Operation Of System Research Articles

Simulation and Mitigation of False Data Injection Cyberattacks on a Space Nuclear Reactor Power System

This paper investigates the response of the DynMo-CBC space nuclear reactor power system to simulated cybersecurity attacks during a startup transient and demonstrates the effectiveness of the mitigation measures. The system nominally generates 134 kW(electric) continuously for 12 years and does not have a single-point failure in reactor cooling and energy conversion. The reactor core is divided into three hydraulically independent sectors, each having a separate loop with a single shaft, closed Brayton cycle (CBC) turbomachinery unit. A He-Xe gas mixture with a molecular weight of 40 g/mol cools the reactor core sectors and is the CBC unit’s working fluid. This paper examines the effects of simulated false data injection attacks (FDIAs) on the operation parameters of the power system. The simulated FDIAs decrease or increase the external reactivity insertion beyond nominal to cause spikes in the reactor’s power and temperatures. The results demonstrate the effectiveness of the programmable logic controller regulating the control drums’ drive motors. It mitigates the effects of the simulated FDIAs on the transient operation of the power system and shortens the recovery time after the termination of the simulated cyberattacks.

Integration of Legacy Industrial Equipment in a Building-Management System Industry 5.0 Scenario

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost.

Survival of out-of-hospital cardiac arrest patients admitted to the hospital during the Tokyo Summer Olympic and Paralympic Games in Japan

BackgroundThe influence of the Tokyo Summer Olympic/Paralympic Games on normal emergency medical system operations in Japan had not yet been fully elucidated. In this study, we examined whether out-of-hospital cardiac arrest (OHCA) patients treated during the Tokyo Olympic/Paralympic Games had differences in outcomes. MethodsUsing the nationwide JAAM-OHCA Registry, we evaluated the outcomes of OHCA patients admitted to the hospital during the Tokyo Olympic/Paralympic Games (July 23 to Aug. 8 and Aug. 24 to Sept. 5) in 2021, compared to those during same the dates in 2020 (Term 1: July 23 to Aug. 8 and Aug. 24 to Sept. 5), those during the pre-Olympic/Paralympic term during the same weekdays in the weeks before the event (Term 2: June. 18 to July. 4 and July. 6 to July. 18), and those during the post-Olympic/Paralympic term during the same weekdays in the weeks after the event (Term 3: Sept. 10 to Sept. 26 and Sept. 28 to Oct. 10). The primary outcome was 30-day survival, and multivariable logistic analysis was performed, adjusted for age and sex. ResultsA total of 3,111 OHCA patients were included in the study period (786 in the Olympic/Paralympic group, 774 in Term 1, 747 in Term 2, and 804 in Term 3). Crude 30-day survivals were 7.4% (58/786), 9.3% (72/774), 6.8% (51/747), and 8.2% (66/804), respectively. Using the Olympic/Paralympic group as a reference, multivariable logistic analysis revealed that 30-day survivals in Term 1 (OR 1.27 95% CI 0.88–1.83p = 0.20), Term 2 (OR 0.92 95% CI 0.62–1.36p = 0.67), and Term 3 (OR 1.10 95% CI 0.76–1.59p = 0.63) did not differ significantly. ConclusionsNo significant differences in 30-day survival for OHCA patients admitted during the Tokyo Summer Olympic/Paralympic Games were identified.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

Analysis of Digitalization Transformation in AirAsia

Digitalization transformation has been allowing airlines to vastly increase their productivity and profitability, and pioneer airlines have demonstrated a strong interest in applying digitalization to their booking system, membership management, and business operations. It is then necessary to analyze how those technologies can benefit budget airlines and how they can be better facilitated to enhance the competitiveness of the airlines. This paper studies the application of digitalization transformation adopted by budget airline AirAsia and how it influences and impacts its performance financially and operationally. The analysis was conducted to understand how AirAsia has done to transform its business into digitalization, including the aspects of both customers perspectives and internal operations and its corresponding performance based on those techniques. The result shows that digitalization is necessary and vital to enhancing its profitability, competitiveness, and innovation, and we propose that AirAsia could further utilize digitalization by enhancing the ticketing and booking system, customer experience, and working process.

