Energy analysis and shadow modeling of a rectangular type salt gradient solar pond

In calculating the total solar energy input into a salt gradient solar pond, the current method is insufficient mainly caused by two problems. Firstly, the existing equations of solar pond energy analysis can be only used for momentary calculations but it is very time-consuming for long time periods. Secondly, the shading effect inside the pond affects significantly the energy storage performance of the pond, especially in small ones. To solve the first problem, the mean values of variable parameters during the concerned time period is proposed in this work and the ‘first mean value theorem for definite integrals’ is used to derive the average values. For the second problem, a rectangular pond with vertical walls is investigated as an example, and the exact sunny areas in different depths of the pond are calculated at different time conditions. The experimental data of a published study is used for the validation. The energy efficiency of the low convective zone of the experimental pond is calculated theoretically, which shows a good agreement with the experimental value. The experimental data and theoretical results for the energy efficiency are 9.68% and 11.38% for January, 17.54% and 18.92% for May, and 28.11% and 30.94% for August, respectively. The modified equations can be used to predict a pond performance before its construction

Novel ZnO-Ag/MWCNT nanocomposite for the photocatalytic degradation of phenol

A novel Ag-doped ZnO nanoparticle with different amount of multi-walled carbon nano tubes (MWCNTs) was developed, aiming to shift the band edge toward longer wavelength. New particles were produced in a simple wet synthesis method and assessed toward the removal of phenol under UV-A illumination. The photocatalysts were characterized by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM) with an energy-dispersive X-ray (EDX) spectroscopy analysis, transition electron microscopy (TEM), BET surface area analyzer, UV–vis diffuse reflectance spectroscopy (DRS) and photoluminescence spectroscopy (PL). The results indicated that all the samples containing MWCNTs exhibit higher photocatalytic activity than the bare ZnO and Ag doped ZnO nanoparticles. At a 10% MWCNT addition, the novel particle achieved a photocatalytic conversion rate of 81% in removing 100 ppm phenol under UV-A light irradiation after 240 min. The pH value, initial concentration and catalyst dosage were also found to influence the particle’s photocatalytic performance significantly

Thermodynamic assessment and multi-objective optimization of performance of irreversible Dual-Miller cycle

In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and ecological function (Eun) by means of NSGA-II technique and thermodynamic analysis are presented. The Pareto optimal frontier obtaining the best optimum solution is identified by fuzzy Bellman-Zadeh, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved. For the results of the condition that (n k) the best point has been LINMAP and TOPSIS answer. The thermal efficiency for this point has been 0.5385. Also, ECOP and Eun have been 1.6875 and 279.7315, respectively. Furthermore, the errors are examined through comparison of the average and maximum errors of the two scenarios

Thermodynamic and economic analysis of performance evaluation of all the thermal power plants : a review

Surging in energy demand makes it necessary to improve performance of plant equipment and optimize operation of thermal power plants. Inasmuch as thermal power plants depend on fossil fuels, their optimization can be challenging due to the environmental issues which must be considered. Nowadays, the vast majority of power plants are designed based on energetic performance obtained from first law of thermodynamic. In some cases, energy balance of a system is not appropriate tool to diagnose malfunctions of the system. Exergy analysis is a powerful method for determining the losses existing in a system. Since exergy analysis can evaluate quality of the energy, it enables designers to make intricate thermodynamic systems operates more efficiently. These days, power plant optimization based on economic criteria is a critical problem because of their complex structure. In this study, a comprehensive analysis including energy, exergy, economic (3-E) analyses, and their applications related to various thermal power plants are reviewed and scrutinized.The National Natural Science Foundation of China, Hubei Provincial Natural Science Foundation of China, Key Project of ESI Discipline Development of Wuhan University of Technology and the Scientific Research Foundation of Wuhan University of Technology.https://onlinelibrary.wiley.com/journal/20500505am2020Mechanical and Aeronautical Engineerin

Semiconducting Metal Oxide Based Sensors for Selective Gas Pollutant Detection

A review of some papers published in the last fifty years that focus on the semiconducting metal oxide (SMO) based sensors for the selective and sensitive detection of various environmental pollutants is presented

