Search Results (24)

To address severe ammonia gas and dust pollution coupled with resource waste in exhaust gases from urea prilling towers, a production process for gasified biochar-based slow-release fertilizer is proposed to achieve resource recovery of exhaust pollutants. Through phosphoric acid impregnation modification applied to gasified biochar, its ammonia gas adsorption capacity was significantly enhanced, with saturated adsorption capacity increasing from 0.61 mg/g (unmodified) to 32 mg/g. Coupled with the tower-top bag filter, the modified biochar combines with ammonia gas and urea dust in exhaust gases, subsequently forming biochar-based slow-release fertilizer through dehydration and granulation processes. Material balance analysis demonstrates that a single 400,000-ton/year urea prilling tower achieves a daily fertilizer production capacity of 55 tons, with 18% active ingredient content. The nitrogen content can be upgraded to national standards through urea supplementation. Economic analysis demonstrates a total capital investment of USD1.2 million, with an annual net profit of USD0.88 million and a static payback period of 1.36 years. This process not only achieves ammonia gas emission reduction but also converts waste biochar into high-value fertilizer. It displays dual advantages of environmental benefits and economic feasibility and provides an innovative solution for resource utilization of the exhaust gases from the urea prilling process. Full article

The global plastic pollution control process has put forward higher requirements for waste plastic reduction and recycling. This study evaluated the plastic demands by 2030 and 2050 in China based on a combination of qualitative and quantitative methods, identified the four consumption terminals, and put forward countermeasures for the sustainable development of the plastics industry. The results show that based on the analysis of China’s low-carbon transition and global plastic pollution control policies, the reasonable demands for plastic will reach 118 and 110 million tons by 2030 and 2050, respectively. The packaging, construction and decoration, electronics and appliance, and automobile areas are the four major terminals of plastic consumption in China, accounting for more than 80% of the total plastic consumption. The enhanced implementation of the policy of banning and restricting plastic bags will lead to a significant drop in the consumption of disposable packaging plastics, while the low-carbon transformation of the whole society will promote the realization of low-energy consumption in the field of construction, the automobile industry toward lightweight materials, and electronics and appliance products toward high quality, thus further stimulating the related plastics demand. Finally, countermeasures for the sustainable development of plastic are proposed. Full article

To address the inadequacies of mechanized potato-harvesting equipment on challenging terrains like hills, mountains, and small fields, a lightweight and simple self-propelled crawler potato combine harvester was developed based on the agronomic and harvesting requirements of potato cultivation. The machine consists of key components including a depth-limited soil-crushing device, an auxiliary feeding device, an excavation device, a rubber rod separation device, and a ton bag sorting device. It offers technical advantages such as a lightweight structure, auxiliary feeding and conveying, and manual assistance in sorting ton bags. The key components, such as the auxiliary feeding device, depth-limiting soil-crushing device, and rubber rod separation device, were analyzed theoretically to determine the relevant structures and parameters. Through initial harvesting performance tests, the separation screen line speed, vibration frequency, and device inclination angle were identified as the experimental factors. Evaluation indicators such as potato bruise rate, skin breakage rate, loss rate, and impurity content were chosen, and a three-factor, three-level Box–Behnken optimization test was conducted. The results indicated that with a separation screen line speed of 1 m/s, vibration frequency of 8 Hz, and device inclination angle of 30°, the potato damage rate during harvesting was 1.318%, the skin breakage rate was 1.825%, the loss rate was 2.815%, and the impurity rate was 2.736%. Field tests with the same parameters showed that the potato damage rate, skin breakage rate, loss rate, and impurity rate of the harvester were 1.357%, 1.853%, 2.86%, and 2.748%, respectively, meeting relevant industry technical standards. This research can serve as a reference for enhancing the harvesting performance of potato combine harvesters and ton bag sorting technology. Full article