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots

In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

Temperature dependence of Fe:ZnSe mid-infrared spectral and laser output properties under ∼4 µm radiation excitation

This paper presents the temperature dependence of the spectroscopic and laser properties of the Fe2+:ZnSe single crystal under excitation by radiation at a wavelength of ∼4.04µm. Excitation was supported by the Er:YAG laser (∼2.94µm) operated in the Q-switched or free-running (FR) mode that pumped the Fe:ZnSe laser cooled by liquid nitrogen to 78 K. This system generated radiation at the wavelength of ∼4.04µm. Temperature dependence of absorption, fluorescence spectra, and fluorescence decay time of Fe2+ ions was measured. The optimal temperature that allows efficient Fe:ZnSe laser system operation with maximum laser output energy was sought in both regimes. It was characterized at a repetition rate of 1 Hz with pulse durations of ∼175ns and ∼155µs in both modes, respectively. The highest laser output energies obtained with the crystal placed in the cryostat in gain-switched (GS) and FR modes were ∼385µJ at 260 K and ∼374µJ at 190 K, respectively. Maximum optical-to-optical efficiencies in the same modes were ∼13% at 260 K and ∼32% at 170 K, respectively. Wide laser radiation oscillation spectra depending on the temperature ranging from ∼4.3µm at 140 K to ∼4.9µm at 340 K obtained with a Gaussian-like beam spatial profile were demonstrated. Under atmospheric conditions at RT, this laser system reached a pulse energy of over 0.5 mJ at ∼4.78µm in ∼135ns pulses, which corresponds to a peak power of ∼3.9kW.

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

Developing a new adaptive heat balance model to enhance thermal comfort predictions and reduce energy consumption

Accurate thermal comfort prediction is essential for enhancing both thermal comfort and energy efficiency in buildings. The heat balance and adaptive models, while widely used, have been often questioned in relation to their strengths and limitations. To overcome these challenges, a comprehensive understanding of both the physical parameters influencing heat exchange and occupants’ adaptive capacities is essential.This study introduces an analytical method to formulate a new adaptive heat balance model, the adPMV, which integrates the strengths of both models considering various parameters influencing thermal perception. The model integrates the PMV with an adaptive factor associated with the running mean outdoor temperature, in line with adaptive theory.Tested on 1377 samples from European university classrooms, the adPMV demonstrates enhanced accuracy (MAE=0.74, RMSE=1.01, MBE=-0.11) compared to PMV and other adaptive heat balance models. Validation on naturally ventilated university classrooms from ASHRAE’s databases further confirms promising results, showcasing reduced error indices (MAE=0.68, RMSE=0.81, MBE=-0.03). Notably, using adPMV setpoints not only improves thermal sensation prediction accuracy but also leads to a substantial reduction in heating demand, reaching up to 40 %.The adaptability of this model to different contexts, such as building types, climates, and HVAC system operations, presents it as a versatile tool for exploring adaptive principles.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Numerical investigations on electrolytic performance of proton exchange membrane electrolysis cell with parallel flow field

In this study, a three-dimensional simulation model of PEMEC with parallel flow field was established, the heat and mass transfer, electrochemical reactions were described to investigate the electrolytic performance. The distribution characteristics of temperature, liquid water and gas products in the electrolysis cell were obtained. The results show that during the operation of PEMEC system, the liquid water content decreases sequentially along the flow channel direction, while there is a significant temperature gradient in the flow direction of the electrolysis cell. The increase of the initial temperature can increase the current density of the electrolytic cell when the temperature increases from 60 ℃ to 80 ℃, the current density can be increases by 7.86 %, but it can also lead to the dehydration of PEMEC, resulting in thermal degradation of PEMEC, and further the reactivity and proton transfer performance were decreased. It is also found that the increase of liquid water inlet flow not only increased the reactant concentration, accelerated the chemical reaction rate, but also took away more heat generated by ohmic polarization as a coolant, which was conducive to the improvement of electrolytic performance. As the inlet flow rate increased from 1 mL/s to 3 mL/s, the current density at 2.4 V and 1.4 V can be increased by 3.66 % and 1.77 %, respectively, and the maximum temperature can be reduced by 7.4 K.

Dynamic performance of a supertall building with an active tuned mass damper system during Super Typhoon Saola

This paper investigates the dynamic characteristics (natural frequencies and damping ratios) of a 600-m-tall supertall building with an active tuned mass damper (ATMD) system during Super Typhoon Saola. Based on the wind speed, wind direction, and wind-induced acceleration response records by the structural health monitoring (SHM) system installed on the skyscraper, the wind characteristics during the passage of Saola are first investigated, and the relations between the mean wind speeds atop the skyscraper and the root-mean-square (RMS) acceleration responses are established. Then, employing the stochastic subspace identification (SSI) method and an uncertainty quantification method, this paper comprehensively investigates the uncertainty bounds, time-varying features, and amplitude-dependency characteristics of the modal parameters with and without the operation of the ATMD system. Next, on the basis of the identified modal parameters and time-frequency analysis, the performance and effectiveness of the ATMD system in suppressing the wind-induced vibrations of the skyscraper is investigated. Lastly, the structural health condition is evaluated from the perspective of building natural frequency and damping ratio changes before and after the typhoon hit. The goal of this study is to advance the understanding of the wind effects on supertall buildings and provide useful information for the wind-resistant design and vibration control of supertall buildings in tropical cyclone-prone regions.

