Waste Management Model Research Articles

A Sustainable Pattern of Waste Management and Energy Efficiency in Smart Homes Using the Internet of Things (IoT)

Nowadays, environmental protection involves many issues and problems, among which the waste generated by various human activities makes up a significant share, which is becoming newer day by day. Moreover, the production of normal, industrial, special, hospital, and agricultural waste and improper management of these materials has created many health, safety, and environmental problems. Based on this approach, this research study aims to determine the model of waste management and energy efficiency in smart homes using the Internet of Things (IoT). The research method used by this study is estimative-computational. For this purpose, the required data were collected using a computational approach. For this purpose, the required views and data were collected through experts in this field and calculated in MATLAB and STATA software. The data analysis tool was represented by fuzzy calculations and for this purpose MATLAB software was used. The study revealed that energy costs in smart homes using the IoT technology are impressive. The number of home residents in smart homes using the IoT is impressive. Home area in smart homes using the innovative technology of IoT is also impressive.

Trialing Household Waste Segregation in Island Communities of the Maldives

Abstract Waste management in the small island communities of the Maldives is challenging due to the fragility of the low-lying islands and the logistical complexities of a geographically dispersed setting. The Soneva Namoona team is developing a holistic sustainable waste management model that is suited to the context of such islands. In this article, we present the findings of household-level waste segregation trials implemented in four islands in Baa Atoll using the Trials of Improved Practices (TIPs) research method. Thirty-eight of 45 households successfully completed the trial, and we observed participants’ high willingness to segregate. The main challenges included difficulty in communicating and coordinating new segregation behaviors with household members and the lack of space in households for keeping separate containers. We also observed that coordination with the Island Waste and Resource Management Centres (IWRMCs) was vital for successful household segregation.

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.

Anyelir Waste Bank Program at Medan City on the Aplication in Circular Economy Techniques

The circular economy approach has begun to be applied as a model for waste management in Indonesia. This approach is expected to improve environmental quality and increase economic value. The Anyelir Medan waste bank has implemented the application of circular economics in waste management with the concept of saving waste to gold. This study aims to analyze the operational activities of the carnation waste bank in realizing the circular economic model of waste management. The research method is descriptive research with observational research design. Data is processed using mass balance analysis. Through the Anyelir waste bank, savings can be collected into gold savings under the guidance of PT Pegadaian Persero. Waste managed at the Anyelir waste bank is 1640.94 kg per month with an average of 828.42 kg (50.48%) of plastic waste, 704.58 kg of paper (42.94%) and 107.92 metals (6.58%) . The results of the mass balance analysis show that the total reduction efficiency is 97.2% of the total waste that goes into bank waste. As much as 1450.97 kg (88.42%) of the waste was sold to recycling factories, 41 kg (2.5%) were used as recycled crafts and 103 kg (6.28%) were stored in stock. The rest goes to TPA as much as 45.95 kg (2.8%). The Anyelir waste bank does not only focus on economic value, but also tries to increase awareness of the environment, through education to improve the social life of the community and strengthen the economic pillars. The circular economy movement carried out by the Anyelir waste bank has produced processed waste into compost, recycled creations and raw materials for paper and plastic factories.

Multi-objective two-stage stochastic optimization model for post-disaster waste management

Abstract Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities in returning to a stable state following a disaster. To develop an efficient post-disaster waste management strategy, this study presents a multi-objective two-stage stochastic mixed integer linear programming model for post-disaster waste management. The proposed mathematical model was developed based on a mixed strategy of on-site and off-site waste separation in the supply chain. This study aims to minimize not only the total cost and the environmental impact to provide waste flow decisions and choose collection and separation sites, recycling sites, landfill sites, and incineration sites throughout the supply chain under the uncertain situation. To solve a multi-objective problem, a normalized weighted sum method is used to find the solution. A numerical case based on realistic data is presented to validate and verify the proposed model. Based on the numerical example, the results demonstrated that the implementation of the mixed strategy for waste separation with the consideration of uncertain situations can reduce the total cost, balance the environmental impact, and determine the unexpected situation in the post-disaster waste supply chain efficiently.

A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research.

