Search Results (35)

With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value. Full article

The construction industry accounts for approximately 28% of global CO₂ emissions, and emission management at the building demolition stage is important for achieving carbon neutrality goals. Systematic studies on the demolition stage, however, are still lacking. In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. CO₂ emissions were predicted by applying various ML algorithms (e.g., gradient boosting machine [GBM], decision tree, and random forest), based on the information on building features and the equipment used for demolition, as well as energy consumption data. GBM was selected as a model with optimal prediction performance. It exhibited very high accuracy with R² values of 0.997, 0.983, and 0.984 for the training, test, and validation sets, respectively. The GBM model also showed excellent results in generalization performance, and it effectively learned the data patterns without overfitting in residual analysis and mean absolute error (MAE) evaluation. It was also found that features such as the floor area, equipment, wall type, and structure significantly affect CO₂ emissions at the building demolition stage and that equipment and the floor area are key factors. The model developed in this study can be used to support decision-making at the initial design stage, evaluate sustainability, and establish carbon reduction strategies. It enables efficient data collection and processing and provides scalability for various analytical approaches compared to the existing life cycle assessment (LCA) approach. In the future, it is deemed necessary to develop ML tools that enable comprehensive assessment of the building life cycle through system boundary expansion. Full article

Waste management involves the systematic collection, transportation, processing, and treatment of waste materials generated by human activities. It entails a variety of strategies and technologies to diminish environmental impacts, protect public health, and conserve resources. Consequently, providing an effective and comprehensive optimization approach plays a critical role in minimizing waste generation, maximizing recycling and reuse, and safely disposing of waste. This work develops a novel Possibilistic Multi-Objective Mixed-Integer Linear Programming (PMOMILP) model in order to formulate the problem and design a circular–sustainable–reliable waste management network, under uncertainty. The possibility of recycling and recovery are considered across incineration and disposal processes to address the main circular-economy principles. The objectives are to address sustainable development throughout minimizing the total cost, minimizing the environmental impact, and maximizing the reliability of the Waste Management System (WMS). The Lp-metric technique is then implemented into the model to tackle the multi-objectiveness. Several benchmarks are adapted from the literature in order to validate the efficacy of the proposed methodology, and are treated by CPLEX solver/GAMS software in less than 174.70 s, on average. Moreover, a set of sensitivity analyses is performed to appraise different scenarios and explore utilitarian managerial implications and decision aids. It is demonstrated that the configured WMS network is highly sensitive to the specific time period wherein the WMS does not fail. Full article

The recycling of materials is in line with the policy of a closed-loop economy and is currently an option for managing waste in order to reuse it to create new products. To this end, 3D printing is being used to produce materials not only from pure polymers but also from their composites. Further development in this field seems interesting and necessary, and the use of recycled materials will help to reduce waste and energy consumption. This article deals with the use of degradable waste materials for the production of insulating materials by 3D printing. For the study, samples with different numbers of layers (one and five), composite thickness (20, 40, 60, 80, and 100 mm) and composition (including colored resins that were transparent, black, gray, and metallized, as well as resins that were colored gray using soybean oil and gray using natural fibers) were made. The role of natural fillers was played by glycerin and biomass ash with a weight ratio of 5%. The finished materials were tested, and the values of the coefficient of thermal resistance and heat transfer were determined. The best thermal properties among the tested materials were distinguished by a five-layer sample made of soybean-oil-based resin with a thickness of 100 mm. This sample’s heat transfer coefficient was: 0.16 W/m²K. As a material for thermal insulation in 3D printing technology, biodegradable components have great potential. Full article

A suitable waste-management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and it requires precise data on the volume of demolition waste (DW) generated. Therefore, we developed an optimal machine learning model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. We constructed a dataset comprising information on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, landfill mix waste, and recyclable minerals). We applied an artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest, and support vector regression. Further, we derived the optimal model through data preprocessing, input variable selection, and hyperparameter tuning. In both the validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies (R²) of 0.987 and 0.972, respectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies (R²) of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. This study confirmed through Shapley Additive exPlanations analysis that the floor area is the most important input variable in the four models (i.e., recyclable mineral waste, recyclable combustible waste, recyclable metals, and landfill mix waste). Additionally, the type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill waste generated. The results of this study can provide more detailed information on the generation of recycling and landfill waste. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals. Full article

Today, with the increase in population, technological developments, industrialization and urbanization, problems related to waste management (WM) have become increasingly important to a sustainable and global clean environment. The gradual deterioration of the quality of environmental elements and the increase in environmental problems have caused societies to focus more on environmental problems. Waste management is a form of management that includes the prevention, non-prevention, reuse, recovery, and disposal of domestic, medical, hazardous, and non-hazardous wastes. This study aims to prioritize critical success factors (CSFs), via the Analytical Hierarchy Process (AHP), in industrial waste management and to determine the most important critical success factor. The four main criteria and 23 sub-criteria were scored by the AHP method according to the opinions of five environmental engineers. After determining critical success factors, survey questions were prepared to make employees rank these factors. While the “national/local waste management strategies and policies” factor was the most important critical success factor according to environmental engineers, the most important critical success factor for employees was “enterprise waste management strategies and policies”. In addition, differences in the priorities of CSFs were found in the opinions of employees in different sectors. Full article