In recent years, the environmental impacts of plastic production and consumption have become increasingly significant, particularly due to their petroleum-based origins and the substantial waste management challenges they pose. Currently, global plastic waste production has reached 413.8 million metric tons across 192 countries, contributing notably to greenhouse gas emissions. Bioplastics have emerged as eco-friendly alternatives, with bioplastic carrier bags composed of 20% starch, 10% additives, and 70% polybutylene adipate terephthalate (PBAT) being the focus of this research. This study aimed to evaluate the biodegradation of these bioplastic bags under industrial composting conditions, addressing the gap in the existing literature that often lacks real-world applicability. A large-scale composting experiment was conducted using 37.5 tons of manure/wood and 50 tons of biopolymer bags over 12 weeks. Results showed that compost temperatures peaked at 70 °C and remained above 50 °C, pH levels stabilized at 8.16, and electrical conductivity was recorded at 1251 μs cm⁻¹. Significant changes were observed in key metrics, such as the carbon-to-nitrogen ratio and organic matter content. Disintegration tests revealed that 95% of the bags disintegrated by the 12th week, though ecotoxicity tests indicated varying germination inhibition rates. Advanced analytical methods (Fourier transform infrared spectroscopy, gas chromatography coupled with mass spectrometry) highlighted morphological and chemical transformations in the bags. This research enhances understanding of bioplastic degradation in real-world composting environments and suggests potential improvements to existing standards, promoting sustainable waste management solutions. Full article

Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements on resource-constrained operational equipment—this paper proposes an improved lightweight ton bag detection algorithm, YOLOv8-TB (YOLOv8-Ton Bag), which is optimized based on YOLOv8. Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. On the logistics ton bag operation dataset provided by shipping port enterprises, the improved algorithm improves by 3.7% and 5% compared with the original algorithm in mAP50 and mAP50-95, respectively, the model size is reduced by 4.42 MB and the amount of model computation is only 8 G, which is capable of accurately detecting logistics ton bags in real time. The superiority of the method is verified by comparing it with other classical target detection algorithms. Full article

Plastic production on a commercial scale began in the 1950s, reaching an annual production of 460 million metric tons in 2019. The global release of 22% of produced plastics into the environment has raised concerns about their potential environmental impacts, particularly on aquatic ecosystems. Here, we quantify and categorize plastic debris found along Richland Creek, a small, heavily forested watershed in western North Carolina, USA. Plastics within the riparian zone of seven 50 m reaches of Richland Creek and its tributaries were sampled two or three times. The 1737 pieces of collected plastic debris were returned to the lab where they were measured and categorized. A small-scale laboratory study using seven of the items collected was performed to determine their ability to break down into microplastics (particles < 5 mm in size). The majority (76%) of collected items were made of either plastic film (particularly bags and food wrappers, 43%) or hard plastics (e.g., bottles, 2%). However, when viewed on a surface area basis, films and synthetic fabrics (e.g., clothing, sleeping bags) equally dominated. Roughly three-quarters of the items collected had a width less than 10 cm, due primarily to the fragmentation of the original items; over two-thirds of the collected items were fragmented. Items composed of foams and films exhibited the highest fragmentation rates, 93% and 86%, respectively. Most collected plastics were domestic in nature, and the number of items increased downstream through more developed areas. Laboratory studies showed that plastic debris has a propensity to break down into microplastics. We believe the data collected here should be replicated in other streams, as these freshwater environments are the source of plastics that eventually enter the oceans. Full article

