Waste Classification Research Articles

The identification and classification of energy waste for efficient energy supervision in manufacturing factories

To respond to global climate change, “Net-Zero” targets have been set up by different governments, organizations, companies, etc. In this regard, manufacturing companies are urged to improve energy efficiency of their production systems. Reducing unnecessary energy consumption, namely energy waste, is therefore becoming critical. Unfortunately, the concept of energy waste has not been clearly defined and classified in both academia and industry. This study aims to uncover the key characteristics of energy waste through an interview-based case study. In total, twenty-five practitioners in different manufacturing factories from China, the UK, Japan, Germany, Austria, and Kenya were interviewed. These interviewees believe that on average energy performance can be improved by 10–20% through energy waste management. Besides, less energy waste results in more stable energy consumption, which can facilitate firms to conduct energy supervision more easily and accurately. Based on the analysis of these cases, an energy waste analytic framework was developed. Thirteen types of energy waste were identified and classified into four categories: 1) management-related behavioural energy waste (Mb-EW); 2) management-related technical energy waste (Mt-EW); 3) technology-related energy waste (T-EW); and 4) design-related energy waste (D-EW). A “step-by-step” strategy is then proposed for reducing energy waste. Both research and development (R&D) efforts and capacity-building efforts should be made to further reduce energy waste. Finally, future research directions are raised, including tools development for reducing energy waste and preparing assessment indicators on energy waste.

Promoting and maintaining public participation in waste separation policies — A comparative study in Shanghai, China

Waste management is an evolutionary system, but few studies have explored how and why public willingness to participate in waste separation changes, or explored possible paths to increase residents’ willingness to participate. This paper took Shanghai as a case study of a city that has experienced recent environmental policy change to embrace an evolutionary feedback perspective. The results showed that after the policy implementation, resident’s satisfaction with waste management increased by only 5.3%, and participation willingness actually decreased by 5.4%. A Geodetector model showed that both separation attitude and knowledge are highly important both before and after policy implementation. Residents’ satisfaction with community waste management showed the largest increase soon after the policy implementation while the importance of time occupied by waste separation dropped significantly. Policy simulation by a system dynamics model showed that the community-driven scenario was able to achieve 95% participation 2 years earlier than baseline scenario. Improving community satisfaction by improving waste management infrastructure can offset the adverse effects of an increased number of waste categories. The study helps to quantify the interaction between institutional change and public participation and find effective measures to maintain public participation in environmentally-friendly behaviors.

Real time waste classification using deep learning and AV: Deep learning and implementation in the frontend

To combat climate change, accurate waste disposal is essential at the point of disposal. Strong greenhouse gases like methane are released into the atmosphere when items that might be recycled or composted are instead dumped in landfills. Current efforts to lessen the disposal of incorrect garbage are often costly, incorrect, and lengthy. In this project, we offer NoWa, an intuitive smartphone app that instantly categorises waste into recyclable or compost for consumers. NoWa uses highly efficient deep learning algorithms, using modern deep learning techniques and models. We have tested several convolution neural network topologies for garbage detection and classification. On the test set, our best model, a multi-layer deep learning residual convoluted neural network, has an accuracy of more than 95%- a number higher than what has ever been achieved in such models.

Fatty acids and mineral composition of seed oils extracted from different Rosa L. taxa