Determining the optimal food hub location in the fresh produce supply chain

Purpose Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions. Design/methodology/approach Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches. Findings Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making. Practical implications This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers. Social implications The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs. Originality/value This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.

Differentiable programming for gradient-based control and optimization in physical systems

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

Optimization of indoor temperature sensor deployment in large spaces for multiple building operation scenarios using the genetic algorithm

Indoor Environmental Quality (IEQ) monitoring is significant for occupant comfort and system operation. Due to the complicated nature of the indoor environment, large spaces generally require an appropriate sensor deployment scheme for scientific, comprehensive, and cost-effective IEQ monitoring. However, most sensor deployment methods rely on manual experience without quantitative guidance. This study develops an optimization method for sensor deployment in large spaces by selecting air temperature as the targeted IEQ parameter. Three typical operation scenarios are considered separately and simultaneously for sensor deployment optimization. The effectiveness of the method has been demonstrated in an airport terminal. The results indicate that the proposed method outperforms the traditional methods in all scenarios. For each scenario, 20–30 (one per 300–450 m2) is the appropriate deployed number due to the marginal benefit decrease of additional sensors. Optimal deployed locations exhibit similarities and contradictions in different scenarios and trade-offs are required in a multi-objective optimization.

Decentralized coordinated optimal dispatching of multi-area interconnected electricity-gas-heat system based on inertia Bregman ADMM

The coordinated interconnection of multi-area interconnected electricity-gas-heat systems (MIEGHS) has become a typical feature of the modern energy internet. While enhancing system operational efficiency, it also introduces complexity and uncertainty into system operation. In consideration of these factors, a decentralized coordinated robust optimization scheduling framework for MIEGHS is proposed to facilitate coordinated optimization. Firstly, a two-stage robust stochastic optimal dispatching model for a single-area integrated electricity-gas-heat system (IEGHS) is established, considering the uncertainty of wind power output and load. Secondly, a decentralized self-regulation control model for MIEGHS is proposed based on the principle of consistency constraints. Next, considering both the timeliness and convergence of the traditional alternating direction method of multipliers (ADMM) in solving problems, an improved ADMM method based on inertial Bregman distance is presented for the optimized solution of the proposed model. Finally, the effectiveness and superiority of the presented model and methods are verified by three interconnected IEGHS.

Nonlinear Quadratic Programming Algorithm Modelling for Operation of The Off-Grid Energy Resources System with Backup Storage System

Nonlinear Quadratic Programming Algorithm Modelling for Operation of The Off-Grid Energy Resources System with Backup Storage System

Mapping the way: functional modelling for community-based integrated care for older people

BackgroundHealthcare system sustainability is challenged by several critical issues; one of the most pressing is the ageing population. Traditional, episodic care delivery models are not designed for older people who are medically complex and frail. These individuals would benefit from health and social care that is more comprehensive, coordinated, person-centred and accessible in the communities in which they live. Delivering this is a challenging endeavour. Community-based health and social care professionals are siloed, dispersed across various locations and sectors, each with their own mental models, electronic health information systems, and means of communication. To move away from fragmented care delivery models and towards a more integrated approach to care, an analysis of the process of community-based comprehensive geriatric assessment was conducted in an urban location in Atlantic Canada. The purpose of the study was to identify where in the community-based comprehensive geriatric assessment process challenges and opportunities existed for moving towards a more integrated model of care delivery.MethodThe functional resonance analysis method (FRAM) and dynamic FRAM (DynaFRAM) modelling were used to model the community-based health and social care system and create a hypothetical patient journey scenario. Data collected to inform modelling consisted of document review, focus groups, and semi-structured interviews with health and social care professionals providing care and service to older people in the community setting.Findings.Challenges and opportunities for implementing integrated care in the local context were identified. Findings from the FRAM and DynaFRAM analysis informed the co-design of multi-level process improvement recommendations that aim to move the local community-based comprehensive geriatric assessment process towards a more integrated model of care.ConclusionsA transformative redesign of community-based health and social care in the local context is necessary but cannot be accomplished without an understanding of how health and social care professionals conduct their work and how older people may receive care under the dynamic conditions. The FRAM and DynaFRAM modelling provided an enhanced understanding of system operations and functionality and demonstrated a critical step that should not be overlooked for decision-makers in their efforts to implement a more integrated model of care.

Optimized wastewater management utilizing multivariate statistical analysis: a case study of the Mascara wastewater treatment plant, Algeria.

Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management.