Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.
Guang An Ooi, Otavio Bertozzi, Mohd Asim Aftab
et al.
Modern power systems with high penetration of inverter-based resources exhibit complex dynamic behaviors that challenge the scalability and generalizability of traditional stability assessment methods. This paper presents a dynamic recurrent adjacency memory network (DRAMN) that combines physics-informed analysis with deep learning for real-time power system stability forecasting. The framework employs sliding-window dynamic mode decomposition to construct time-varying, multi-layer adjacency matrices from phasor measurement unit and sensor data to capture system dynamics such as modal participation factors, coupling strengths, phase relationships, and spectral energy distributions. As opposed to processing spatial and temporal dependencies separately, DRAMN integrates graph convolution operations directly within recurrent gating mechanisms, enabling simultaneous modeling of evolving dynamics and temporal dependencies. Extensive validations on modified IEEE 9-bus, 39-bus, and a multi-terminal HVDC network demonstrate high performance, achieving 99.85%, 99.90%, and 99.69% average accuracies, respectively, surpassing all tested benchmarks, including classical machine learning algorithms and recent graph-based models. The framework identifies optimal combinations of measurements that reduce feature dimensionality by 82% without performance degradation. Correlation analysis between dominant measurements for small-signal and transient stability events validates generalizability across different stability phenomena. DRAMN achieves state-of-the-art accuracy while providing enhanced interpretability for power system operators, making it suitable for real-time deployment in modern control centers.
The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time.
The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a problem solving worldview. It focuses on algorithm parameters, features, and samples, and neglects the perspective offered by considering system structure and system behavior or dynamics. Online learning is an active field of research and has been widely explored in terms of statistical theory and computational algorithms, however, in general, the literature still lacks formal system theoretical frameworks for modeling online learning systems and resolving systems-related concept drift issues. Furthermore, while the machine learning formulation serves to classify methods and literature, the systems theoretic formulation presented herein serves to provide a framework for the top-down design of online learning systems, including a novel definition of online learning and the identification of key design parameters. The framework is formulated in terms of input-output systems and is further divided into system structure and system behavior. Concept drift is a critical challenge faced in online learning, and this work formally approaches it as part of the system behavior characteristics. Healthcare provider fraud detection using machine learning is used as a case study throughout the paper to ground the discussion in a real-world online learning challenge.
This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices. The method combines control barrier functions and adaptive laws to generate a safe controller through a nonlinear program with an explicitly given closed-form solution. The proposed approach verifies the non-emptiness of the admissible control set independently of online parameter estimations, which can ensure the safe controller is singularity-free. A data-driven algorithm is also developed to improve the performance of the proposed controller by tightening the bounds of the unknown parameters. The effectiveness of the control scheme is demonstrated through numerical simulations.
Nurbek Yerdessov, A. Omarova, Chingiz Usmanovich Ismailov
The article analyzes the coverage of water supply and sanitation services among the rural and urban population of Karaganda region; presents data on the length of networks and their technical condition, as well as the effectiveness of water disposal system facilities. The aim of the work: to study the degree of coverage of water supply and sanitation services and their technical condition among the rural and urban population of Karaganda region for the period 2010-2020. Retrospective analysis of secondary data with a depth of 10 years, form: “On the work of water supply and sewage facilities in the Republic of Kazakhstan”. Among 421 rural settlements of the region – 245 are provided with the centralized water supply (89,9%), 166 with the decentralized water supply (9,9%) and 8 with the transported water supply (0,2%). Coverage of the centralized water supply in urban areas of the region is 98%, coverage by the centralized sewage system is 78.43%. The length of water supply networks in rural areas tends to increase with an increase of 80.5%, the drainage system also increased by 81.2%. In urban areas, the length of networks remained unchanged. The coverage of the rural population with central water supply and sanitation significantly increased during the study period. The results of the research indicate that at present there is a significant imbalance between the provision of the population with centralized water supply and centralized water disposal. The technical condition of water supply and sewerage networks in rural areas remains at an unsatisfactory level, which leads to high losses and deterioration of drinking water quality.