In the study, the macro and micro elements and fatty acid contents in rose seeds, which are generally evaluated in the waste category, were determined. Among the plants belonging to these taxa, only the registered varieties belonging to four taxa were used, while the others were genotypes. The plant materials of the study include rosehip (R. canina L.), Yildiz variety of rosehip (R. canina L. cv ‘Yildiz’), Syrup rose (R. heckeliana Tratt. subsp. vanheurckiana), Austrian briar (R. foetida Herrm.), lax rootstock [R. caesia Sm. (Syn: R. laxa Retz.)], wild rose [R. montana subsp. woronovii Chaix subsp. woronovii (Lonacz) Ö. Nilsson L.], hybrid landscape roses (R. x hybrida) and Hosap rose [R. pisiformis (Christ) D.] taxa. It was determined that the contents of nitrogen, potassium, phosphorus, magnesium and calcium of the macro elements examined in the seed samples differed statistically from each other. The seeds of different Rosa L. species examined within the scope of the research can be considered as one of the fruit oil sources with its 3.71-10.01% oil content. The fatty acid contents were determined as follows: linoleic acid (ω6) contents in the taxa ranged from 41.63 to 50.11% with an average of 44.88%; oleic acid (ω9) ranged from 20.80 to 30.27% with an average of 24.95%; linolenic acid (ω3) varied between 14.00-28.51% with an average of 19.20%; arachidic acid ranged from 0.75-1.63% and the average was 1.97%; eicosenoic acid ranged between 0.13-0.65% and averaged 0.33%; palmitoleic acid contents ranged from 0.08-0.60; behenic acid varied between 0.08-0.19% with 0.11% average. It was observed that the (ω3/ω6 ratio of the hybrid rose, which is especially used as a landscape rose and whose fruits are not evaluated, had an average value. R. canina ‘Yildiz’ cultivar showed a high (ω3/ω6 ratio, which is important in health terms. The high oleic acid contents found in these taxa are important results.

Activation Analysis for the Inboard Region of FNSF Using SERPENT

This study describes an application of the SERPENT 2 code with the TENDL-2017 nuclear data library and the latest available model features of the Fusion Energy System Studies–Fusion Nuclear Science Facility (FNSF), to evaluate the activation of components after shutdown at 1, 10, and 100 years, assuming a plant lifetime of 8.5 full-power years. The primary parameters evaluated include the specific activity, decay heat, and waste disposal rating (WDR). The specific activity and decay heat are calculated with SERPENT 2 using a 360-deg model of the FNSF, while the WDR is calculated and classified based on the waste disposal limits established by the U.S. Nuclear Regulatory Commission under 10 CFR 61.55 as well as by using the Fetter approach. A python-based script developed for a previous high-level waste classification and analysis study was implemented and adapted to this research to calculate the WDR by comparing nuclide concentrations to the values established in 10 CFR 61.55 to generate a waste classification for each component surveyed. As only three short-lived isotopes have limitations for classifications beyond Class A, of which only 63Ni is present in appreciable quantities, there is a limit to the amount that short-lived isotopes contribute to the most significant waste analyzed here. In most cases, a handful of long-lived isotopes can be problematic, such as 59Ni and 94Nb, for example, which are solely responsible for multiple Class C classifications. The results herein reported heavily depend on the specific materials and mass/volume fractions in the specific model used in this study, which has changed and evolved since the inception of the FNSF concept and past studies. Therefore, the more significant contributions of this study may be the development of a modeling and simulation toolkit and a strategy to perform these calculations, so to help evaluate and optimize future fusion facilities.