In Colombia alone, 12.6 million bags of green coffee are produced, but at the same time, 784,000 tons of waste biomass are dumped in open fields, of which only 5% is recovered or used, and 10 million tonnes of coffee emit 28.6 million tonnes of CO₂ eq annually. This presents a worrying dilemma, and the need to develop a technology to transform the waste into usable products is increasing. As a response to this, the valorization of coffee waste was explored through the production of biochar and platform chemicals by implementing a set of hydrothermal experiments with different biomass/water ratios (1:5, 1:10, 1:20, 1:40), particle sizes (0.5, 1, 2, 5 mm), stirring rates (5000 and 8000 rpm), and catalysts (H₂SO₄, NaHCO₃ and CH₃COOH) at 180, 220, and 260 °C in a batch reactor with autogenous pressure. Notably, the smaller B:W ratios of 1:20 and 1:40, as well as smaller particle sizes of 0.5 and 1 mm, yielded higher amounts of platform chemicals, while stirring showed minimal influence. CH₃COOH significantly enhanced the process compared to other catalysts. The biochar was characterized as anthracite, and this obtaining of coal-like materials from biomass itself represents a remarkable feat. Said anthracite presented little to no variation in physical parameters, while catalysts induced functionalization. By optimizing factors like B:W ratio, particle size, and catalyst application, valuable insights have been gained into enhancing the yield of platform chemicals and quality of biochar from coffee waste. The findings not only contribute to sustainable waste management practices but also highlight the importance of exploring innovative solutions for utilizing agricultural by-products effectively. Full article

The mechanized harvesting level of potatoes in the arid areas of Northwest China is low and mainly relies on simple machinery to dig the soil surface, and then people manually pick up and bag the potatoes. This harvesting method has the problems of a high labor intensity, low operation efficiency, and high labor cost. Based on this, a wheeled-chassis potato combine harvester with integrated bagging and ton bag-lifting systems was developed, which could complete potato digging, potato–soil separation, potato–film separation, automatic bagging, and field ton bag lifting in one go. Firstly, based on the agronomic requirements and unique terrain characteristics of potato planting in this area, the structural design of the whole machine was completed with SOLIDORKS 2019 3D software. Secondly, the dynamic model was established for a numerical analysis, and the core parameters of key components were determined. The field experiments showed that the potato loss rate was 2.1%, the potato damage rate was 1.7%, the skin breaking rate was 2.5%, the impurity content was 1.9%, and the productivity was 0.15~0.23 hm²/h. The above field test indexes met the requirements of national and industrial standards. Full article

Colombia is the world’s leading producer of mildly washed arabica coffee and produces 12.6 million bags of green coffee, but at the same time, 784,000 tons of waste biomass are dumped in open fields, of which only 5% is recovered or used. The objective of this project was to evaluate the production of platform chemicals from these coffee wastes for sustainable resource management. To achieve this, biomass characterization was carried out using proximate analysis, ultimate analysis, and structural analysis. Hydrothermal valorization was carried out at a temperature range of 120–180 °C (LHW) and 180–260 °C (HTC) for one hour. The platform chemicals obtained were quantified by HPLC-RI and monitored by pH and conductivity, and the solid fraction was characterized by monitoring the functional groups in IR spectroscopy and elemental analysis. Hydrolysis processes were obtained at 150 °C, production of platform chemicals at 180 °C, and maximum concentration at 180 °C-4 h; over 200 °C, degradation of the products in the liquid fraction starts to take place. Homogeneous basic and acid catalysts were used to improve the yields of the reaction. The kinetics of the hydrolysis of lignocellulosic structures to sugars were also analyzed and described, and reaction orders of 1 (LHW), 3 (HTC), and their respective reaction rate equations were reported. Full article

The rapid expansion of the livestock production sector to meet the world population’s demand is posing a big challenge to environmental sustainability. Plant-based feed additives extracted from agro-food byproducts could potentially result in multiple outcomes: reducing food-processing wastes and improving animal growth performances, hence mitigating environmental impacts of meat production chains. This presented study was carried out to assess the environmental impacts of the use of a commercial citrus extract feed additive (CEFA) in swine and broiler chicken farming. Life-cycle assessment (LCA) was applied to assess the impact of manufacturing and distributing one 25 kg bag of CEFA and its use in feed in broiler chicken and swine productions. With regards to CEFA manufacturing and distribution, results showed that most of the impact came from the production of CEFA ingredients, accounting for 70% of the impact generated. The remaining 30% effect was divided between transportation to the customer (25%), CEFA packaging (3%), and CEFA manufacturing and production loss (2%). When enlarging the scope, the use of the CEFA in pigs and broilers’ diets was shown to improve the measured environmental indicators, compared to such standard systems. Indeed, CEFA-added feeds have demonstrated enhanced growth performances, hence reducing the required amount of consumed feed to achieve the same level of growth. Consequently, this helped reduce environmental issues from animal feed ingredients’ agriculture. To be more specific, the use of one 25 kg bag of CEFA in feed at 250 g per ton of feed led to a reduction of 6 tons of CO₂ equivalent (CO₂ eq) emitted along the life cycle of poultry production and 5 tons in the case of fattening pigs. The inclusion of this CEFA in the diet also led to a reduction in the land use footprint by 0.7 hectares and reductions in water consumption by 201 m³ and 82 m³ for broiler chicken and swine production, respectively. The environmental performance assessment thus showed the interest in using this CEFA in swine and broiler chicken diets to mitigate the environmental impacts. Full article

