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Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings' heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R² and RMSE). Model performance of PSO-MLP is shown by R² amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R² amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. [ABSTRACT FROM AUTHOR]

Nanofluids are great heat transfer carriers for collecting thermal energy in solar thermal applications. In the present study, a theoretical study of single‐slope solar still (passive type) has been carried out by incorporating CuO, Al2O3, Ag, Fe2O3, and SiC‐water nanofluids at different volume concentrations (0.02, 0.05, 0.08, 0.12, and 0.2). This analysis has been carried out with an optimum water depth of 0.02m as obtained from the experimental and theoretical studies. In order to validate the model, the experiments were conducted on solar still and then performance of still was compared. The analytical expression of the characteristic equation using Runga‐Kutta ODE, for passive single slope solar still was found to be in good agreement with experiments carried out in Patiala, India. The total deviation for both experimental and theoretical distillate output of a still for a day was found to be 12.24%. Daily production for Al2O3‐water‐based nanofluid was found to be (14.22%) higher than simple solar still without nanofluid, followed by CuO (10.82%), Ag (8.11%), Fe2O3 (7.63%) and SiC (7.61%). [ABSTRACT FROM AUTHOR]

This research manuscript addresses the study of the performance of a flat tube having a 90° bend under the flow of three different nanofluids such as copper oxide, multiwalled carbon nanotubes, and aluminum oxide/water nanofluids at different inlet fluid temperatures and Reynolds numbers. The performance of the flat tube is analyzed under the Reynolds number between 5000 and 11000 and a fluid inlet temperature range of 35°C–50°C. The results obtained in this study show that the heat transfer coefficient increases with the increase in volume concentration as well as Reynolds number. The maximum heat transfer coefficient is obtained using multiwalled carbon nanotubes followed by copper oxide and then aluminum oxide. This study also illustrates that the friction factor increases with the increase in volume concentration and decrease in Reynolds number. The results of the numerical study have been validated with the help of an experimental study. The study has proved that the use of nanofluids instead of the conventional fluid can lead to reducing the size of the tube for the same amount of heat transfer which can prove the reduction of the size in heat transfer equipment. Furthermore, it is also observed in this study that the presence of the 90° bend in the flat tube improved the heat transfer performance due to the increased turbulence at the bent section of the tube. [ABSTRACT FROM AUTHOR]

Anthropogenic emissions, such as industrial, vehicular, biomass burning, and coal combustion, play a significant role in degrading the atmospheric conditions of India. Therefore, in the present study, applicability of the photocatalytic-concrete blocks was estimated in improving the ambient environment of Chandigarh, India. The photocatalytic-concrete blocks were prepared by mixing the TiO2 particles with cement. All the experiments, designed in accordance with the Box–Behnken approach, in combination with response surface methodology, were performed in a batch reactor. Further, the process parameters, namely, concentration of TiO2 (1 to 5 g), UV-A irradiance (1 to 5 mW/cm2), and relative humidity (RH) (10 to 70%), were optimized to achieve maximum degradation of NO2. Outcomes of batch experiments depicted that the maximum degradation of NO2, that is, 68.32%, was attained at 3.35 g of TiO2, 5 mW/cm2 of UV-A irradiance, and 64.60% RH. The findings of batch experiment were further theoretically applied to degrade the ambient NO2 concentration of Chandigarh, India. It was estimated that using the photocatalytic concrete for construction of Chandigarh's pavements may reduce the ambient NO2 concentration of Chandigarh, India, to an average of 5.80 μg/m3. Afterwards, reusability of photocatalytic-concrete blocks was also assessed, and it was made evident that after five cycles, their efficiency was reduced by only 7.15%. Subsequently, it was revealed that hydrogen peroxide-based treatment of photocatalytic-concrete blocks could completely regenerate its treatment efficiency. Therefore, it is expected that the findings of this study may prove beneficial in urban planning, as it may assist scientific auditory in identifying the applicability of TiO2-based photocatalysis in mitigating the impacts of vehicular emissions. [ABSTRACT FROM AUTHOR]

In the preceding times, the number of enclosed parking garages has increased significantly in developing nations. The toxic emissions from vehicular exhausts are expected to drastically compromise the environmental conditions of the parking garages. Subsequently, exposure of humans to these accumulated pollutants is also expected to degrade their health. Therefore, in the present investigation, efforts were made to estimate the applicability of TiO2-mediated UV photocatalysis in degrading the concentration of vehicular emissions, viz., NOx and SO2, in the enclosed parking garages (EPGs). In this regard, an artificial EPGs' environment was created and experiments were designed using the Box-Behnken design in combination with response surface methodology. The process parameters chosen for maximizing the degradation of the pollutants were a concentration of TiO2 emulsion (20 to 120 ml/m2), UV irradiance (1 to 5 mW/cm2), and relative humidity (10 to 50%). Optimization of the laboratory experiments revealed that at optimal conditions of the process parameters, i.e., a concentration of TiO2 emulsion = 77.50 ml / m 2 , intensity of UV irradiance = 3 mW / c m 2 , and relative humidity = 43.2 % , maximum degradation of the NOx and SO2, i.e., 61.24% and 55.05%, respectively, was achieved. Further, it was revealed that relative humidity may prove to be the limiting factor while using the TiO2-mediated UV photocatalysis in humid areas. Findings of this study may prove beneficial in urban planning as it may assist scientific auditory and local authorities in identifying the applicability of TiO2-based photocatalysis in mitigating the impacts of vehicular emissions. [ABSTRACT FROM AUTHOR]