Hasil untuk "River, lake, and water-supply engineering (General)"

Yadviga Tynchenko, Vladislav Kukartsev, Alexey Gladkov et al.

Introduction. Mining requires water treatment and wastewater processing, abstraction and discharge during mining increases consumption several times. Since water consumption in mining and processing is usually associated with domestic, industrial and technical needs, the need for water supply systems required for water treatment increases. Water from different sources can be used for treatment: incoming water, process and reused water, and wastewater. But the water obtained from any of the sources must meet all the norms and requirements. Water quality is determined by physical, chemical and bacteriological properties. The main directions for improving water consumption by mining enterprises are to reduce the consumption of drinking water from rivers, lakes and municipal water supply, as well as to expand the use of mine and quarry water for domestic and technical needs. Materials and methods. As training data for training the neural network, a dataset that includes water quality data obtained from fresh water sources was selected for the methods work, and using machine learning, develops a model that predicts whether the water is suitable for technical use in mines. This dataset includes 2293 values (samples) as well as 9 attributes. Correlation, neural network, and decision tree methods were used to build the models in this study. Results. Various machine learning methods (neural network and decision trees) were used to build a predictive model to assess the quality of water that would be suitable for use in the mining industry for technical purposes. With the help of the built models were processed data obtained from public sources, when analyzing which it was found that the method of decision trees was more accurate. The constructed model, for determining dependencies, thus, has high accuracy (small error). To increase the practical significance of the study, a number of transformations of the initial data set were carried out, in particular, an experiment with the division of attributes into groups of importance, in relation to the data, taking into account the subject area. The results obtained made it clear that checking only for hazardous impurities does not guarantee the suitability of water, but almost completely excludes (low significance factor) samples with impurities that do not meet the requirements, and the model can have practical significance. Allocation of the group for rapid quality determination, showed that for the express test, in an emergency situation or under time constraints, the possibility of practical use of the obtained model, has a justification, due to the small error. In general, the conducted experiments have shown that when taking into account the costs (total) for data collection, it makes sense to use models, taking into account the reduction of collected data, on the parameters (factors) of technical water. Discussion. In general, on the basis of the conducted research, we can talk about the successful application of machine learning methods in determining the suitability of technical water in the mining industry. During the experiments, the decision tree method performed particularly well, with the lowest error values. In addition, further work can be carried out to reduce the error in the models, in particular, by possibly increasing the number of attributes, as well as more fine-tuning of the applied machine learning methods. Conclusions. The authors conclude that machine learning techniques can be successfully integrated to determine the quality and suitability of process water in the mining industry in today’s world. Resume. The paper compares machine learning methods such as decision trees and neural network method. The comparative analysis of these methods and their quality of information processing is shown on the example of a set of data on water quality in the mining industry. With the help of built models were processed data obtained from open sources, when analyzing which it was found that the method of decision trees was more accurate. The constructed model for determining dependencies has high accuracy (small error). Suggestions for practical applications and future research directions. This study can form the basis for research in this or related fields to conduct further studies on the reliability and accuracy of using machine learning to predict the quality of water used in the mining industry. Continued work in the above direction may be the rationale for wider use of the above methods to improve various meaningful production performance in this or related areas.