The construction industry in Australia generates a significant amount of construction and demolition (C&D) waste, necessitating better waste management (WM) practices. This research addresses this issue by investigating CE strategies aimed at minimising C&D waste in Australian construction projects (CPs). Utilising a qualitative approach, the study is based on 20 interviews and four case studies of commercial CPs, analysed through NVivo content analysis. The findings emphasise the need to integrate CE strategies at every CP stage. In the pre-design phase, setting sustainable objectives and engaging stakeholders early is crucial for aligning goals to reduce C&D waste. The tendering process benefits from incorporating WM into contracts, demonstrating early commitment to sustainability. The design phase, through Building Information Modelling and designing for disassembly, offers substantial waste-reduction opportunities. Modular and prefabricated components during the construction phase enhance material reuse and recycling. Operational strategies like regular maintenance and retrofitting extend material lifespan, while selective demolition and digital cataloguing at the end-of-life phase enable efficient material recovery. This highlights the essential roles of policy, technology, and stakeholder collaboration in advancing CE practices, providing practical insights for construction professionals and policymakers to implement CE-related strategies in CPs. The research concludes that adopting CE strategies can lead to significant reductions in C&D waste and improved sustainability in the construction sector. Full article

This study reports on the application of an innovative plastering system that reuses organic waste, namely spent coffee grounds (SCG), to improve energy efficiency in historical buildings according to the European Green Deal. The case study was conducted in the village of Polizzi Generosa, selected from 21 small villages located in the extensive UNESCO Geopark of Madonie Park in Sicily. Over time, traditional plasters used in Madonie buildings have shown durability issues due to thermal and hygrometric stresses caused by significant temperature fluctuations in the area. Moreover, much of the considered architectural heritage lacks energy efficiency. Given the global increase in coffee production and the need for more sustainable waste management systems, this investigation proposes an ecological method to reuse SCG in plaster formulation, thereby enhancing the circular economy. To achieve this, many thermoplaster formulations were developed, and the best-performing one, considering both material and aesthetic compatibility with historical buildings, was selected for a real-world application. Additionally, virtual modeling and energy simulations were conducted to test the energy performance of a traditional building in Polizzi Generosa using SCG-based thermoplaster in comparison to traditional lime mortar and commercial alternatives. The real-world application demonstrated the technical feasibility of the process, and the energy simulations showed an improved building masonry energy performance of 0.788 W/m²K and an 11% improvement compared to traditional plaster. Results clearly indicate that SCG can be successfully reused to produce eco-friendly bio composite plasters, providing a more sustainable housing option. This approach offers a durable and cost-effective alternative for housing solutions that meet regulatory requirements for energy efficiency, serving as a smart, highly sustainable, and long-lasting choice for the construction sector. Finally, this result supports the research goal of transforming the 21 municipalities of Madonie into smart and green villages, with the “Smart Coffee-House” exemplifying intelligent rehabilitation processes of existing heritage buildings. Full article

The building and construction industry consumes a significant amount of natural resources alongside contributing to the generation of waste materials. Addressing the dual challenge of waste management and recycling in this sector is imperative. This study begins with a bibliometric assessment to identify waste materials used as insulation in building and construction systems. The assessment of 2627 publications revealed mattress waste materials were seldom considered. The aim of this research focuses on exploring alternative methods for repurposing mattress materials in construction, aiming to mitigate waste generation. While various materials are being recycled for building applications, this research emphasises the potential of incorporating recycled polyurethane foam (PUF) from mattresses as insulation products. A transient plane source (TPS) was employed to determine the thermal conductivity of waste mattress PUF obtained from a recycling plant in Victoria, Australia. The results exhibited promising thermal resistance, with a mean value of 0.053 Wm/K. However, optimal thermal performance was observed with increased thickness, suggesting that a thickness of 215mm aligns with industry standards for building fabric systems. Further research is required to comprehensively analyse moisture resistance and fire retardation of waste mattress materials. This paper presents key findings of current trends, limitations, and future research directions to the use of waste mattress PUF as an insulation material. Full article

