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With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.

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The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

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Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

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Educational buildings have a large share and impact on urban development. While research shows a significant portion of non-industrial energy consumption in these buildings, obtaining optimal thermal comfort in educational buildings remains one of the main concerns in achieving the grounds to promote students' best performance and efficiency. Extensive research has been done in this field, however, this research presents a new approach to the diverse use of nanotechnology techniques which improve its properties and components in the buildings, aiming to reduce energy consumption and increase thermal comfort. In this paper, thermal comfort and energy consumption are evaluated in a 12-class elementary school located in Shiraz City. Aeropan and nano-Phase change materials (nano-PCMs) is used in the window glass and walls of the studied case. This evaluation presents the simulation and experimental analysis of thermal comfort (PMV) and energy consumption of three classroom alignments in the school building including the Linear-shape (LS), the Integrated Linear-shape (ILS), and the U-shaped (US) alignment. The simulation was performed using EnergyPlus 9.6 software, while the experimental data was collected using TESTO 425 device. The result of this research shows that after applying nano-PCM and Aeropan techniques in window glass and walls, the US alignment has the highest reduction in energy consumption (monthly average of 11.80%) compared to LS and ILS alignments. This alignment includes an energy consumption reduction of 12.03% in the coldest, and, 11.66% in the hottest day of the year in addition to increasing the monthly average thermal comfort of school by the use of nanomaterials.

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In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.

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BACKGROUND: Validated and internationally standardised measurement instruments are a prerequisite for ensuring that physical activity during pregnancy is comparable and for deriving physical activity recommendations. In Germany, there has been no adapted version of the internationally used Pregnancy Physical Activity Questionnaire (PPAQ) until now. This study's aim centred around translating the original English version into German (PPAQ-G) and determining its reliability as well as validity in a German population. METHODS: The PPAQ was translated into German using the forward-backwards technique. Its reliability and validity were tested. Thirty-four correctly completed questionnaires were analysed. The test-retest reliability was presented using the intraclass correlation coefficient (ICC) and Spearman correlation coefficient. Validity was tested by using accelerometer (n = 23) and determined by Spearman correlation coefficient. RESULTS: In the transcultural adjustment, two questions were amended to describe intensity more precisely, and two other questions were adapted to reflect the units of measurement used in Germany. The ICC indicated a reliability of r = 0.79 for total activity (without sitting), and the intensity subcategories ranged from r = 0.70 (moderate-intensity activities) to r = 0.90 (sitting). Although, validity assessment showed no significant correlation for sedentary, moderate or vigorous intensity, there were significant correlations for total activity (light and above; r = 0.49; p < 0.05) and for light activity (r = 0.65; p < 0.01). CONCLUSIONS: The PPAQ-G showed good reliability for use on pregnant German women and a moderately accurate measurement of physical activity. It can be used nationally for epidemiological studies, and it also enables international comparisons of physical activity during pregnancy. TRIAL REGISTRATION: DRKS00023426; Registration date 20 May 2021.

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The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17-40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

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The need to incorporate renewable energy generators (REGs) into the electrical grid has become increasingly crucial due to the push for a more sustainable environment. This study advocates an innovative strategy for optimizing inertia-integrated generation and transmission expansion planning (GTEP) to implement feed-in tariffs (FiT). The application of the GAMS CPLEX solver to the model, which tested on an IEEE 6/IEEE 16 system, reveals that using FiT results in a 12.1 % drop in system cost ($599 million to $526 million) and a 7.91 % rise in total system inertia. Sensitivity analysis highlights the correlation between increased REG integration and FiT payment reduction at 50 % penetration. The model outperforms soft computing optimization techniques, showcasing rapid convergence and computational efficiency. The proposed model's validated superiority in rapid convergence and computational efficiency is demonstrated by comparing its results with those obtained from other soft computing optimization techniques.