WEN Shu-jie, XU Chang-yi, HUANG Xiang, HUANG Ying-hao, FU He-lin

[Objective] A method for preparing solidified lightweight red sandstone soil using microbial-induced calcium carbonate precipitation (MICP) technology was proposed for the recycle use of red sandstone residual soil in engineering. A design study was conducted on bio-based solidified lightweight red sandstone soil to investigate the solidification mechanism of the modified material. The effects of expanded polystyrene (EPS) mass content and cementation solution concentration on the strength of the lightweight solidified soil are analyzed. Based on this, the compression failure characteristics of the solidified lightweight red sandstone soil are studied, and its cementation mechanism is validated through both strength analysis and failure characteristics. [Methods] Bacillus pasteurii was selected as the target strain, and cementation solutions with concentrations ranging from 0.5 to 2.0 mol/L were prepared. Solidified lightweight red sandstone soil samples with EPS contents ranging from 0% to 1.125% were prepared. The internal microstructure of the modified red sandstone residual soil was analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Additionally, its mechanical properties were evaluated through slow shear tests and uniaxial compression tests. [Results] After MICP treatment, a substantial amount of calcite-type CaCO3 precipitates was generated within the red sandstone residual soil. These CaCO3 crystals formed a continuous and dense cementation network between soil particles, serving as the primary contributor to the strength of the solidified lightweight red sandstone soil. In contrast, only sparse crystal clusters were observed on the surfaces of hydrophobic EPS particles. When the cementation solution concentration was 1.5 mol/L and the EPS content was 0.375%, the solidified lightweight red sandstone soil samples exhibited the optimal performance combination. The compressive strength reached 0.76 MPa, meeting the standard requirement (≥0.6 MPa) for foam lightweight soil. The bulk density was 14.3 kN/m3, representing a 13% reduction compared to the undisturbed soil. Additionally, the internal friction angle and cohesion increased by 39% and 17%, respectively. Failure mode analysis revealed that samples with low EPS content (≤0.375%) exhibited typical brittle shear failure, with cracks propagating in a “Y” shape. In contrast, samples with high EPS content (≥1.125%) showed bulging failure, accompanied by surface spalling and debris detachment. [Conclusions] The combination of microbial solidification technology and EPS lightweight foam soil technology has effectively solidified lightweight red sandstone soil, overcoming the high energy consumption limitations of traditional cement-based solidification methods. A quantitative relationship between “cementation solution concentration, EPS content, and mechanical properties” was established. The proposed optimal mix ratio (1.5 mol/L cementation solution + 0.375% EPS) combines both lightweight characteristics (bulk density of 14.3 kN/m3) and high strength (0.76 MPa). This study provides a low-carbon and environmentally friendly solution for the resource utilization of red sandstone residual soil, demonstrating significant application value in engineering fields such as subgrade filling.

Aleksandra V. Selezneva, Vladimir A. Seleznev

Assessing the change in the hydrological regime of the reservoirs of the Volga-Kama cascade is very relevant in the context of global warming. Reservoirs are used for domestic and drinking water supply, fisheries and recreation. The largest in the cascade, the Kuibyshev Reservoir, was chosen as the object of study. It is characterized by a seasonal regime of regulation of the Volga water flow. The main objective is to quantify interannual and seasonal changes in the reservoir water level for the period from 1958 to 2024. Relevance.

Hussain H. Bokhari, F. Corsi, A. Miara et al.

The engineering of rivers by dams is a formative feature of human‐nature systems and the interconnectivity of water, energy, and the climate. Sufficient and broad‐based representations of dams in large‐scale hydrological models prove essential to mapping their extensive regulation of river flow and biogeochemistry and gauging climate‐linked provisions, including freshwater supply and hydropower. We present an integrated modeling framework to investigate future streamflow and hydropower generation in the Contiguous U.S. (1990–2075), leveraging an ensemble of six downscaled and bias‐corrected General Circulation Models (GCMs) from the high‐end SSP585 scenario of the CMIP6. To achieve this, we develop a reservoir operations and parameterization scheme for 1,384 dams in a high‐resolution river network, including simulated hydropower generation for 326 dams. For the GCM ensemble mean, we simulate a widespread increase in regulated streamflow into the late‐century (11% annual and 17% in winter for the dam median) with region‐specific changes in summer streamflow that feature prominent declines in the Northwest (−7%). Mediation by reservoirs is shown to dampen intra‐annual streamflow changes, delivering additional summer releases that partially mitigate declining flows. Total hydropower generation is projected to increase modestly (+3%), with boosted generation in the winter (+9%) and spring (+5%) offsetting declined summer generation (−3.4%), suggesting strong adaptation potential for hydropower in the future energy portfolio. Further analysis reveals that the choice of GCM, particularly in western regions, has significant bearing on projected streamflow and hydropower changes.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

LIU Jin, CHEN Yechao, DING Wu et al.