Entransy analysis and optimization of irreversible Carnot-like heat engine

Owing to the energy demands of the world and the issues involved with global warming, analyzing and optimizing power cycles have increased in importance. In this work, the concepts of entransy dissipation, power output, entropy generation, energy, exergy output, exergy efficiencies for irreversible heat engine cycles are applied as a means of analyzing them. This paper presents thermo-dynamical study of an irreversible heat engine cycle with the aim of optimizing the performance of the heat engine cycle. Moreover, four different strategies in the process of multi-objective optimization are proposed, and the outcomes of each strategy are evaluated separately. In the first scenario, in order to maximize the exergy output, ecological coefficient of performance (ECOP) and exergy-based ecological function (EECF), a multi-objective optimization algorithm was executed. In the second scenario, three objective functions comprising the ecological function, ECOP and exergetic performance criterion were maximized at the same time by employing multi objective optimization algorithms. In the third scenario, in order to minimize the entransy dissipation and maximize the ECOP and EECF, a multi-objective optimization algorithm was executed. In the fourth scenario, three objective functions comprising the exergetic performance criterion and ECOP and EECF were maximized at the same time by employing multi objective optimization algorithms. All the strategies in the present work are executed via the multi objective evolutionary algorithms based on NSGA|| method. Finally, to govern the final answer in each strategy, three well-known decision makers are executed

A highly accurate model for prediction of thermal conductivity of carbon-based nano-enhanced PCMs using an artificial neural network

Nano-PCMs, which contain nanostructured materials, can enhance the low thermal conductivity of phase change materials (PCMs). It is crucial to predict the precise thermal conductivity of nano-PCM to assess the heat transfer during phase change procedures such as melting and solidification. In this study, artificial neural network (ANN) and response surface method (RSM) were used to develop a model for predicting the thermal conductivity of carbon-based nano-enhanced phase change materials (NEPCMs) using 482 experimental samples collected from various datasets. The carbon-based nano-particles were SWCNTs, MWCNTs, graphite, graphene, and (CNFs). Six input parameters were considered with varying temperature, mass fractions, sizes, thermal conductivities of PCMs and nano-particles, and the phase of PCM. The ANN was designed using a multi-layered feed-forward structure and Levenberg–Marquardt back-propagation algorithm with one hidden layer consisting of eight neurons. The transfer function was varied to achieve optimal performance. It was revealed that the predictive capacity of the ANN model is greater than that of the RSM model based on their corresponding the coefficient of determination (R2) and root mean square error (RMSE) values. The developed ANN achieved a R2 value of 0.99, while the RSM method model achieved an R2 value of 0.79

Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications

The results of a study on incorporating solar-thermal collectors into a hybrid renewable energy system are reported. A photovoltaic–wind turbine–fuel cell–solar-thermal collector system is designed and an economic model is introduced for supplying the residential thermal and electrical loads via the grid-connected hybrid system. Since determining the optimal operation of a hybrid system such as a combined heat and power system constitutes a complex optimization problem requiring a sophisticated optimization method, a modified heuristic approach-based particle swarm optimization is proposed for solving the optimization problem. The results are compared with those obtained by an efficient metaheuristic optimization method, namely a genetic algorithm, in terms of accuracy and run time. The results show that, using the grid-connected hybrid combined heat and power system, among the cases considered, decreases the total cost of the system. The results also demonstrate that the reductions in daily cost relative to the base case by the modified particle swarm optimization algorithm for Cases 1–4 are 5.01%, 25.59%, 19.42%, and 22.19%, respectively. Finally, Case 2 is the most cost-effective and reliable. Moreover, the modified particle swarm optimization algorithm leads to better results than the genetic algorithm

The 3E Optimal Location Assessment of Flat-Plate Solar Collectors for Domestic Applications in Iran

The analytical hierarchy process (AHP) was utilized to determine the optimal location on which to install flat-plate solar thermal collectors for residential buildings in a number of cities in Iran under diverse climatic conditions. The payback period of investment (IPBP) was chosen as one of the decision criteria, while payback periods of energy and greenhouse gas emissions (EPBP and GGEPBP), being two recently introduced concepts, were also taken into account to provide a broader insight from the energy, economic, and environmental (3E) benefits of the system. The novelty of this work is proposing a method to find places with the greatest potential to install flat-plate solar collectors. It was performed using AHP as a systematic decision-making tool, and based on energy, environmental, and economic criteria, which are the key aspects of an energy system. Codes developed in the MATLAB software were employed to determine the values for different investigated cities. According to the results, Yazd, located in the center of the country, was found to be the best place to install the system. This city enjoys EPBP, IPBP, and GGEPBP scores of 2.47, 3.37, and 0.71 years, respectively. The collector area for this city was also found to be 109.8 m2. Yazd gained a score of 26.5 out of 100. With scores of 24.4, 18.6, 15.9, and 14.6 out of 100, Tehran, Bandar Abbas, Rasht, and Tabriz were found to be the second, third, fourth, and fifth priorities for utilizing the system, respectively