Foteini Sakaveli, Maria Petala, Vasilios Tsiridis
et al.
Processing of the produced primary and secondary sludge during sewage treatment is demanding and requires considerable resources. Most common practices suggest the cotreatment of primary and secondary sludge starting with thickening and anaerobic digestion. The aim of this study is to investigate the anaerobic digestion of the primary sludge only and estimate its impact on sludge treatment and energy recovery. Within this context, the performance of the anaerobic digestion of primary sludge is explored and focused on practices to further enhance the methane production by using additives, e.g., a cationic polyelectrolyte and attapulgite. The results showed that the overall yield in methane production during anaerobic digestion of primary sludge alone was higher than that obtained by the anaerobic digestion of mixed primary and secondary sludge (up to 40%), while the addition of both organic polyelectrolyte and attapulgite enhanced further the production of methane (up to 170%). Attapulgite increased the hydrolysis rate of biosolids and produced relatively stabilized digestate, though of lower dewaterability. Moreover, the results suggest that single digestion of primary sludge may accomplish higher methane production capacities at lower digestors’ volume increasing their overall efficiency and productivity, while the produced digestates are of adequate quality for further utilization mainly in agricultural or energy sectors.
Abstract. The expediency and critical conditions of conducting hydraulic calculations of drainage pipelines according to simplified power-law formulas have been determined. The main interrelationships between structural and kinematic parameters of drainage pipelines when performing their hydraulic calculations using simplified formulas are analyzed. Numerical values of coefficients and exponents in simplified formulas for pipelines made of different materials were obtained. It was established that for all types of urban drainage (domestic, rainwater and combined), the values of the minimum allowable slopes of the pipelines practically coincide, depend only on their diameters and can be calculated according to the obtained empirical formula with the recommended numerical values of its parameters for pipes of different materials. The maximum allowable of minimum and maximum slopes, as well as the corresponding maximum waste water consumption for pipelines of different diameters and materials, are determined. New approaches to hydraulic calculations of water drainage networks involves their execution according to a single methodology based on the proposed simplified formulas. Also, it involves the use of the obtained empirical dependencies to determine the minimum and maximum permissible slopes and the corresponding maximum marginal flows of wastewater and pipe diameters. The algorithm for the unification of hydraulic calculations when solving the optimization problem is considered. The effectiveness of the application of simplified power-law formulas of hydraulic calculations and empirical dependencies obtained on their basis for unification is shown. Carrying out hydraulic calculations of drainage pipelines using simplified power-law formulas allows not only to simplify the calculations themselves, but also to obtain additional dependencies between the structural parameters of the pipelines based on them.
Arsalan Zakeri Afshar, Hamidreza Abbasian Jahromi, Sayyed Mohammad Mirhosseini
et al.
In order to choose the method of project implementation in the form of partnership or conventional method, various factors in the formation of the concept of value for money creation in each project are evaluated to be the basis for decision making. Many countries use Public Sector Comparators (PSC) to reach this decision. In this research, the correct calculation of PSC and simulation of risks to achieve a negotiable price range in water and wastewater projects in Iran has been done. The data collection tool in this study was to review various articles to identify the types of risks and distribute questionnaires and interviews with experts in the water and wastewater industry in order to determine the main and effective risks and then, the occurrence and severity of the effects of each risk. The price range was determined using the Monte Carlo simulation. After determining the main risks on PSC, using Monte Carlo method and risk distribution functions, the minimum and maximum amount of each risk and the total risk were determined for 70%, 80% and 90% confidence coefficients. According to the obtained model, to determine the price range, the price presented in the case study should be 500% to 550% in the minimum case and 750% to 850% increase in the maximum case for different reliability coefficients. As a result of this study, inflation risks, exchange rate fluctuations, regional political instability, public and private sector corruption have had the greatest impact on the PSC and price range determination.