Dynamic System Modeling and Sustainability Strategies for Circular Economy-Based Dairy Cow Waste Management

Dairy farming activities produce waste that can have a negative impact on the environment. The processing of livestock waste will reduce pollution and support environmental conservation programs that are currently being intensified by the West Java Provincial Government, Indonesia. An economic approach that can minimize waste and maximize resources is the circular economy (CE) approach. CE can occur at the macro-, meso-, or microlevels. The application of micro-scale CE can be carried out in a business activity. One of the micro business groups that have implemented the CE concept in Bandung Regency, West Java Province, Indonesia, is the Taruna Mukti Farmer Group, which is located in the Citarum River sub-basin area. From 2008 to 2022, this group processed 49,580 tons of cow dung into organic fertilizer. However, there has been a decline in livestock waste processing, where production reduced from 3700 tons in 2018 to 1280 tons in 2021, then to only 500 tons in 2022. Therefore, this study aims to design a sustainable strategy for livestock waste management based on CE in the Taruna Mukti Farmer Group. The method used is in-depth interviews with the Drivers, Pressure, State, Impact, Response (DPSIR) approach. In addition, this study will also describe the CE model in the management of dairy cattle waste. Key Informants come from the Farmer Group and related stakeholders. Based on the results of the study, it is known that the dairy cattle waste management model in the Taruna Mukti Farmer Group is a form of implementing the CE concept with a circular flow of material and involving various stakeholders and an extensive marketing network. With the current conditions, the simulation results of processing livestock waste into organic fertilizer show a decline/unsustainability. To support the sustainability of livestock waste management, a response is needed in the form of strengthening the internal management of the Farmer Group’s organization, regular assistance and supervision from the local government and non-government organizations, and guidance regarding the marketing of organic fertilizers and ease of access to markets.

Artificial Intelligence with Earthworm Optimization Assisted Waste Management System for Smart Cities

<p>In recent days, massive quantity of waste materials gets considerable increased with increasing population. Proper management of waste materials becomes essential to reduce environmental degradation and improve quality of life in smart cities. Waste management helps to collect and treat waste materials from society. Smart waste management is a new frontier for local authorities assisting in reducing municipal solid waste and improving community recycling rate. Appropriate classification of waste objects necessitates the design of automated waste classification models based on artificial intelligence (AI) and computer vision (CV) based approaches. With this motivation, in this study, an automated artificial intelligence with earth worm optimization assisted waste management and classification (AIEWO-WMC) model is proposed for smart city environment. The proposed technique intends to recognize and categorize waste objects using the DL techniques. The proposed model primarily derives a RetinaNet based object detection module to identify the existence of waste objects in the images. To improve the classification performance, Adagrad optimizer is applied. Moreover, earthworm optimization with stacked autoencoder (SAE) algorithm is applied for the classification of waste objects. To assuring the improvised results of the AIEWO-WMC technique, comprehensive experimentation is performed on standard dataset and the obtained values indicated the supremacy of AIEWO-WMC model over the other techniques with increased accuracy of 99.15%.</p>

Research on the cooperative network game model of marine plastic waste management

Marine plastic waste pollution damages the stability of the marine ecosystem and inhibits the sustainable development of the "blue economy", which has aroused widespread concern worldwide. Nowadays, cooperation on marine plastic waste management is an urgent research topic. A global consensus on management cooperation is emerging, but the economic feasibility of cooperation has not yet to be proven. This paper takes the amount of capital investment, technology level of governance and the amount of marine plastic waste to be treated as variables affecting the cooperative income to construct a cooperative network game model for marine plastic waste management from the perspective of economics. The paper distributes benefits based on the "Myerson value", analyzes the equilibrium conditions of the model and tests the stability of cooperation. In addition, numerical analysis is carried out using actual data from key countries to demonstrate the practical economic feasibility of cooperation in marine plastic waste management. The findings include: (1) The technology level of governance and the amount of marine plastic waste to be treated have a negative impact on the country's choice of cooperative governance strategies and the stability of cooperative alliance, while the amount of capital investment is conductive to it. (2) The size of the alliance has an impact on country's strategic choices and the stability of the alliances. Too small an alliance is not conducive to cooperative alliance building, which gradually becomes more likely as the size of the alliance increases, but it is uncertain the effect of oversized alliance and what size is most appropriate. (3) Cooperation in marine plastic waste management is economically feasible at both the theoretical and practical levels. (4) Encouraging technological innovation to improve the governance level, implementing extended producer responsibility measures to shift the management cost, exerting the positive influence of key countries to promote the stability of the alliance, and establishing a reasonable interest adjustment mechanism to coordinate the interests of all parties are helpful to build a stable and efficient cooperation alliance and improve the economic feasibility of marine plastic waste management cooperation. This paper not only provides theoretical support for the global cooperation of marine plastic waste management, but also proves the feasibility of practice and points out the direction for its practice.