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This study examined the eradication of Tetracycline hydrochloride (TCH) antibiotic, an emerging pollutant, by utilizing eggshell membrane activated carbon (EMAC) and magnetite (Fe3O4) nanocomposite in conjunction with the electroperoxone process employing the One Factor at a Time method (OFAT) in a baffled reactor. The nanocomposite was synthesized through the hydrothermal method using an autoclave, and its properties were assessed via XRD, FTIR, FESEM, EDAX Mapping, BET, and VSM analyses. The findings revealed that under optimal conditions (including a pollutant concentration of 300 mg/L, a natural pH of 6.2, an ozone consumption rate of 0.28 g/h, a nanocomposite concentration of 0.2 g/L, a flow intensity of 0.5 A, a wastewater recirculation flow rate of 8 L/h, and a 0.1 M Na2SO4 electrolyte concentration), 95.9%, 76.4%, and 53.4% of pollutants, COD, and TOC were respectively eliminated after 90 min. Additionally, the reusability of the nanocomposite was evaluated over five usage periods, during which the process efficiency decreased from 95.9% to 83.1%. In short, this study proved that EMAC/Fe3O4 nanocomposites are promising electroperoxone catalysts due to their low cost, excellent stability and reusability, environmental compatibility, and superior catalytic activity for TCH antibiotics removal.

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This study investigated the effects of microwave-assisted freezing on the quality attributes of button mushrooms (Agaricus bisporus). Four levels of microwave power (0, 10, 20, 30%) were applied to the mushroom samples during freezing. The quality attributes of the frozen and thawed mushrooms were then evaluated. The results suggested that higher microwave power produced the smaller and more uniform ice crystals. Moreover, the browning index of the mushroom samples increased with increasing microwave power. The textural properties (hardness) of the mushrooms were also affected by the microwave power, showing higher values as the power increased. Furthermore, the ratio of the microwave operating system's power to the freezer power was low and approximately 20% at the highest power level. Therefore, these findings confirm the potential of microwave-assisted freezing for reducing freeze damage to mushroom tissue and, thus, provide frozen mushroom with a better texture.

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Electro-oxidation (EO) technology demonstrates significant potential in wastewater treatment. However, the high energy consumption has become a pivotal constraint hindering its large-scale implementation. Herein, we design an EO and 4-electron oxygen reduction reaction coupled system (EO-4eORR) to replace the traditional EO and hydrogen evolution reaction (HER) coupled system (EO-HER). The theoretical cathodic potential of the electrolytic reactor is tuned from 0 V (vs. RHE) in HER to 1.23 V (vs. RHE) in 4eORR, which greatly decreases the required operation voltage of the reactor. Moreover, we demonstrate that convection can improve the mass transfer of oxygen and organic pollutants in the reaction system, leading to low cathodic polarization and high pollutant removal rate. Compared with traditional EO-HER system, the energy consumption of the EO-4eORR system under air aeration for 95% total organic carbon (TOC) removal is greatly decreased to 2.61 kWh/kgTOC (only consider the electrolyzer energy consumption), which is superior to previously reported EO-based water treatment systems. The reported results in this study offer a new technical mode for development of highly efficient and sustainable EO-based treatment systems to remove organic pollutants in waste water.

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With the widespread application of deep learning technology in various fields, power load forecasting, as an important link in power system operation and planning, has also ushered in new opportunities and challenges. Traditional forecasting methods perform poorly when faced with the high uncertainty and complexity of power loads. In view of this, this paper proposes a power load forecasting model PSO-BiTC based on deep learning and particle swarm optimization. This model combines a temporal convolutional network (TCN) and a bidirectional long short-term memory network (BiLSTM), using TCN to process long sequence data and capture features and patterns in time series, while using BiLSTM to capture long-term and short-term dependencies. In addition, the particle swarm optimization algorithm (PSO) is used to optimize model parameters to improve the model's predictive performance and generalization ability. Experimental results show that the PSO-BiTC model performs well in power load forecasting. Compared with traditional methods, this model reduces the MAE (Mean Absolute Error) to 20.18, 17.57, 18.61 and 16.7 on four extensive data sets, respectively. It has been proven that it achieves the best performance in various indicators, with a low number of parameters and training time. This research is of great significance for improving the operating efficiency of the power system, optimizing resource allocation, and promoting carbon emission reduction goals in the urban building sector.