70. Environmental Sustainability of Internal Fixation Hardware

PURPOSE: Environmental sustainability has become a critical initiative among several industries, but it is rarely mentioned in healthcare. The environmental footprint of surgical procedures is not only based on the resource utilization of operating rooms, but rather on the entire value chain of intraoperative products utilized. The purpose of this investigation was to assess the corporate environmental responsibility of two leading manufacturers of internal fixation hardware. METHODS: The sustainability practices of two leading internal fixation hardware manufacturers were investigated based on self-reported corporate environmental data. Data were standardized per $1 million USD in revenue. Recent corporate initiatives and their resulting sustainability impact were analyzed over the last six years, specifically within the categories of greenhouse emissions (in metric tons of carbon dioxide equivalent, MTCO2e), water withdrawal (in cubic meters, m3), solid waste (in metric tons), and energy utilization (in gigajoules, GJ). RESULTS: Regarding greenhouse emissions, Company S most recently reported 10.0 MTCO2e per $1M in revenue, while their competitor Company D reported 12.0 MTCO2e per $1M in revenue. Company S recently reduced its carbon footprint at a rate of 1.9 MTCO2e per $1M per year, which has been nearly twice as fast as Company D at 1.0 MTCO2e per $1M per year. Regarding water withdrawal, Company S utilized 5.6 m3 per $1M in revenue, whereas Company D utilized only 1.43 m3 per $1M in revenue. Neither company has made substantial changes to their water withdrawal rates. Regarding solid waste, Company S only produced 0.34 tons of solid waste per $1M in revenue, whereas Company D produced 1.22 tons of solid waste per $1M in revenue. Company D currently recycles an impressive 86.9% of its nonhazardous solid waste. Over the past five years, Company S has diverted 25 million pounds of waste from landfills by reprocessing end of lifecycle products. Regarding energy utilization, Company S utilized 123.2 GJ per $1M in revenue, whereas Company D utilized 155 GJ per $1M in revenue. Both companies have made large strides in their energy reductions, such that Company S has reduced their energy utilization by 2.5 GJ per $1M per year, and Company D has reduced their energy utilization by 7.0 GJ per $1M per year. Company S more than doubled its renewable electric power utilization from 102.7 GJ per year to 241.7 GJ per year. While this is impressive, it still only represents 13.6% of its total purchased energy utilization. CONCLUSION: There are substantial differences in the business practices between internal fixation hardware suppliers. Physician leaders need to begin discussing responsible resource procurement. Future research over the coming months will investigate various other suppliers to analyze the supply chain footprint of each of our procedures, as well as the supply chain footprint of our plastic surgery department overall.

Exploring the Factors Influencing the Behavioral Intention of Waste Classification - Based on the Theory of Planned Behavior

Sustainable development is receiving more attention as a result of the swift advancements in science, technology, and the global economy. As an effective means of environmental protection, waste classification still has a lot of development restrictions and limitations at present. Also, there are some differences in the results of existing researches on the intention of waste classification based on the theory of planned behavior. The goal of this study is to learn more about the variables that influence waste classification behavior. This study constructed a scale based on the planned behavior theory and collected data from a questionnaire survey of urban residents. SPSS was used to analyze the collected data, and correlation analysis, ANOVA, linear regression analysis, and other methods were used to analyze the relationship between variables. Finally, it was concluded that attitude, subjective norms and perceived behavioral control all affected the behavioral intention of waste classification of samples and showed a significant positive correlation with them. Through this study, the factors that influence urban residents' willingness to classify their waste could be understood, allowing for the systematic and all-encompassing promotion and development of waste classification as well as motivational enhancement of target individuals' waste classification behavior.

Black Plastic Waste Classification by Laser-Induced Fluorescence Technique Combined with Machine Learning Approaches

Sensor-based sorting devices commonly used in plastic recycling plants, mainly working in the near infrared range (NIR), are unable to identify black plastics, due to their low spectral reflectance. The aim of this work was to investigate the potentialities offered by laser-induced fluorescence (LIF) technique (spectral range 270–750 nm) for the identification of black polymers inside a plastic waste stream, thus allowing the possibility to build efficient sorting strategies to be applied in recycling plants. Representative samples of black plastics collected among the most utilized in household packaging were selected, constituted by four different types of polymers, i.e., expanded polystyrene (EPS), polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE). The acquired LIF spectra were processed using multivariate approaches in order to optimize polymer classification. The developed hierarchical—partial least square-discriminant analysis (Hi-PLS-DA) classification model, showed excellent performances, confirmed by the values of sensitivity and specificity values in prediction, being equal to 1. The correctness of classification obtained by LIF was confirmed by the application of Fourier Transform Infrared spectroscopy (FTIR) on the same samples. The achieved results demonstrated the potential of LIF technique combined with a machine learning approach as sorting/quality control tool of black polymers in recycling plants.Graphical