Hermetic bag storage is a growing innovative technology that can effectively mitigate insect activity in stored grain and preserve quality without pesticides. This study aimed to estimate the environmental and economic impacts of hermetic storage bags as the basis for the sustainable adoption of the technology. This study demonstrated an approach to estimate the environmental impact of using hermetic bags and their superior economic benefits for storing maize at the 1-ton scale over three years. The life cycle assessment (LCA) of six commercially available hermetic bags (AgroZ^®, GrainPro, Storezo, ZeroFly^®, Elite, and PICS™) from cradle to grave was evaluated and compared using the Sustainable Minds LCA software. The gas barrier liners were analyzed for structure and polymer composition using confocal microscopy and Raman spectroscopy. The results showed that bag manufacturing had the highest environmental impact contribution, with 84.6% to 90.8% of the total impacts (mPt). The carbon footprint contribution of the total service life delivered for one hermetic bag ranged from 1.1 to 1.7 kg CO_2eq. The economic benefits of using hermetic bags were calculated and compared with traditional storage bag methods for one smallholder farmer using ten (10) hermetic bags storing 100 kg/bag (1 ton) of maize. The results found that using hermetic bags exhibited the highest profit of 1130 USD when used for nine months over three years, while storage loss was maintained at less than 1%. Full article

Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature °C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-_SC1 and ANN-MLP-_SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-_SC1) and 110.7 (ANN-MLP-_SC4). In the testing phase, the ANN-MLP-_SC4 had the highest r value (0.96), followed by ANN-MLP-_SC1 (0.94) and RF-_SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-_SC4 and ANN-MLP-_SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-_SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-_SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management. Full article

Microplastic contamination has become a concerning topic of study in recent decades. This review discusses the development of microplastic pollution based on a selection of South Asian countries consisting of Bangladesh, Iran, Philippines, Thailand, India, Indonesia, and Vietnam. The condition of microplastic pollution related to the abundance of microplastic found in various environments as well as the presence of microplastics in food and the air, is covered in this review. Several reports found that drinking water sourced from taps was found to have about 83% of microplastic particles in the year 2017 based on results from 14 nations, and in the year 2018, 260 bodies of water for human consumption in 11 countries were found to have about 93% of microplastic particles. Micro debris pollution in seas and oceans worldwide is predicted to be at an amount of 236,000 metric tons based on a statistical report. A mean value of 30 micro debris per liter of glacier water was recovered from the top of Mount Everest, whereas about 2200 small particles per liter were discovered in the deep waters of the Mariana Trench. The main environments that are severely microplastic-contaminated are water-based places such as rivers, estuaries, and beaches. The presence of microplastics in food items, such as tea bags, sugar, shrimp paste, and salt packets, has been reported. In terms of impacts on the environment, microplastic contamination includes the ingestion of microplastics by aquatic creatures in water environments. The impacts on terrestrial environments relate to microplastics sinking into the soil, leading to the alteration of the physicochemical parameters of soil. Meanwhile, the impacts on the atmospheric environment include the settling of microplastics on the external bodies of animals and humans. Full article

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R²) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO₂e) and mean absolute percentage error (0.29 GtCO₂e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO₂ equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change. Full article

With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks. Full article