• Energy Research

Nowadays in India the renewable energy sources are continuum growing to accommodate the current demands of energy. Therefore, for an effective use of this energy, a careful and critical analysis is required. As per literature review, India was reported having a massive potential as superpower source in terms of wind energy In the present research, an effort has been carried out to explore various decision making approaches such as TOPSIS, VIKOR, and Fuzzy analysis, to subsequently rank various Indian states with respect to their wind energy potential. In this perspective, potentiality indices have been found on the justification of five significant factors that influence the effective use of wind energy and then a classification has been proposed. It was found that the wind power density is the most significant parameter while the technical expertise has been found as the least significant among identified parameters. The results presented here indicates that among all alternative states of India, Tamilnadu and Maharashtra have the maximum potential to tap the wind energy potential. This study will act as a guide for various government agencies to re-evaluate and re-formulate their energy policies as well as will help various investors (under the ‘Make in India’ campaign) orientated to do business here, to take a well informed decision. The present study also provides a way to make strong policies, in the area of high wind energy potential, in order to maximize the use of renewable source of energy which allows to tackle the societal need and poverty.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Geopolymer concrete, because of its less embodied energy as compared to conventional cement concrete, has paved the way for achieving sustainable development goals. In this study, an effort was made to optimize its quality characteristics or responses, namely, workability, and the compressive and flexural strengths of Ground Granulated Blast-furnace Slag (GGBS)-based geopolymer concrete incorporated with polypropylene (PP) fibers by Taguchi’s method. A three-factor and three-level design of experiments was adopted with the three factors and their corresponding levels as alkali ratio (NaOH:Na2SiO3) (1:1.5 (8 M NaOH); 1:2 (10 M NaOH); 1:2.5 (12 M NaOH)), percentage of GGBS (80%, 90%, and 100%) and PP fibers (1.5%, 2%, and 2.5%). M25 was taken as the control mix for gauging and comparing the results. Nine mixes were obtained using an L9 orthogonal array, and an analysis was performed. The analysis revealed the optimum levels as 1:2 (10 molar) alkali ratio, 80% GGBS, and 2% PP fibers for workability; 1:2 (10 molar) alkali ratio, 80% GGBS, and 2.5% PP fibers for compressive strength; and 1:2 (10 molar) alkali ratio, 80% GGBS, and 1.5% PP fibers for flexural strength. The percentage of GGBS was found to be the most effective parameter for all three responses. The analysis also revealed the ranks of all the factors in terms of significance in determining the three responses. ANOVA conducted on the results validated the reliability of the results obtained by Taguchi’s method. The optimized results were further verified by confirmation tests. The confirmation tests revealed the compressive and flexural strengths to be quite close to the strengths of the control mix. Thus, optimum mixes with comparable strengths were successfully achieved by replacing cement with GGBS and thereby providing a better path for sustainable development.

The large size of a glazed component allows greater access to natural light inside and a wider view of the outdoors while protecting the inside from extreme weather conditions. However, glazed components make buildings energy inefficient compared to opaque components if not designed suitably, and sometimes they create glare discomforts too. In order to protect against excessive natural light and direct sunlight and for privacy, dynamic shading devices are integrated into the glazed façade. In this study, the impact of various glazing and shading design parameters has been investigated by performing uncertainty and sensitivity analyses. The uncertainty analysis indicates that the variance coefficients for the source energy use, lighting energy use, useful daylight illuminance (UDI), and shade-deployed time fraction are in the ranges of 15.04–30.47, 39.05–45.06, 40.57–49.92, and 19.35–52%, respectively. The dispersion in the energy and indoor visual performance is evident by the large variation in the source energy consumption and UDI (500–2000), which vary in the ranges of 250–450 kWh/(m2-year) and 5–90%. Furthermore, a sensitivity analysis identified the window-to-wall ratio (WWR), aspect ratio (ASR), glazing type (Gt), absorptance of the wall (Aw), and shade transmittance (ST) as major influences of the parameters. Each of the identified parameters has a different proportionate impact depending on the façade orientation and performance parameters.

Preservation of potatoes in a controlled cool environment (i.e., in cold storage) consumes a substantial amount of energy. The specific energy consumption in Indian cold storage has been estimated to be between 9 and 26 kWh/ton/year. In this article, the potential for minimizing the energy consumption in the refrigeration process of cold storage through passive cooling concepts (i.e., roof evaporative cooling and the earth integration of the storage building) was explored. These passive concepts of cooling have shown significant potential for lowering the cooling loads and the energy consumption in different types of buildings. Therefore, a feasibility analysis for a potato storage building, considering the effect of the passive cooling concepts, was conducted for three different climatic conditions (i.e., hot–dry, warm–humid, and composite) in India. The energy saving potentials in the cold storage were assessed by quantifying the thermal energy exchange between the indoor and outdoor environments using the modified admittance method. The effect of heat transfer through the building envelope on total energy consumption was estimated for the building having various sunken volumes (buried depths) without and with roof evaporative cooling. Further, the economic feasibility of adopting passive concepts was assessed in terms of life cycle saving compared to the base case. Results indicate that earth coupling without and with evaporative cooling has substantial potential to reduce the cooling load and can produce significant savings.

In India, a densely populated country, fossil fuel depletion affects the energy sector that fulfils the industrial and human needs. Concerning greenhouse gas emissions and pollutants, and sustainability, there is a great demand to search for alternate feedstocks to produce alternate fuels at a low cost. The present work focuses on waste coconut and fish oil as potential inexpensive feedstock for biodiesel production. Two-stage transesterification processes for biodiesel production from hybrid oils mixed in a 1:1 volume ratio by employing solid nano-catalyst Magnesium Oxide (MgO). Response surface methodology (RSM) was used to analyze the effects of the physics of transesterification variables, such as methanol-to-oil molar ratio (M:O), MgO catalyst concentration (MgO CC), and reaction temperature (RT), on biodiesel yield, based on experimental data gathered in accordance with the matrices of central composite design (CCD). MgO CC showed the highest contribution, followed by M:O and RT, to maximize biodiesel yield. All interaction factors showed a significant effect except the M:O with RT. Grasshopper optimization algorithm (GOA) determined optimal conditions (M:O: 10.65; MgO CC: 1.977 wt.%; RT: 80 °C) based on empirical equations, resulting in maximum biodiesel yield conversion experimentally equal to 96.8%. The physical stability of the MgO nano-catalyst and reactivity up to 5 successive cycles can yield 91.5% biodiesel yield, demonstrating its reusability for sustainable biodiesel production at low cost. The optimized biodiesel yield showed better physicochemical properties (tested according to ASTM D6751-15C) to use practically in diesel engines.

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.