposed the stacking of an enhanced Swin Transformer, improved ConvNeXt, and a spatial attention mechanism. The enhanced Swin transformers incorporate two key components- hierarchical feature extraction and shifting window mechanism to extract the global features from the garbage images effectively. The shifting window mechanism extracts the most important features from various regions of the images to identify the objects. In contrast, the hierarchical feature extraction captures long-range dependencies within the image to effectively identify different types of garbage. The improved ConvNext block with optimized parameterization extracts the local features of the image. This enhanced feature extraction capability enables the model to effectively discern fine-grained details of individual garbage particles, such as shape, texture, and subtle variations in color and appearance, leading to more accurate classification results. When we evaluated the performance of the proposed model using the publicly available Garbage Classification dataset, it attained 98.97% accuracy, 98.42% Precision, and 98.61% Recall. Due to its lightweight and low computational time and power, the proposed model surpasses the existing state-of-the-art deep learning models.

As there is an obvious and increasing need to preserve valuable resources and reduce waste and pollution, several researches are focusing into this area. However, the solutions provided are neither budget-friendly nor effective to be practical in a real-world application. In this paper, we present a Smart Recycling Bin using modern approaches for waste classification. The design of the system permits the low-cost manufacturing of the final product, while uses state of the art technologies such as neural networks and the LoRaWAN protocol. We implemented a low-cost Smart Bin prototype able to classify different types of waste with an accuracy of 92.1%. The system also remotely transmits valuable data to the corresponding authorities, which can increase their effectiveness in waste management.

The purpose of this research is to identify and manage toxic and hazardous waste, called THW, present in the Concentrating Division of PT Freeport Indonesia. The identification and management process of this waste was based on the Indonesian Government Regulation Number 22 of 2021. Furthermore, this research uses a qualitative approach where data was collected by field observations and secondary data collection. The data collected incorporated different types and weights of the THW generated in the last 5 years. These wastes were identified based on their level of hazardousness as well as their source. The results showed that the total THW weight generated in the last 5 years was approximately 975,814 kg and the total waste generation rate is 16,264 kg/month. In addition, the most dominant waste types generated include grease waste, fire assay waste, and used oil. Various waste management processes were conducted in this study. They include reduction, storage, collection, and utilization. The identification and management of THW in the Concentrating Division is almost consistent with the Indonesian Government Regulation Number 22 of 2021. However, the process of waste collection in the target area is not yet consistent with this regulation because the Waste Transfer Point (WTP) is still under construction.

Solid waste management (SWM) has received significantly more attention in recent years, especially in developing countries for sustainable development. SWM system encompasses various interconnected processes which contain numerous complex operations. Recently, deep learning (DL) has attained momentum in providing alternative computational techniques to determine the solution of various SWM problems. In the last few years, researchers have focused on this domain; therefore, significant research has been published. The literature shows that no study evaluates the potential of DL to solve the various SWM problems. The study performs a systematic literature review which has complied 25 studies, published between 2019 and 2021 in.reputed journals and conferences. The selected research studies have implemented the various DL models and analysed the application of DL in different SWM areas, namely waste identification and segregation, real-time bin level detection, and prediction of waste generation. The study has defined the systematic review protocol that comprises various criteria and a quality assessment process to select the research studies for review. The review demonstrates the comprehensive analysis of different DL models and techniques implemented in SWM. It also highlights the application domains and compares the reported performance of selected studies. Based on the reviewed work, it can be concluded that DL exhibits the plausible performance to detect the different types of waste and bin level.