The river system in the Pearl River Delta river network is complex with numerous embankments, among which the Zhongshun Dawei river system has the most complex structure and the greatest difficulty in the joint dispatching of sluices and pumps. In order to fully explore the potential of joint dispatching of water projects in controlling water quantity and quality, this paper took Zhongshun Dawei as the research object, constructed a one-dimensional hydrodynamic-water quality coupling model for Zhongshun Dawei, and simulated water quantity and quality processes within the embankment under various dispatching schemes of sluices and pumps. On this basis, a joint optimization dispatching model for sluices and pumps was established to achieve the optimal decision-making of dispatching schemes under different water diversion conditions. Finally, based on the simulation and dispatching models of water quantity and quality, a water quantity and quality dispatching simulation system for the Zhongshun Dawei river network was developed, which realized the simulation of hydrodynamic characteristics of Zhongshun Dawei, the dynamic simulation of pollutant concentration and water quality changes, the emergency dispatching of water pollution, and the visualization of the process. A water quantity and quality simulation and control technology system based on joint optimization dispatching of sluices and pumps under the connection of the Zhongshun Dawei river system was formed. Based on the system, two typical scenarios were analyzed, and the results show that in the case of optimization dispatching, the weighted concentrations of COD and NH3-N at the end of the dispatching period are improved by 10.26% and 27.27%, respectively, compared with the case without optimization dispatching. These findings demonstrate the significant dispatching effects and provide reliable technical support for water quantity and quality dispatching decision-making in the Zhongshun Dawei region.

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

I. Mytiai, V. Khomych, E. V. Degtyarenko et al.

The peculiarities of the transformation of river ecosystems in the reservoir at the construction of mini-hydropower plants have been found out. It was also found that the large-scale hydraulic engineering in the second half of the XX century caused significant changes in environmental conditions in the waterbodies, which has led to negative dynamics of species richness of aquatic organisms in general and phytoplankton in particular. Reservoir building has a significant negative impact on river ecosystems: currents slow down and disappear, canals become silted up, harmful substances of household and industrial waste gradually accumulate. The transformation of rivers into lake-type reservoirs is also accompanied by changes in the species composition and structure of phytoplankton. Eutrophication of reservoirs becomes typical. All this leads to significant degradation in aquatic ecosystems.

LIU Yanju, YANG Ruixiang

Taking water resources as the maximum rigid constraint has become an inevitable requirement for promoting unified management of water resources and high-quality development of the economy and society in the new situation.The core meaning of the maximum rigid constraint of water resources is to use water resources as their capacity permits.Total water consumption control management is the core standard to implement the rigid constraint of water resources.Based on the characteristics of water resources in the Pearl River Basin with better natural water resources conditions,this study focuses on solving the existing practical problems in the implementation of the rigid constraint system of water resources in the Pearl River Basin,such as weak foundation,poor system and mechanism,and imperfect management of total water consumption indicators.The study also puts forward suggestions on total water consumption management indicators of the basin based on available water resources and the sum of water transferred in and out.The scope of application and the preconditions for dynamic adjustment of total water consumption management indicators are clarified,and the indicator adjustment mode is established.It provides support for strengthening the management of total water consumption in the Pearl River Basin under the background of the rigid constraint of water resources,vitalizes the total water consumption indicators,and improves utilization efficiency.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.

HU Jiang, MA Fuheng, SHENG Jinbao et al.

Long-distance water diversion projects are important strategic infrastructure to optimize the patterns of water resource allocation,which are characterized by long distances,many hydraulic structures,complex geological and environmental conditions,and high maintenance requirements.The long-term safe operation of these projects has been highly concerned.The annual safety report of these projects is an effective means to grasp the engineering status in a timely and dynamic manner.On the basis of the characteristics of these projects and the discussion on the existing basis and compilation principles of annual safety reports,the organization and implementation of the main work in annual reports,such as safety inspection,safety detection,and safety assessment,are explored,and the regulation of an annual safety report is proposed.According to the practice of the first annual safety report on the central route of the South-to-North Water Diversion Project,the key contents of the annual safety report,including safety inspection,safety detection,and safety monitoring,as well as operational management contents such as dispatching operation,maintenance,safety production,and emergency management are briefly introduced.Finally,the experience,understanding,and difficulty of annual safety report compilation are summarized.The successful application of the annual safety report in the central route project can provide a reference for similar projects.

