Search Results (220)

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

In Saudi Arabia’s hot and arid climate, residential buildings account for over half of national electricity consumption, with cooling demands alone responsible for more than 70% of this use. This paper explores the hypothesis that contemporary villa designs are inherently inefficient and that current building regulations fall short of enabling adequate thermal performance. This issue is expected to become increasingly significant in the near future as external temperatures continue to rise. The study aims to assess whether passive design strategies rooted in both engineering and architectural principles can offer substantial reductions in cooling energy demand under current and future climatic conditions. A typical detached villa was simulated using IES-VE to test a range of passive measures, including optimized window-to-wall ratios, enhanced glazing configurations, varied envelope constructions, solar shading devices, and wind-tower-based natural ventilation. Parametric simulations were conducted under current climate data and extended to future weather scenarios. Unlike many prior studies, this work integrates these strategies holistically and evaluates their combined impact, rather than in isolation while assessing the impact of future weather in the region. The findings revealed that individual measures such as insulated ceilings and reduced window-to-wall ratios significantly lowered cooling loads. When applied in combination, these strategies achieved a 68% reduction in cooling energy use compared to the base-case villa. While full passive performance year-round remains unfeasible in such extreme conditions, the study demonstrates a clear pathway toward energy-efficient housing in the Gulf region. Full article

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ($T_1$ and $T_2$) and flow rate (${\dot{V}}_1$) and cooling water inlet and outlet temperatures ($T_3$ and $T_4$) and flow rate (${\dot{V}}_3$), as well as chiller power consumption (${\dot{W}}_c$). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

This investigation attempts to develop a tower solar collector-based system designed for the cogeneration of cooling and desalination. The traditional organic Rankine cycle (ORC) integrated with the ejector refrigeration cycle generates limited power and cooling at a single temperature. Acknowledging their limitations, our present study uses an organic flash cycle (OFC) supported by solar heat combined with the two-phase ejector cycle and the reverse osmosis (RO) desalination unit. Since the OFC turbine is fed with two extra streams of fluid, therefore, it provides greater power to run the compressor of the ejector and pumps of the RO unit, resulting in the production of cooling at two different temperatures (refrigeration and air conditioning) and a higher mass flow rate of fresh water. A mathematical model is employed to assess the impact of coil curvature ratio, Rib height, and direct normal irradiation (DNI) on the temperature of the collector’s oil outlet. ANSYS-FLUENT conducts numerical simulations through computational fluid dynamics (CFD) analysis. The results indicate an ultimate increase in oil outlet temperature of 45% as the DNI increased from 450 to 1000 W/m² at a curvature ratio of 0.095 when employing the 1st Rib. Further, a steady-state energy and exergy analysis is conducted to evaluate the performance of the proposed cogeneration, with different design parameters like DNI, coil curvature ratio, rib height, and OFC turbine inlet pressure. The energetic and exergetic efficiencies of the cogeneration system at DNI of 800 W/m² are obtained as 16.67% and 6.08%, respectively. Exergetic assessment of the overall system shows that 29.57% is the exergy produced as cooling exergy, and the exergy accompanied by freshwater, 68.13%, is the exergy destroyed, and 2.3% is the exergy loss. The solar collector exhibits the maximum exergy destruction, followed by the ejector and RO pumps. Integrating multiple technologies into a system with solar input enhances efficiency, energy sustainability, and environmental benefits. Full article

