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A Novel Technique in Determining Mud Cake Permeability in SiO2 Nanoparticles and KCl Salt Water Based Drilling Fluid using Deep Learning Algorithm
Full text linkThe permeability of the mud cake formed at the formation-wellbore interface is an important factor in the designing of water-based drilling fluids. This study presents a novel approach to utilizing experimental thixotropic and rheological parameters of polymeric water-based drilling fluids having varying concentrations of SiO2 nanoparticles and KCl salt. A fully connected feed-forward multi-layered neural network, more commonly known as a Multilayer Perceptron (MLP) was developed to predict the mud cake permeability using input parameters such as SiO2 & KCl concentration, differential pressure, temperature, mud cake thickness, API LPLT and HPHT filter loss volume and spurt loss volume. The results suggested that the developed Multilayer Perceptron model effectively determined the mud cake permeability based on the input parameters of the WBDF mentioned above. The model converged on the global minima, minimizing the loss function using the Gradient descent algorithm. A higher Coefficient of Determination (R2) value i.e., 0.8781, and a lesser Root Mean Square Error (RMSE) value i.e., 0.04378 indicates the higher accuracy of the model. Pearson’s Coefficient of Correlation obtained via the heatmap indicates that mud cake permeability is strongly influenced by the differential pressure followed by filter loss volume, spurt loss volume, mud cake thickness, and temperature. Previous similar studies have focused on using machine learning algorithms, this study utilized a robust deep learning algorithm i.e., Multilayer Perceptron (MLP) neural network to simultaneously model the combined effects of SiO2 nanoparticles and KCl salt concentrations on mud cake permeability, offering an unprecedented level of accuracy in predicting key WBDF performance parameters
Study on the Function and Planning of Urban River Ecological Corridor
Full text linkThis study investigates the functions and planning principles of urban river ecological corridors, highlighting their vital role in urban ecosystems. Urban rivers, which include both waterways and riparian zones, provide essential ecosystem services such as habitat provision, temperature regulation, pollutant filtration, and flood mitigation. However, rapid urbanization has led to the degradation of these corridors, resulting in habitat fragmentation, reduced biodiversity, and compromised ecological integrity. The paper reviews global efforts and strategies for urban river restoration, emphasizing the significance of ecological methods and public participation in the planning process. A case study of the Yuhangtang River in Hangzhou, China, exemplifies various restoration approaches, including traditional flood control, ecological restoration, and landscape design, all assessed using a multi-criteria decision-making (MCDM) framework. The findings indicate that employing the MCDM tool can facilitate planning that integrates the functions of river ecological corridors. Additionally, plans that achieve a balance of ecological, economic, social, and aesthetic benefits are more likely to gain public acceptance
Assessment of User Preferences in Electric Vehicle Charge Billing System
Full text linkDespite the advantages of electric vehicles (EVs), however, their adoption rate in Tanzania remains low. The growth and sustainability of EVs remain questionable due to several factors including an insufficient network of charging infrastructure coupled with billing systems. This study analyzes user preferences related to EV charge billing requirements based on the qualitative assessment of semi-structured interviews with EV users. A survey was conducted around Dar es Salaam city and around 81 sample surveys were administered. The targeted parameters are current EV charge types, daily charging frequency, charging duration, billing system, bill payment, and expectations on public charging infrastructure. The factors were selected to enable an understanding of the drivers for improving acceptance of EV charge billing systems and deduce their market potential. For electric two-wheelers (e2Ws), the findings indicated that 60% of e-bicycle riders charge once per day, 30% charge twice per day, and 10% charge 3 times per day. Moreover, 34% of e-bicycle batteries were charged with 50% of energy remaining in the battery, which took 2 to 3 hours. Contrary, 40.74% of electric three-wheelers (e3Ws) were charging 2 times per day, 33.33% were charging 3 times per day, and 25.93% were charging 1 time per day. In terms of billing, e-bicycles’ charging energy was metered but not billed while e3Ws’ charging energy was not metered but billed at a flat rate regardless of the energy consumed. The majority of EV drivers expected more public charging stations to be equipped with accurate energy measuring systems to enable them to pay-per-use
Predicting Building Primary Energy Use Based on Machine Learning: Evidence from Portland
Full text linkAccurately predicting equivalent primary energy use (EPEU) in buildings is crucial for advancing energy-efficient design, optimizing operational strategies, and achieving sustainability goals in the built environment. This study aims to develop reliable prediction models for EPEU by leveraging a comprehensive and high-quality dataset from buildings in Portland, USA. To achieve this, a systematic machine learning framework is adopted, encompassing feature selection, data preprocessing, model training, and performance evaluation. Several state-of-the-art machine learning algorithms are applied, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Back-Propagation Neural Networks (BP). These models are trained using key features such as building type, gross floor area, construction year, and various operational characteristics that are known to significantly influence energy consumption patterns. The dataset is carefully cleaned and normalized to ensure model generalizability and minimize bias. Model performance is assessed using standard statistical metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Among the tested models, ensemble learning methods—particularly RF and GBDT—consistently outperform others in terms of prediction accuracy, robustness, and stability across different building types. The results of this study not only highlight the potential of machine learning in energy prediction tasks but also provide actionable insights for architects, engineers, facility managers, and policymakers. By identifying the most influential variables and employing effective predictive models, this research supports data-driven decision-making processes aimed at improving building energy performance.