Technology, Water supply for domestic and industrial purposes
Mohammad Parvaresh, Nasser Mehrdadi, Abdolreza Karimi
et al.
Urban governments face a number of challenges today related to water governance. As a result, a variety of water governance models have emerged involving governments, public-private partnerships, and private firms. Using a descriptive model, this paper describes how water and wastewater companies in integrated urban management are modeled, and then it suggests how the optimal type of water governance is established in Iran. This study analyzed all parameters and effective variables in continuous management of Iran's water resources, along with the set of laws and regulations that govern upstream activities and the authorities of the governing bodies. Based on institutional mapping, four scenarios were created for dividing responsibilities between government, municipality and private sector in water governance, including governmental planning, semi-centralized governance with the central government, semi-centralized governance with municipalities and decentralized governance based on local government mapping (municipalities). On the basis of the considerations, advantages, and disadvantages of every scenario, scenario 2 has been proposed as the most desirable scenario for application under the country's circumstances.
Technology, Water supply for domestic and industrial purposes
Bita Roohi Asl, Mahnaz MirzaEbrahimTehrani, Alireza Estelaji
et al.
Urban floods have been exacerbated by climate change, urbanization, and limited drainage of urban infrastructure. Over the past decades, they had many negative effects, including the vulnerability of key centers. The vulnerability of key urban centers through man-made hazards and natural disasters causes their inefficiency, intensifies public dissatisfaction and lack of service in accidents. In order to make key centers resilient, it is necessary to identify important centers and examine their vulnerability to various hazards and threats. Criteria and sub-criteria for grading and evaluation of assets were weighted by AHP technique in Expert Choice software and then the key centers of the city were identified. Intra-Urban and extra-Urban hydrology and modeling of rivers in Hamadan in different return periods were studied by using HEC-RAS software. Next, the results were transferred to GIS and flood risk zoning of Hamadan was determined. After entering the average sample comments in Expert Choice software, the weight of each index was determined separately, which shows that the quantitative level of utilization index has the highest weight and the economic value of the asset has the lowest weight. Finally, with the adaptation of key centers and flood risk zones in GIS, vulnerable centers were identified.
Technology, Water supply for domestic and industrial purposes
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user's pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https://github.com/akaxlh/CRANet.
The global challenges that face sustainable sanitation services in developing countries are the lack of fecal sludge (FS) management; this is due to the rapid urbanization and population growth as it generates enormous quantities of fecal sludge. The extensive use of unimproved sanitation technologies is one of the main reasons for environmental and public health concerns. In dispersed rural areas, isolated slums or in urban areas where a sewerage system is costly, a decentralized wastewater system can be used. Therefore centralized management of decentralized wastewater systems along with proper institutional framework treatment of fecal sludge can be used to enhance the economies of developing countries from resource recovery. The discovery of new ways to inactivate pathogens contained in human waste is key in improving access to sanitation worldwide and reducing the impact of conventional waste management processes on the environment. The entire FS management system should include on-site sanitary treatment methods, collection, and transportation of FS, treatment facilities as well as resource recovery or disposal of the treated end products. This review paper addresses the hygienization of fecal sludge and improved treatment technologies for safe reuse or disposal of the end products and the significant economic revenues attained from the treatments of fecal sludge.
The absence of a wastewater collection, management, and disposal scheme is one of Kabul’s most serious environmental issues. This has resulted in both health and ecological problems. This research used Arc GIS and SewerGEMS tools to assess the viability of a decentralized sewerage collection model in the research area. The research area was chosen as the city’s 5th district. Land-use and land-cover, Digital Elevation Model (DEM), and Satellite data were used to construct the network’s geometry in the Arc map environment. SewerGEMS software was used to perform hydraulic simulation and modeling. The variables were regulated based on the results of the study using conventional wastewater topology guidelines. Based on the outputs of hydraulic analysis, it is concluded that the decentralized wastewater collection system would be the best option for the area. It can be deduced from hydraulic design findings that the hydraulic model was successfully developed and built. The methodology can be applied for the development of future wastewater master plans of the city.