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OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system. MATERIALS AND METHODS: Energy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption. RESULTS: Our findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption. CONCLUSION: Implementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs. CLINICAL RELEVANCE STATEMENT: MRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact. KEY POINTS: Optimization of protocol settings reduced energy consumption by 31% and imaging time by 18%. DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol. During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.

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The circular economy practices contribute to sustainable development by maximising efficiency, utilising renewable resources, extending product lifespans, and implementing waste reduction strategies. This study investigates the individual impacts of four sources of the circular economy on the ecological footprint in Germany, a country that is among the pioneers in establishing a comprehensive roadmap for the circular economy. The four sources examined are renewable energy consumption (REC), recycling, reuse, and repair of materials. Using time series data from 1990 to 2021, the study employed the dynamic autoregressive distributed lag (ARDL) simulation technique and also applied kernel-based linear regression (KRLS) to test the robustness of the results. The findings revealed that reuse practices significantly reduce the ecological footprint in both the short and long run. REC and repair also substantially decrease the ecological footprint, as shown by the simulation analysis. Conversely, while recycling is generally considered crucial for minimising environmental impact, in this study, it was found to contribute to environmental degradation. This paradox may be attributed to the nascent state of the recycling industry and data limitations. The results from KRLS confirm the findings of the dynamic ARDL. It is recommended that policymakers develop measures that are appropriate, efficient, and targeted to enhance the role of each source of the circular economy in reducing the ecological footprint in Germany. The major limitation of the study is its reliance on the indirect measures of circular economy attributed to the non-availability of data on direct measures.

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Energy consumption in the building sector justifies the necessity of knowing the thermal comfort perception of vernacular and modern architectural types, based on which a correct recognition was reached for the design of buildings suitable for the climatic conditions of each region. It should be determined that the different types of modern and traditional architecture are in the comfort level in harsh hot climate conditions and how much energy they consume to reach the comfort level. Despite consideration of energy consumption and thermal comfort in different buildings in Iran, there is no clear framework for evaluating these two parameters in different buildings and comparing them. This research aims to compare the indoor thermal comfort levels of vernacular architectural buildings and modern buildings in Iran's semi-hot and dry climate at the peak of summer heat and determine their energy consumption to reach the comfort level. This study has been accomplished by collecting field data, examining the indoor predicted mean vote (PMV) index of the buildings, and comparing them. It was found that rock-cut architecture buildings are in better thermal comfort conditions without energy consumption due to the use of groundmass temperature and low heat exchanges between the indoors and outdoors because of the thermal phase of the materials and the thickness of its layers. The indoor PMV average of rock-cut buildings in summer is -0.61; in modern buildings, it is 0.77, while these two building complexes are in the same climate and close. Also, the energy consumption to reach the comfort level in rock-cut buildings is zero, while modern buildings consume an average of 7.7 kW of electricity daily. The research results will lead to recognizing and modeling the climate design of vernacular architecture, which can be used in today's architecture to reduce energy consumption.

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Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

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Trade policy uncertainty might hamper trade flow, including the trade of green and renewable energy technologies. Therefore, this study aims to examine the asymmetric effects of trade policy uncertainty (TPU) on renewable energy consumption (REC) in China. To calculate the short- and long-term relationships between REC, TPU, national income, carbon footprints, and financial development, we used the nonlinear QARDL technique. The estimates reveal that an upsurge in TPU hurts REC in the short and long run. Conversely, a stable trade policy or a reduction in TPU increases REC in the long run. In the short run, a fall in TPU exerts no influence on REC. The findings further imply that various factors, including GDP, CO2 emissions, and financial development, contribute to long-term improvements in REC in China, both in the short and long run.