When traditional HVAC (heating, ventilation, and air-conditioning) systems are in operation, they often run according to the designed operating conditions. In fact, they operate under part-load conditions for more than 90% of the time, resulting in energy waste. Therefore, studying the optimization and regulation of their operating conditions during operation is necessary. Given that the control set point for cooling tower outlet water temperature differentially impacts chiller and cooling tower energy consumption during system operation, optimization of this parameter becomes essential. Therefore, this study focuses on optimizing the cooling tower outlet water temperature control point in central air-conditioning systems. We propose the Hippopotamus Optimization Algorithm (HOA), a novel population-based approach, to optimize cooling tower outlet water temperature control points for energy consumption minimization. This optimization is achieved through a coupled computational methodology integrating building envelope dynamics with central air-conditioning system performance. The energy consumption of the cooling tower was analyzed for varying outlet water temperature set points, and the differences between three control strategies were compared. The results showed that the HOA strategy successfully identifies an optimized control set point, achieving the lowest combined energy consumption for both the chiller and cooling tower. The performance of HOA is better compared to other algorithms in the optimization process. The optimized fitness value is minimal, and the function converges after five iterations and completes the optimization in a single time step when run in MATLAB in only 1.96 s. Compared to conventional non-optimized operating conditions, the HOA strategy yields significant energy savings: peak daily savings reach 4.5%, with an average total daily energy reduction of 3.2%. In conclusion, this paper takes full account of the mutual coupling between the building and the air-conditioning system, providing a feasible method for the simulation and optimization of the building air-conditioning system. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Liquid atomization has wide applications in jet-type and reciprocating engines, powder generation, cooling towers, and atmosphere dust removal. Droplet size and distribution are the decisive factors in the performance of the above applications. The rapid development and usage of computer science brings huge differences in the research manner of liquid atomization and has shed great light on the micro-phenomena of the formation, deformation, and rupture of liquid ligaments. However, the numerical methods of liquid atomization still lack efficiency due to their huge cost of computer resources and their accuracy due to their dependence on empirical correlations. Before achieving reliable implementation in atomization device design, such computational models must undergo rigorous validation against experimentally measured data acquired through advanced diagnostic techniques. The present paper reviews the mainstream numerical methods of liquid atomization including interface capturing, particle tracking, smoothed particle hydrodynamics, etc. Their respective numerical kernels and some representative simulation cases are summarized. The aim of the present review is to provide a general idea and future research orientation on the capabilities of modern computer and numerical models in calculating atomization and designing relative devices and hopefully guide future research to strive efficiently and productively. Full article

To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts. Full article

The thermodynamic, exergy and carbon reduction potential of seawater shower cooling towers for central air conditioning systems was investigated under various geometric, physical and environmental conditions. A mathematical model of the shower cooling tower was proposed, and the governing equations considering droplet diameter changes were numerically solved during evaporation. The results showed that compared with the downward-spraying tower, the upward-spraying tower achieved 15.59% higher cooling efficiency, 4.89% higher exergy efficiency and 34.58% higher heat dissipation. Increasing droplet diameter significantly weakened the tower’s cooling capacity, with heat dissipation decreasing by 78.11% as the diameter increased from 1 mm to 2.25 mm. The cooling efficiency, thermal efficiency and exergy efficiency demonstrated consistent declining trends with increasing droplet diameter. Under high-salinity conditions (105 g/kg), compared with standard salinity (35 g/kg), the average reduction of cooling efficiency was 2.96%, and exergy efficiency decreased by 2.73%. The increase in air velocity from 2.5 m/s to 4.0 m/s led to a 13.76% improvement in cooling efficiency and a 22.44% increase in heat dissipation, and exergy efficiency decreased by less than 2%. Through multi-objective optimization analysis, the cooling efficiency increased by 14.69% and exergy destruction decreased by 37.87%, demonstrating significant potential for energy conservation and carbon emission reduction in central air conditioning applications. Full article

Relative to other renewable energy technologies, concentrated solar power (CSP) is only in the beginning phases of large-scale deployment. Its incorporation into national grids is steadily growing, with anticipation of its substantial contribution to the energy mix. A number of emerging economies are situated in areas that receive abundant amounts of direct normal irradiance (DNI), which translates into expectations of significant effectiveness for CSP. However, any assessment related to the planning of CSP facilities is challenging because of the complexity of the associated criteria and the number of stakeholders. Additional complications are the differing concepts and configurations for CSP plants available, a dearth of related experience, and inadequate amounts of data in some developing countries. The goal of the work presented in this paper was to evaluate the practical CSP implementation options for such parts of the world. Ambiguity and imprecision issues were addressed through the application of multi-criteria decision-making (MCDM) in a fuzzy environment. Six technology combinations, involving dry cooling and varied installed capacity levels, were examined: three parabolic trough collectors with and without thermal storage, two solar towers with differing storage levels, and a linear Fresnel with direct steam generation. The in-depth performance analysis was based on 4 main criteria and 29 sub-criteria. Quantitative and qualitative data, plus input from 44 stakeholders, were incorporated into the proposed fuzzy analytic hierarchy process (AHP) model. In addition to demonstrating the advantages and drawbacks of each scenario relative to the local energy sector requirements, the model’s results also provide accurate recommendation guidelines for integrating CSP technology into national grids while respecting stakeholders’ priorities. Full article