A. Olorunfemi, A. B. Alao-Daniel, T. Adesiyan
et al.
119 Ife Journal of Science vol. 22, no. 3 (2020) INTRODUCTION Solid waste collection and disposal have always being a universal problem. However, developed countries have found a way around their solid waste management problems while developing countries are still battling with theirs (Arneth et al., 1989; Adewole, 2009; Ogunseiju, 2014). Waste is most of the time contained in landfills with impermeable bottom liner and leachate collection centers to prevent environmental pollution in the developed countries, while open dumps are the most common methods of waste disposal in developing countries of the world. The reason why the open dump systems of waste management are being operated in developing countries is due to technical, financial, complex and the expensive engineering system involved in setting up a sanitary landfill (Ogunseyiju, 2014). Large population and obsolete techniques used in waste management complicate the problem in developing country. Waste disposal in landfills is an integral part of waste management strategy around the world. The production of by-products of organic and inorganic decomposition known as leachates in landfills pose a serious threat if released to the environment (Arneth et al., 1989; Kjeldsen, 1993). Physical, chemical and microbial processes are usually involved in the release of pollutants from the wastes into the environment (Kimmel and Braids, 1974). Leachates when released into the underlying groundwater forms a contaminant plume that can alter the physical, chemical and microbial properties of groundwater (Baedecker and Back; 1979; Ogunseiju, 2014). The result of mass transfer processes of contaminants into the leaching water percolating through the waste layer is described as leachate pollutant. Solid waste and sewage generation and their poor disposal mechanism in urban areas of most developing countries have become an inherent threat to the environment. Open dumps are usually sited in reserved areas such as outskirts of towns far from residential areas, but with the upsurge in cities population as a result of ruralurban migration, these reserved areas are being https://dx.doi.org/10.4314/ijs.v22i3.10
Malihe Eskandary, MohammadTaghi Taghavifard, Iman Raeesi Vanani
et al.
One of the pillars of any country’s development is access to safe water and sanitation, so it is important to execute water and wastewater projects in the shortest possible time. In this regard, considering the emergence of various methods of partnership, choosing the right approach has become one of the most important issues in this industry. Therefore, a proper investment method in this field has always been the concern of decision makers. Using the database of partnership projects and data mining algorithms in the water and wastewater sector, we have designed a model to predict a proper way for public-private partnership projects. In this research, CRISP data mining method was applied to the data from 176 projects. After understanding and identifying the data, they were cleaned by deleting outliers and noisy data, and missing values were replaced. Then, the process of data classification was performed using decision tree and machine learning algorithms, and necessary analysis was performed. Also, the indicators of PPP were extracted and prioritized. K-fold cross validation method is used for validation and dividing the data. Based on the modeling and considering the data preparations and data mining methods, the stacking method is suitable for predicting and determining the type of public-private partnership contract in the implementation of each project of water and wastewater industry, which has an accuracy of 86.27%. In the pre-processing section, the combined COF method for deleting outliers and entropy factors for feature selection was used. Using the model, the success rate of each project can be predicted and an appropriate PPP contractual template for any water and wastewater project can be proposed. In addition, by entering the information of each new project, the impact of the improvement of each indicator can be easily examined.
Technology, Water supply for domestic and industrial purposes
Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the predictions. We design a classifier using Inductive Conformal Prediction (ICP) based on a triplet network architecture in order to learn representations that can be used to quantify the similarity between test and training examples. The method allows computing confident set predictions with an error rate predefined using a selected significance level. A feedback loop that queries the sensors for a new input is used to further refine the predictions and increase the classification accuracy. The method is computationally efficient, scalable to high-dimensional inputs, and can be executed in a feedback loop with the system in real-time. The approach is evaluated using a traffic sign recognition dataset and the results show that the error rate is reduced.
The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency's nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel nadir constraint from the swing equation that, for the first time, provides a framework for the optimal comparison of all these services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone, resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to £559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.