Modeling photovoltaics’ waste projection and waste management optimization

The world is moving towards renewable energies as an alternative to fossil fuels. The photovoltaic technologies have the highest capacity increase rate among all the renewable energy systems throughout the last decade. Therefore, the huge amounts of photovoltaic wastes from such significant global installations are inevitable and needs to be dealt with sooner than later to increase the potential of PVs being an affordable and clean energy source. This work is split into two parts with different models. The first one mathematically models the PV waste projection using the Weibull distribution and socio-environmental decision-making factors while connecting them to the power purchase agreements (PPAs). This model uses data from both c-Si and CdTe PV projects in the United Arab Emirates (UAE). This model's results showed the possibility of increasing the PV projects operational age in UAE to 30 years for both technologies with 2030 being the best and latest year to start PV waste management procedures with the profitable reusability of PVs after projects' decommissioning. The second part is an optimization model for the PV waste and installations management by controlling the total future PV installations and possible PV replacements, while considering budget limitations and dismantling capacities. With a planning horizon until the year of 2100, this model's results suggest annually installing 670 MW of new PVs to achieve 50% of the country's consumption by 2030 and 100% by 2040 without any power shortage or outage. Finally, the models are flexible and extremely beneficial for future works for improvements, results comparison, and to design PV waste management centers or dismantling facilities.

Doctoral dissertation summary: A model of waste management in island tourist destinations

Doctoral dissertation summary: A model of waste management in island tourist destinations

The legal institutional model of community-based waste management to reinforce multi-stakeholder collaboration in Indonesia

The legal institutional model of community-based waste management to reinforce multi-stakeholder collaboration in Indonesia

Development of an Integrated Model for Effective Waste Management (A Case Study of The Federal Polytechnic, Ado-Ekiti)

Development of an Integrated Model for Effective Waste Management (A Case Study of The Federal Polytechnic, Ado-Ekiti)

Piranha Model Solid Waste Management - A Scalable Decentralised Waste Management Solution from Kerala

Solid Waste Management is a wide range of Problematic Concern all over the world. Unwarranted or open dumping of waste causes serious effects in Urban Atmosphere, health hazards to public and thus leads to Environmental Pollution. Citizens in Trivandrum city is facing such problems nowadays, due to the open dumping of solid waste. Based on this Aspects Piranha model of Waste Management took initial steps to avoid the crucial crisis faced by solid waste in Trivandrum City which includes the collection, transportation, segregation and processing of bio and non bio waste. This study discloses a correct mechanism within the city to treat solid waste generated and ends up in the proper development.

Benchmarking of sustainable waste management models

The paper examines the potential of black soldier fly larvae (BSFL) recycling in sustainable and economically viable organic waste treatment. Recent studies demonstrate effective biowaste conversion and nutrient recycling abilities of BSFL. However, comparisons with other waste treatment methods require further investigation in terms of quantification of environmental and economic efficiency parameters. The aim of this study is to analyze the performance and viability of the BSFL model against alternative organic waste processing approaches. The research employs a case study methodology using operating data from a commercialscale BSFL recycling facility in Central Europe specializing in food industry residuals. The results show a 22% higher bioconversion rate compared to regular composting translated into 170 kg of product output per ton of organic substrate. BSFL processing leads to lowered greenhouse gas emissions from waste treatment by an average of 35% owing to significant biogenic carbon sequestration. The reduced environmental impacts coupled with marketable BSFL-derived products create the prospects for sustainable waste management. The study concludes that incentivizing businesses in the waste management sector to integrate BSFL-centered approaches leads to achieving dual economic and environmental sustainability goals. The paper contributes to knowledge on the viability of insect-based waste recycling. Further research needs include lifecycle assessments of the BSFL value chain and evaluating scaling potential.