One of the largest issues facing countries, particularly emerging nations with high population, production, and consumption growth, is an inadequate waste management system (WMS). This paper analyzes the development of the waste management systems of nine capital cities in the Association of Southeast Asian Nations (ASEAN) region by using a recently developed approach, the Waste Management System–Development Stage Concept. This concept comprises five development stages and various components, including Collection and Transport, Waste Disposal, Energy Recovery, Waste Recycling, and Waste Prevention and Reuse. The findings indicate that in terms of waste collection, waste disposal, and energy recovery, Singapore is at a higher development stage (Stage 5) and is more advanced than other ASEAN cities. For most of the components, Bangkok, Jakarta, Kuala Lumpur, and Manila fall into stages 2 to 4, whereas the early development stages 1 to 3 are present in Bandar Seri Begawan, Hanoi, Phnom Penh, and Vientiane. The results will be used to determine the next steps in developing the WMSs, including the introduction of separate collection for recycling or the installation of a waste-to-energy plant. The environmental impact of each measure will be later assessed using the LCA approach, and the most effective measures shall be identified in future studies. Full article

The life cycle assessment (LCA) serves as a crucial tool for assessing the environmental impact of products, with recent emphasis on polyethylene terephthalate (PET) bottles. Our meta-analytical review of 14 LCA research papers (2010–2022) on PET bottles, aligned with PRISMA guidelines, spans six phases: raw material production (MP), bottle production (BP), distribution and transportation (DT), collection and transport (CT), waste management (WM), and environmental benefits (EB). Utilizing the global warming potential (GWP) as the indicator, our study harmonized data into a consistent functional unit, revealing an average emission of 5.1 kg CO₂ equivalent per 1 kg of PET bottles. Major contributors to global warming were identified across the MP, BP, and DT phases. While the MP and BP phases exhibited low variability due to uniform processes, the CT, WM, and EB phases displayed higher variability due to scenario considerations. A comparison with Korean environmental product declaration data affirmed the methodology’s practical utility. Our approach offers potential applicability in diverse product category assessments, emphasizing its relevance for informed decision-making in sustainable product development. Full article

In order to effectively control high temperatures inside coal gangue hills, gravity heat pipes with specific spacings are vertically installed in coal gangue hills. Heat extracted from these heat pipes can be utilized for power generation through energy conversion. In this study, an equivalent model of gravity heat pipes in coal gangue hills was established and, in a laboratory setting, experimental research and optimization were conducted on power generation per unit area using the temperature difference of gravity heat pipes for electricity generation. To facilitate real-time testing of different heat pipe parameters and to display the experimental results, a multi-parameter measurement system was designed and constructed. This study systematically investigated the effects of various structural parameters such as inclination angle, heating temperature, initial absolute pressure, and working fluid height. Through single-factor experiments, it was determined that the inclination angle had no significant impact. The range of values for heating temperature, initial absolute pressure, and working fluid height were confirmed based on six sets of experiments. To maximize the performance of the thermoelectric generator, a response surface analysis experiment was conducted using the Design-Expert software. The optimal conditions were determined to be a working fluid height of 200.001 mm, an initial absolute pressure of 0.002 MPa, and a heating temperature of 413.15 K. Under these conditions, the power generation per unit area of the thermoelectric generator reached 0.122981 W/(m²·K). The accuracy of the theoretical experiments was verified through on-site industrial experiments. By calculations, it was determined that the maximum temperature difference power generation capacity per gravity heat pipe was 42.39 W. This provides a new solution for the management of coal mine gangue hills and the secondary utilization of waste energy. Full article

The construction industry has one of the highest waste intensities in Australia. While there are barriers to the implementation of sustainable waste management (WM) practices, there is a lack of viable solutions for head contractors to overcome these barriers. This research investigates the role of incentives in achieving sustainable WM in the Australian commercial construction industry. A qualitative approach was adopted through interviews with experts in the field to explore the role of incentives as possible solutions to the barriers presented. The findings show that participants are willing to use more sustainable WM practices. However, the barriers are perceived to be too substantial. Many types of incentives can encourage changes in behavior, which contribute to better waste outcomes. The findings also indicate key stakeholders such as the client, government, and industry regulators may provide incentives, including enhancing relevant key performance indicators, amending existing legislations, and implementing government programs to foster a Circular Economy to improve sustainable WM practices. This study contributes to the field by raising awareness about the role of incentives for head contractors to achieve sustainable WM practices. Full article

Background: The interest in the topic of performance assessment in reverse supply chains (RSC) is increasing, although the body of research is still in its early stages. As this is a developing field, it is crucial to expand discussions on topics that have not yet been thoroughly examined, such as the intrinsic bias of indicators and metrics that may be associated with specific operational, economic, environmental perspectives, etc. Such perspectives should be considered in the decision-making process within the context of reverse logistics (RL) and waste management (WM). The aim of this research was to identify different perspectives employed in the development of proposed models in the literature. Methods: A systematic literature review was conducted to analyze thirty papers from Scopus, Web of Science, and Science Direct databases without time restrictions. Results: The review identified various ways in which authors grouped perspectives, including qualitative and quantitative, sustainability, and operational perspectives, among others. Conclusions: This study revealed several gaps in the field, including limited studies on RSC performance assessment and a lack of studies linking performance assessment to decision-making components. Full article

Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 (mAP@0.5) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 (mAP@0.5:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of mAP@0.5, mAP@0.5:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management. Full article