Zhangliang Song

: With climate change, water supply from dams and reservoirs is decreasing in many regions. For centuries, people have built dams on rivers and streams to hold back water, and reservoirs have been built as a means of managing water supply. In this paper, we established a double-dam coordination and cooperation model. We abstracted the lake into a cuboid, and established a physical model of hydropower supply and demand based on the water levels of Lake Mead and Lake Powell. Next, we established a hydropower conversion rate model, taking into account The cooperation between the power stations has established a short-term optimal scheduling mathematical model of the hydropower system. Through the constraints, we have reached the relationship between the hydropower conversion efficiency and the lake water level. We established a short-term optimal scheduling model for hydropower systems, which addresses t he competing interests of general agricultural, industrial residential water use and water availability for water power and electricity production. The electric energy is the largest and the unification of the rules that the electric energy is the largest and the water consumption is the smallest is realized. Combined with the water consumption constraints of the target power station, the water balance constraints of the reservoirs of each hydropower station, and the reservoir capacity constraints, the water level height reduction model is established. On the basis of the previous model, we established an optimization model based on particle swarm optimization, and brought it into matlab for simulation through particle swarm optimization, and calculated the hydropower conversion efficiency of Lake Mead and Lake Powell with the height of the water level under constraints.

Xu Wang, Tao Qian, Yanhui Xue

A severe drought in Lake Mead posed severe challenges for people living there due to the lack in water and electricity supplies. To cope with this serious situation, we formulates a scientific water allocation plan for the surrounding 5 states. First, we establish the Optimal Water Supply Model, which comprehensively considers various general water needs in the five states, as well as the waterline, water volume, and electricity production of the two reservoirs. On average, the two dams need to run for about 3 hours per day with a water flow of about 13800cfs to meet all aspects of water and electricity demand. However, these general demands will be difficult to maintain without an additional water supply. We comprehensively consider the influence of natural evaporation, Colorado River flow and other factors on the reservoir waterline, and establish a functional relationship between the waterline and time. When the water level is below a certain height, interested parties can calculate the additional water supply based on the model.

Tyler Cody, Niloofar Shadab, Alejandro Salado et al.

Engineering methods are centered around traditional notions of decomposition and recomposition that rely on partitioning the inputs and outputs of components to allow for component-level properties to hold after their composition. In artificial intelligence (AI), however, systems are often expected to influence their environments, and, by way of their environments, to influence themselves. Thus, it is unclear if an AI system's inputs will be independent of its outputs, and, therefore, if AI systems can be treated as traditional components. This paper posits that engineering general intelligence requires new general systems precepts, termed the core and periphery, and explores their theoretical uses. The new precepts are elaborated using abstract systems theory and the Law of Requisite Variety. By using the presented material, engineers can better understand the general character of regulating the outcomes of AI to achieve stakeholder needs and how the general systems nature of embodiment challenges traditional engineering practice.

Lingling Zhao, Fei Xu, Jun Xia et al.

Potential evapotranspiration (PET) is a comprehensive factor that characterises climate change, and considering the numerous methods to calculate PET, it is difficult to objectively select a method according to the requirements. In this study, the applicability of 12 commonly used PET estimation methods in China was studied. Based on temperature and humidity, China is divided into 11 temperature zones (TZ) and 5 arid and humid regions (AHRs). The study used the FAO Penman–Monteith (P-M) method as the standard, and the applicability of the 12 methods was analysed using four factors: correlation, annual mean values, seasonal distribution, and parameter characteristics. The results show that the radiation-based methods have the best monthly correlation with the P-M method, the temperature-based methods are second best, and mass-transfer-based methods perform the worst. Among these, the P-T method is the best, and the Hamon method is the worst. The Kharrufa and Abtew methods have the better applicability in higher TZs, whereas the Harg method has the least applicability. The seasonal distribution of radiation-based methods (excluding the Jensen method) in the different AHRs and different TZs is better than that of temperature-based and mass-transfer-based methods. According to the evaluation results of all factors, the Rohwer, P-T, and Mark methods are recommended when the data conditions are not conducive for the P-M method. HIGHLIGHTS There are many methods to calculate PET, while it is difficult to choose objectively according to the needs.; The applicability of 12 commonly used PET estimation methods in China was studied from four aspects.; Variation range of the national annual value estimated is 288.72–1355.1 mm.; The P-M method and energy-based methods have the highest monthly correlation.; The Rohwer, P-T, and Mark methods are recommended.;