Ground temperature conditions are key factors affecting the stability of cast-in-place pile foundations for transmission towers in permafrost regions. With global climate warming, the ground temperature environment in permafrost regions has undergone significant changes, leading to an increasing risk of disasters for these pile foundations. However, research on the prevention and control of pile foundation diseases caused by permafrost degradation is relatively limited, and engineering practices are insufficient. To address this, this study proposes embedding a two-phase closed thermosyphon (TPCT) inside a concrete pile foundation to create a composite structural system with both load-bearing and cooling functions. A mathematical model is developed to focus on the cooling performance and temperature control efficiency of the composite structure. The results indicate that: (1) The TPCT can alleviate, to some extent, the downward shift of the permafrost table around the transmission tower foundation due to climate warming. The cooling effect of the TPCT slows the rate of permafrost degradation, but its control effect on the permafrost table is limited. (2) The performance of the cast-in-place piles with an embedded TPCT is closely related to temperature, with an effective operational period from early October to late March each year. (3) This device effectively mitigates the impact of permafrost degradation due to climate change, significantly lowering the risk of foundation-related issues in transmission towers. The findings of this study are crucial for maintaining ground temperature stability in cast-in-place pile foundations for transmission projects in high-latitude permafrost areas, as well as enhancing the theoretical framework for pile foundation design. Full article

This study provides fundamental data for optimal planning by analyzing key factors influencing microclimate and building energy loads. * to provide fundamental data for optimal planning. A total of 11 apartment layout types, including tower-type, flat-type, and mixed-type configurations, were analyzed using ENVI-met simulations. The results indicate that layout types significantly influence microclimate and energy consumption. Tower-type layouts enhanced wind flow, reducing surface temperatures and cooling loads. In contrast, dense flat-type layouts restricted airflow, leading to heat accumulation and increased cooling energy demand. Mixed layouts exhibited varied effects depending on the proportion of open spaces and high-density clusters. Additionally, south-facing layouts optimized solar radiation, reducing heating loads, whereas east–west-facing layouts experienced imbalanced solar exposure, increasing cooling demand by 15–20% in the summer. Horizontal parallel and staggered layouts improved ventilation efficiency and mitigated heat accumulation, making them effective strategies for enhancing microclimate and reducing energy consumption. This study confirms that apartment layout planning plays a crucial role in microclimate regulation and energy efficiency. The findings can guide architectural strategies to improve thermal comfort and reduce building energy consumption. Full article