Review of Recyclable Bio-based Epoxy Resins with Dynamic Chemical Bonds
Full text linkEpoxy thermosetting resins are usually reliant on fossil fuel-based resources, commonly diglycidyl ether bisphenol A (DGEBA) type epoxy monomers. Most raw materials of these thermoset resin are toxic to the health of human, and their eternal cross-links make them difficult to reuse and recycle. To alleviate concerns about the environment and human health, it is an effective way to design new bio-based epoxy thermosetting materials to replace petroleum based thermosetting materials. The introduction of cleavable and dynamic bonds for bio-based thermosetting materials can also realize the recycling of bio-based epoxy resin. In this way, the damaged thermosetting materials can be recovered to prolong their service lifetime and reduce the thermosetting waste. This review article aims to outline the latest improvements in intrinsically recyclable bio-based epoxy thermosetting materials. This review first describes the synthesis method of recyclable bio-based epoxy, then reviews the structure, recyclable and other properties of bio-based epoxy containing different dynamic bonds, and finally summarizes the challenges and opportunities for the recyclable bio-based epoxy
An Urban Lake Drainage Catena: Influences of Terrain, Soils, and Precipitation
Full text linkUrban lake drainage systems are heavily impacted by terrain, soil characteristics, and precipitation, which influence water infiltration and groundwater movement. This study focused on the drainage catena around Lake Nokomis in Minneapolis, Minnesota, where local residents have experienced wet basements and yards. The primary goal was to identify the factors contributing to these water-related problems, particularly soil permeability and how it responds to precipitation. By conducting soil borings, using pressure transducers, and measuring saturated hydraulic conductivity (Kfs), the study compared upland and lowland areas. Findings indicated that upland soils, primarily composed of sandy fill, had much higher infiltration rates, with Kfs values ranging from 72.4 cm/hr to 149 cm/hr. In contrast, lowland areas characterized by lacustrine and organic soils exhibited significantly lower Kfs values, ranging from 1 cm/hr to 14.8 cm/hr. Between 2022 and 2024, wet and dry seasons occurred, yet recorded more than 127.5 cm of rain and snow water equivalent, further contributing to groundwater rise and surface water presence in low-lying regions. The study concluded that increased precipitation, coupled with specific hydrogeologic conditions, was the main factor causing elevated groundwater levels and surface saturation in these areas. To address these challenges, Minnesota's water management authorities are encouraged to implement strategies that consider the increasing magnitude and intensity of precipitation events due to climate change. Incorporating hydrogeologic assessments into urban planning is recommended to better manage water infiltration, reduce flood risks, and strengthen the resilience of drainage systems to changing climate patterns
Assessment of Electricity Consumption of Middle-income Households in Tanzania
Full text linkElectricity is the foundation of modern society, powering a vast array of daily activities and technological advancements. Despite increased electricity access, the majority of Sub-Saharan African countries face the dilemma of energy consumption outpacing generation. Gaining a good grasp of behavioral drivers of energy use, especially among middle-income households (MIHs), is necessary to reduce energy consumption. This study assesses the electricity consumption from MIHs in a targeted area of Masaki, Dar es Salaam region, Tanzania. The study integrated the household characteristics and electrical load consumption patterns in the electricity consumption of MIHs. The 1-month data, between May 2024 and June 2024, were gathered from 99 respondents using an e-questionnaire. The household characteristics included the number of occupants per household, awareness of energy management programs, adoption rate, and interested features and expectations in energy management programs. The electrical load consumption patterns include types of electrical loads, hourly usage, average monthly bills, and fluctuations in monthly energy bills. Findings revealed that the average number of occupants per household was 6, but only two out of 6 occupants per household were aware of energy management programs. Appliance control was the most adopted energy management program (44.12%) followed by real-time energy monitoring (11.76%) and integration with renewable energy sources (8.82%). Contrary, about 96% of respondents were interested in engaging in energy management initiatives aiming at cost-saving (62%) and convenience (20.7%). Evening hours reported to use the most energy (68.7%), followed by night hours (50.5%). The average monthly energy bills were found to range between TZS 70,000 and TZS 300,000 with 48.5% of respondents reporting large swings in their electricity expenses. The findings of this study provide policymakers with evidence that awareness initiatives should be included when formulating energy consumption and efficiency strategies
Characterization and Analysis of Gas-Solid Flow Dynamics in Fluidized Bed Systems