Influence of the Classification of Municipal Solid Wastes on the Reduction of Greenhouse Gas Emissions: A Case Study of Qingdao City, China

The municipal solid waste (MSW) sector is an important source of greenhouse gas (GHG) emissions. MSW classification can achieve waste reduction and improve resource utilization. However, few studies have investigated the effects of MSW classification on GHG emission reduction. Therefore, the GHG emissions under different MSW disposal modes before and after classification were studied based on the life cycle assessment method in the four districts of Qingdao City. The results showed that MSW classification could significantly reduce the GHG emissions during the whole MSW treatment process. The net carbon emissions(in CO2/MSW)during the whole process of waste treatment for mode 1 (mixed collection+landfill), mode 2 (mixed collection+incineration), mode 3 (waste classification+anaerobic digestion of food waste and other incineration), and mode 4 (waste classification+anaerobic digestion of food waste, recycling of recyclable waste, and other incineration) were 686.39, -130.12, -61.88, and -230.17 kg·t-1, respectively. Improving the classification efficiency of food waste had no significant impact on carbon emissions. The reduction in carbon emissions increased linearly with the improvement of waste recycling efficiency. For every 10% increase in the recovery efficiency of recyclable waste, the net carbon emission decreased by 26.6%(16.5 kg·t-1). Appropriate separation of food waste, improving the recycling efficiency of recyclable waste, and reducing the leakage rate of biogas from anaerobic digestion are feasible strategies to reduce carbon emissions from MSW disposal units through the classification of MSW.

Correction: Ali et al. A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem. Mathematics 2022, 10, 2675

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Oily waste to biosurfactant: A path towards carbon neutrality and environmental sustainability

Tons of oily waste accumulating worldwide has led to severe environmental problems and an increase in carbon footprint. The oily waste is rich in carbon and therefore its utilization as a substrate for the production of value-added products can aid in the concept of carbon neutrality. Oils can be directly utilized as substrate and microorganisms can catabolize them to produce biosurfactants. Biosurfactants being biodegradable and less toxic than synthetic surfactants are the molecules of the 21st century and are preferred candidates. Also, several fungal species can bio-transform oils to produce biosurfactants. Therefore, this study comprehensively summarizes different categories of oily waste generated worldwide, their sources, and environmental toxicity. The microbial efficiency towards oily waste utilization for the production of biosurfactants is reviewed. Following this, advance techniques including metabolic engineering, and omics approaches for biosurfactant production from this waste have been presented. Their global market and future perspective have been discussed to further emphasize the requirement for biosurfactants. The state-of-the-art information provided in various sections of this manuscript may aid the researchers to understand the relationship of oily waste utilization with carbon footprint generation. This directs attention and warrants future research towards the development of improved pathways/processes in oil waste based biorefineries.

Waste to energy conversion: a perspective from adult learners in South-East Nigeria

The study identified waste categories that can be turned into energy, energy conversion techniques, and ways that these techniques could support a circular economy in South East Nigeria from the viewpoint of adult learners. In the study, a descriptive survey design was employed. The study was directed by three research questions and two hypotheses. There were 67731 adult learners in the population. The study included a sample size of 605 participants. Mean, t-test, and Pearson correlation were used to examine the data, which was collected using questionnaires. The results showed that wood, sawdust leaves and bark of trees, paper and cardboard, food remains like vegetable stock, yam peels, among others, were the types of waste that can be converted to energy. The study found out that combustion technology, gasified technology, and anaerobic digestion, among others, were some of the waste to energy conversion techniques. The study also noted that waste to conversion techniques foster circular economy. The paper recommends that the government should display political will by designing and enacting laws that facilitate the development of indigenous waste-to-energy conversion technology. This way, even at the household level, these technologies will be inexpensive and accessible.