In this study, a regression model was developed using the thermo-regulated residual refinement regression model (TRRM) analysis method based on three years and four months of in situ data collected from two water-cooled centrifugal chillers installed in A Tower, Seoul, South Korea. The primary objective of this study was to predict the coefficient of performance (COP) of water-cooled chillers under various operating conditions using only the chilled water outlet temperature ($T_2$) and the cooling water inlet temperature ($T_3$). The secondary objective was to estimate the COP under standard temperature conditions, which is essential for the absolute performance evaluation of chillers. The collected dataset was refined through thermodynamic preprocessing, including the removal of missing values and outliers, to ensure high data reliability. Based on this refined dataset, regression analyses were conducted separately for four cases: daytime (09:00–21:00) and nighttime (21:00–09:00) operations of chiller #1 and chiller #2, resulting in the derivation of four final regression equations. The reliability of the final dataset was further validated by applying other regression models, including simple linear (SL), bi-quadratic (BQ), and multivariate polynomial (MP) regression. The performance of each model was evaluated by calculating the coefficient of determination ($R^2$), coefficient of variation of root mean square error (CVRMSE), and the p-values of each coefficient. Additionally, the predicted COP values under the design and standard temperature conditions were compared with the measured COP values to assess the accuracy of the model. Error rates were also analyzed under scenarios where $T_2$ and $T_3$ were each varied by ±1 °C. To ensure robust validation, a final comparison was performed between the predicted and measured COP values. The results demonstrated that the TRRM exhibited high reliability and predictive accuracy, with most regression equations achieving $R^2$ values exceeding 90%, CVRMSE below 5%, and p-values below 0.05. Furthermore, the predicted COP values closely matched the actual measured COP values, further confirming the reliability of the regression model and equations. This study provides a practical method for estimating the COP of water-cooled chillers under standard temperature conditions or other operational conditions using only $T_2$ and $T_3$. This methodology can be utilized for objective performance assessments of chillers at various sites, supporting the development of effective maintenance strategies and performance optimization plans. Full article

Shells are significant structural components that are extensively utilized in numerous engineering fields, including architectural and infrastructural projects. These components are employed in the construction of domes, water tanks, stadiums and auditoriums, hangars, and cooling towers. Significant research efforts have been dedicated to the analysis of vibrations and dynamic behaviors of shells, due to their distinctive capacity to efficiently bear loads through their geometry rather than mass. Additionally, a vast array of shell theories and computational methods have been proposed and developed by researchers. This paper represents a continuation of research initiated begun in a 2009 paper by Elishakoff, wherein the suggestion was made to disregard an energetic term in the dynamic analysis of Timoshenko–Ehrenfest beams, wherein the suggestion was made to disregard an energetic term in the dynamic analysis of Timoshenko–Ehrenfest beams. The resulting reduced theory was found to be both more straightforward and more reliable than the complete, classical approach. While the original idea was heuristically justified, a more sound variationally consistent theory was proposed in the papers of De Rosa et al. concerning the dynamic analysis of the Timoshenko-Ehrenfest beams and later extended to the case of the Uflyand-Mindlin plates. In accordance with the proposal put forth in those works, we initially delineate the classical shell theory and subsequently propose two alternative hypotheses that give rise to two distinct aspects of the energy terms. By employing the variational approach, we derive two novel boundary problems, which are direct generalizations of those previously considered. Both theories can be readily specialized for beams and plates, and the theory can also be specialized for the case of cylindrical shells. Full article

The performance of pH controllers in industrial cooling towers is critical for maintaining optimal operational conditions and ensuring system efficiency. Industries such as the fertilizer, petrochemical, oil refinery, gas production, and power plant sectors rely on cooling towers, where poor pH regulation can lead to corrosion, scaling, and microbial growth. Traditional proportional–integral–derivative (PID) controllers are used for pH neutralization but often struggle with the cooling tower environments’ dynamic and nonlinear nature, resulting in suboptimal performance and increased operational costs. A hybrid particle swarm optimization (PSO) algorithm combined with a multiple adaptive neuro-fuzzy inference system (MANFIS) was developed to address these challenges. The MANFIS leverages fuzzy logic and neural networks to handle nonlinear pH fluctuations, while PSO improves the convergence speed and solution accuracy. This hybrid approach optimized the PID controller parameters for real-time adaptive pH control. The methodology involved collecting open-loop pH data, deriving the system transfer function, designing the PID controller, and implementing the PSO–MANFIS algorithm to fine-tune PID gains. Three tuning methods—MATLAB Tuner, MANFIS, and PSO–MANFIS—were compared. The findings proved that the PSO–MANFIS approach markedly enhanced the closed-loop efficiency by reducing overshoot and enhancing the dynamic response. These findings demonstrate that the PSO–MANFIS approach effectively maintains pH levels within desired limits, reduces energy consumption, and minimizes chemical usage and the risk of mechanical equipment damage. This study provided valuable insights into optimizing pH control strategies in industrial cooling tower systems, offering a practical solution for improving efficiency and reliability. Full article