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Under the "dual-carbon" strategic goals, it is urgent to examine whether the energy consumption permit trading scheme （ECPTS）, as an innovative system in China's market-oriented reform of the energy sector, can promote the synergistic enhancement of pollution reduction and carbon abatement. Based on provincial panel data of China from 2008 to 2019, this study adopted a difference-in-differences model to examine the impacts of the ECPTS on the synergistic enhancement of pollution reduction and carbon abatement. The results demonstrated that the ECPTS improved the level of pollution reduction and carbon abatement in pilot areas. Specifically, the ECPTS led to a reduction of 13.3% in CO2 emissions and 3.1% in PM2.5 concentration in the pilot areas and resulted in an overall improvement of pollution reduction and carbon abatement by 0.237 units. Mechanism analysis revealed that energy efficiency served as a pathway through which the ECPTS empowered the synergistic enhancement of pollution reduction and carbon abatement. Moreover, the strengthening of local government environmental protection goals enhanced the pollution reduction and carbon abatement effects of the ECPTS. Surprisingly, the effectiveness of the ECPTS was not undermined by the goal of economic growth. This study provides new empirical evidence for understanding the relationship between market-based environmental regulation and collaborative governance and provides strong support for China to achieve its "dual-carbon" strategic goals.

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CO2 capture from coal power plants is an important and necessary solution to realizing carbon neutrality in China, but CCS demonstration deployment in power sector is far behind expectations. Hence, the reduction potential of energy consumption and cost for CCS and its competitiveness to renewable powers are very important to make roadmaps and policies toward carbon neutrality. Unlike the popular recognition that capturing CO2 from flue gases is technically and commercially mature, this paper notes that it has been proved to be technically feasible but far beyond technology maturity and high energy penalty leads to its immaturity and therefore causes high cost. Additionally, the potential energy penalty reduction of capture is investigated thermodynamically, and future CO2 avoidance cost is predicted and compared to renewable power (solar PV and onshore wind power). Results show that energy penalty for CO2 capture can be reduced by 48%-57%. When installation capacity reaches a similar scale to that of solar PV in China (250 GW), CO2 capture cost in coal power plants can be reduced from the current 28-40 US$/ton to 10-20 US$/ton, and efficiency upgrade contributes to 67%-75% in cost reduction for high coal price conditions. In China, CO2 capture in coal power plants can be cost competitive with solar PV and onshore wind power. But it is worth noting that the importance and share of CCS role in CO2 emission reduction is decreasing since renewable power is already well deployed and there is still a lack of large-scale CO2 capture demonstrations in China. Innovative capture technologies with low energy penalties need to be developed to promote CCS. Results in this work can provide informative references for making roadmaps and policies regarding CO2 emission reductions that contribute towards carbon neutrality.

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This study presents the development, calibration, and validation of a mathematical model tailored for biological wastewater treatment at an actual urban sanitation facility. Utilizing multi-criteria optimization techniques, the research identified the most effective MCO algorithm by assessing Pareto optimal solutions. The model incorporated three primary performance measures energy consumption, overall volume, mean quality of effluent, and optimized 12 process parameters. Three algorithms, CRFSMA, particle swarm algorithm, and adaptive non-dominated sorting genetic algorithm III, were rigorously tested using MATLAB. The CRFSMA method emerged as superior, achieving enhanced Pareto optimal solutions for three-dimensional optimization. Quantitative improvements were observed with a 14.8 % increase in wastewater quality and reductions in total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and ammonium nitrogen ( N H 4 + - N ) concentrations by 0.95, 2.38, 0.04, and 0.14 mg/L, respectively. Additionally, the total cost index and overall volume were decreased, contributing to an 18.27 % reduction in overall volume and an 18.83 % decrease in energy utilization. The adapted anaerobic-anoxic-Oxic (A2O) framework, based on real-world wastewater treatment plants, demonstrated compatibility with observed effluent variables, signifying the potential for energy savings, emission reductions, and urban sanitation enhancements.