şçılık Meslek Yüksekokulu Eğitim Mutfağı Uygulamalarında Oluşan Atıkların Miktarı ve Çeşitlerinin Belirlenmesi ve Eğitimde Çevreci Öneriler, Ortaca Meslek Yüksekokulu Örneği

It is possible to find waste classification and waste management studies related to the food and beverage industry in the literature. Waste classifications and counts have been made in restaurants, hotel kitchens or production areas of mass nutrition systems. However, there is no waste management study related to educational kitchens in the literature. Educational kitchens differ from other kitchen applications interms of purpose and content. In educational kitchens, there is a variety of products, a variety ofproduction, and a small amount of production. Studies in these kitchens are carried out not for one type,but for the products of all different types of production. There are lessons in Ottoman cuisine, Turkish cuisine, regional cuisines, world cuisine, pastry, bread and many other fields. In addition, it is possible to increase the amount of waste due to mistakes and wrong applications during production. In educational kitchens, which are different for these and other reasons, information should be given not only on food production; but also on how to make production without waste andhow to reduce waste. Bringing this awareness to kitchen workers of the future at the beginning of their studies will be beneficial in the sectoral sense. For these purposes, the tools and equipment used in all applications and wastes produced in the application kitchen of Muğla Sıtkı Koçman University Ortaca Vocational School Cooking Program were counted, classified and recorded over a period of two weeks. According to the data obtained, it was concluded that the highest waste was production waste, while the second highest waste was food waste at the end of the day. Suggestions were made for the recycling of production and food waste; and for waste-free production practices training.

Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT

The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing city procedures in real time. The most important problem connected to smart city applications has been solid waste management, which can have adverse effects on society’s health and environment. Waste management has developed a challenge faced by not only evolving nations but also established and developed counties. Solid waste management is an important and stimulating problem for environments across the entire world. Therefore, there is the need to develop an effective technique that will remove these problems, or at least decreases them to a minimal level. This study develops a modified cuttlefish swarm optimization with machine learning-based solid waste management (MCSOML-SWM) in smart cities. The MCSOML-SWM technique aims to recognize different categories of solid wastes and enable smart waste management. In the MCSOML-SWM model, a single shot detector (SSD) model allows effectual recognition of objects. Then, a deep convolutional neural network-based MixNet model was exploited to produce feature vectors. Since trial-and-error hyperparameter tuning is a tedious process, the MCSO algorithm was applied for automated hyperparameter tuning. For accurate waste classification, the MCSOML-SWM technique applies support vector machine (SVM) in this study. A comprehensive set of simulations demonstrate the improved classification performance of the MCSOML-SWM model with maximum accuracy of 99.34%.

Factors affecting the behaviour of domestic solid waste reduction in households:A case study in Dien Chau, Nghe An

This research aims to analyse the factors affecting the behaviour of domestic solid waste reduction in households in Dien Chau town (Nghe An province) by using the Probit regression model. The data was collected from 60 households. The results showed that the domestic solid waste in the study area is mainly from households and organic solid waste. Besides, there were solid wastes, that are paper, scrap, metal, and plastic, could be recycled and reused. Results of the surveyed households showed that there were many activities to reduce solid waste, such as waste classification at source, recycling and reuse, covert organic rubbish into compost or animal feed, etc. From the Probit model, the factors affecting the behaviour of domestic solid waste reduction at households in Dien Chau town included age, gender, and awareness of households on domestic solid waste reduction. These are affecting factors that can be used as a reference to strengthen the behaviour of reducing domestic solid waste in the study area in the coming time.

Research on "Internet Standardized" Garbage Sorting Market Operation Method--Taking University Garbage Sorting as an Example

With the advent of the Internet era and the improvement of people's living standards, more and more garbage is generated per capita. In order to explore the market-oriented operation of garbage sorting in China, this paper takes universities as an example and creates the "Internet+standardized" university garbage sorting market-oriented operation alliance to test the effect of garbage recycling projects in universities. In the initial stage of the project, the unique conditions such as strong environmental awareness, huge amount of domestic waste, high recyclable rate and regular work and rest are the inherent advantages for promoting waste classification reform. In the implementation process, this paper will establish an integrated platform benefiting schools, students, merchants and recycling enterprises, form a market-based mechanism with a multi-body linkage for resource recovery and reuse, raise the environmental awareness of college students, and explore a suitable path for waste separation to be implemented in China.