Full text linkFluidization is a critical process in various industrial applications, particularly in the oil and gas sector, where it plays a pivotal role in optimizing production, phase separation, and the efficiency of mass and heat transfer, as well as chemical reactions involved in physical and chemical operations. The significance of fluidization stems from its direct impact on key parameters such as pressure drop, flow velocity, particle concentration, and void fraction in multiphase flows. In this context, fluidization enhances the interaction between fluid and solid particles, reducing resistance to heat and mass transfer and promoting material homogenization. This experimental study aims to investigate the flow behavior of a particulate system within a fluidized bed, specifically focusing on the fraction of the continuous (gas) phase relative to a defined volume of solid particles. The results obtained, when compared to existing literature primarily concerned with the effects of particle size and flow rate on fluidization, demonstrate consistency in both qualitative and quantitative analyses. These findings suggest that the increase in the volume of the continuous phase in such flows is strongly influenced by the Reynolds number and the particle size within the system. Thus, this study makes significant contributions to the optimization of fluidization processes, particularly in industrial sectors like oil and gas. By providing detailed experimental insights into the factors that influence fluidized bed performance, the findings offer practical implications for improving the efficiency of heat and mass transfer, phase separation, and reaction rates in industrial applications where the behavior of the continuous phase is crucial
Evidence of a Large Debris Avalanche Event (22.0 Ma) from the Comondú Group on the Baja California Sur Peninsula, Mexico
Full text linkThe morphological, sedimentological, and microtextural characteristics of Miocene debris avalanche deposits which extend from the Punta Coyote to the vicinity of the city of La Paz, were studied along the eastern of the Baja California Peninsula. The debris avalanche deposits studied include a mixture of angular mega blocks whose composition comes from the deposits that make up the Comondú Group: pre-Comondú (red sandstones and conglomerates with intercalated ignimbrites), the Upper Unit (brownish sandstones, shales, and conglomerate), and breccia, with a predominance of jigsaw cracks, injection structures, and fault structures. These deposits were studied and analyzed considering the stratigraphic relationships between the rock formations present in the mega-blocks. Six stratigraphic sections were measured to describe the composition and morphology of the clastic components present in the mega-blocks of the debris avalanche. Two different units (m1 and m2), were identified in the debris avalanche deposits. Unit m1 is the oldest, with a thickness of 100m, and consists of a chaotic set of mega-blocks up to 100 m in diameter derived from the pre-Comondú Group, and Upper Unit. The deposits are highly heterolithic, with angular and highly fractured clasts at different scales. While the unit m2 consists principally of 20-100 m thick volcaniclastic layers dominated by poorly sorted, breccias and minor epiclastic deposits. According to stratigraphic relationships, the collapse occurred at 22.0 Ma. The debris deposit covers an area of 150 km2 and has an estimated volume of 1.3 km3. The characteristic suggests a transport mechanism with a disintegration of the mega-blocks and a contact/collision interaction. Where mega-blocks moved within a dense flow in a buffered manner, remaining consistent over long distances. The observed structures and textures suggest that the mega-blocks were mainly produced by the alteration and ingestion of older substrates by the avalanche of moving debris. The avalanche flowed over pre-existing topography excavated in the Comondú Group sequence, and flow indicators reveal a west-southwest direction, exhibiting a typical mountainous avalanche topography. The study of ancient debris avalanche events not only provides a deeper understanding of these natural phenomena but also contributes to the development of tools to predict, mitigate, and manage risk areas
Charcoal-production, Air Pollutant Impacts on Ambient Environment and Associated Health Risks: A Systematic Review
Full text linkCharcoal is a widely utilised fuel produced from the carbonisation of organic materials, such as wood and other biomass sources. Regrettably, airborne contaminants from traditional charcoal producing techniques can negatively impact human health and the environment. This research explore air pollutant emissions from traditional charcoal producing methods and their impacts on human health and the environment. This study utilised a qualitative synthesis methodology, incorporating case studies, archival research, and discourse analysis, to elucidate the impacts of charcoal production. The results demonstrate that the traditional charcoal production method results in substantial carbon loss from fuelwood and emits by-products of incomplete combustion, exacerbating serious health risks and degrading air quality associated with community health problems. Empirical evidence indicates that the majority of charcoal manufacturing workers lack awareness of the health risks associated with their working circumstances and the respiratory problems they face. Unsustainable environmental practices highlight the social and ecological repercussions of charcoal production. It is advisable to apply air pollution mitigation methods around charcoal kiln facilities to protect environmental and community health. The Environmental Protection Agency must actively implement effective oversight and integrated management to improve air quality and safeguard communities from air hazards. This study recommends testing high-efficiency technologies in communities capable of maintaining and assessing their effects on environmental degradation. Both governmental entities and humanitarian organisations should prioritise educational activities centred on effective land management approaches, as this study's findings suggest