Retracted: Machine Learning and IoT-Based Waste Management Model.

[This retracts the article DOI: 10.1155/2021/5942574.].

Assessment Of Solid Waste Management Practices in Abuja Municipal Area Council (Amac)

This study assesses the solid waste management practices and the role of agencies responsible for its management in Abuja Municipal Area Council (AMAC). A total of 400 structured questionnaires distributed randomly were used to obtain information from household heads and Informal waste collectors operating AMAC. Oral interviews with the staff of Abuja Environmental Protection Board (AEPB) were also undertaken. Simple descriptive statistics analysis was used in the presentation of the data obtained. It is evident from the study that socio-economic characteristics such as educational levels, monthly income, family size and etc. of the households in the study area greatly influence the quantity of waste generation. The study revealed that 69% and 31% of the waste generated in AMAC are biodegradable and Non-biodegradable respectively with kitchen waste alone constituting 46%. The average waste generated is 0.65Kg/capital/day. The findings also showed that waste collection within the study are done mostly ones in a week (46%) sometimes the waste spent more than a week (17%) without being attended to which pose serious health issue. It is observed from our findings that the informal waste collectors are only interested in the recyclable material, this is not far-fetch from the fact that after sorting is done on the waste collected, 10% are thrown into drainages, 24% to open space, 55% left at the separation point. Only 11% got to public collection points. The outcome of the interview on staff of AEPB revealed that the agency is only responsible for the collection, transportation and disposal of the waste to the government approved sites leaving out waste treatment. The poor performance of this agency is closely linked to inadequate machineries/equipment, trained personnel, poor funding and negative attitude of the households towards solid waste management. The study finally recommends community participation, payment for waste disposal, increase public-private partnership, adequate resourcing of waste management agency, monitoring and supervision, use of integrated solid waste management model and the application of appropriate technologies among others.

An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learning (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes. The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns (DLBP), EfficientNet, and DenseNet121. In addition, the flower pollination algorithm (FPA) based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models. Moreover, a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine (SVM), extreme learning machine (ELM), and gradient boosting tree (GBT). The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

Construction of models for managing military waste generated under the conditions of war

The object of this study is military waste management models as a system of actions and processes aimed at choosing how to handle it. The task to devise ways to reduce military waste under conditions of increasing volumes and rates of its accumulation is considered. The policy of military waste management in the countries of hostilities was investigated; it was concluded that there are no effective mechanisms and schemes for managing such waste. Trends in the increase in the amount of military waste have been identified and a forecast of their further growth under the conditions of modern wars has been formed. The current legislative support for the implementation of waste management policies formed in the process of physical and moral deterioration of military equipment and ammunition in military units was analyzed. It was determined that the main subject of management of such waste is the state represented by state authorities and management. Models to manage military waste formed in the process of hostilities have been proposed. These military waste management models are based on the criteria of economic, social, and environmental efficiency. Special features of the research results are that the proposed models provide for the transfer of rights to perform certain stages in the waste management chain by subjects of the non-state sector of the economy. Distinctive features of the reported results are that they present alternative models for managing military waste formed in the process of warfare. The choice of model is determined by the priorities of the defense and policy of post-war reconstruction of the country's economy. The area of practical use of the results is the system of public administration bodies, which forms proposals for changes to the legislation in terms of forms of cooperation between the state and non-state sectors in the field of military waste management

Implementasi Circular Economy pada Rumah Inovasi dan Daur Ulang Bank Sampah Nusantara Pondok Pesantren Al Ihya Ulumaddin Kesugihan Cilacap

This paper describes the implementation of the circular economy at the Innovation and Recycling House of the Nusantara Garbage Bank at the Al Ihya Ulumaddin Kesugihan Islamic Boarding School in Cilacap which was carried out by students of the Al Ihya Ulumaddin Kesugihan Islamic Boarding School at Cilacap. This paper uses a qualitative method with a field research approach. This paper emphasizes 5 models of waste management, namely (Reduce, Reuse, Recycle, Replace, Repair) which are implemented in a circular economy system.