Recycling Collagen-Containing Waste from the Leather Industry

A method of recycling collagen–containing waste – kamus (skin from the lower leg of animals), formed when separating the skin from the carcass of an animal (horse, cattle), based on its secondary use for sewing fur winter shoes by processing in a new way, is considered. It is concluded that developed environmentally friendly technology for the recycling of collagen-containing waste to transfer the skin from horse skins from the category of solid waste to the category of fur raw materials, to increase the forming properties of the skin by improving the physical and mechanical properties of the leather tissue, exclude wastewater from tanning and fatliquoring, containing toxic chromium compounds.

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixel-wise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

Material Flow and Waste Recovery in a Community-Based Waste Management System in Batu, Indonesia

Indonesian government has promoted to establish Community Based Waste Management (CBWM) in Indonesia to increase the resource recovery rate and to reduce the amount of landfilling waste in stead of conventional the waste management. In CBWM, recyclable materials are recovered by neighborhood communities and only the residual waste is landfilled. Although CBWM can contribute to the reduction of green house gas emission and conservation of resources, the effectiveness of CBWM on the reduction of waste is an clear due to a lack of detailed information about CBWM such as economic benefit, waste flow and its sustainability. The aims of this study are to identify the waste flow in common CBWM in Indonesia and to evaluate the effectiveness of CBWM for reduction of MSW carried to a landfill site. The TPS3R Dadaprejo Mandiri (DM-TPS) in Dadaprejo Village, Batu City, Indonesia was investigated as a case of CBWM. Waste composition was surveyed for 8 consecutive days on three separate occasions in 2021 and 2022. Each resource was weighed and measured at each step of the process to analyze waste flow. Monthly data summarizing the amount of each resource separated at DM-TPS was obtained from their records. Information about the amount of waste and resources collected by the 13 waste banks was obtained by interviewing the administrators of those waste banks. The amount of waste carried to DM-TPS during the first, second, and third surveys averaged 5.61, 6.65, and 5.40 t/day, respectively. Of the 9 categories of waste, the largest was the organic waste. Fruits and vegetables made up the largest percentage of resources followed by plastic bags. The average resource recovery rate was approximately 15.2%. However, the composition survey revealed that the potential resource recovery rate was approximately 39.5%. In order to increase of resource recovery rate, the corporation of households on simple source separation at home would be required.

Mobile App as Digitalisation of Waste Sorting Management

The problem of waste in urban areas that are not managed properly has created a threat to the environment and human life. And this problem is the co-responsibility of the community to get involved in waste management and waste recycling. It is necessary to sort the waste starting at home with recycling because the amount of waste going to the landfill can be reduced. Another benefit of sorting waste is reducing the risk of contaminating the environment. The 3R concept, that is, reuse, reduce, and recycle, has become a government regulation to improve the environmental and waste problems in Indonesia. The focus of this research is on households, beginning with the selection of household waste. Waste sorting is divided into 4 categories, namely organic waste, non-organic waste, B3 or e-waste, and sanitary waste. Using mobile app technology as a solution to encourage individual households, especially housewives, to participate in household waste sorting, the ‘Pilahin’ prototype app was introduced. The selection of media apps on smartphones is because the app has been widely used by urban communities. The app is packed with features that help users scan and detect trash and provide trash categories to identify and sort, as well as the option to find nearby trash banks. The app ‘Pilahin’ has over 40 design screens that have been turned into interactive prototypes and tested on 4 target users with satisfactory results. This application has the potential for development by working with organizations that are already working on waste classification.