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In this article, the fractional third‐order dispersive partial differential equations were investigated by using Shehu decomposition and variational iteration transform methods. The well known Riemann‐Liouville fraction integral, Caputo's fractional‐order derivative, Shehu transform for fractional‐order derivatives and Mittag‐Leffler function were used as the major basis of the methodology. The graphs and table show the solution behavior for various fractional order values. The comparison provided the signed agreement of the solutions to each other as well as with the exact result. The accurateness and efficiency of the suggested techniques are examined using two numerical experiments. The reliability and validity tests show that for various fractional‐order values, the newly proposed method is reliable, accurate, and efficient. [ABSTRACT FROM AUTHOR]

Dryers are utilized in food industry and agriculture in order to extend the useful lifespan of corps. Thermal energy is required for water removal in the process of drying which can be provided by different sources. Solar thermal energy is one of the most applicable sources for drying processes with several benefits such as avoidance of greenhouse gas emission and availability. Regarding the involvement of various factors in the performance of solar dryers, this paper focuses on the works conducted on these systems. In this regard, various types of solar dryers including direct, indirect, mixed-mode and hybrid supplied by solar energy are discussed. According to the outcomes of this review paper, it can be determined that the performance of the solar dryers depends on various parameters such as the type of dryer, solar irradiation, drying time and operating condition. Moreover, it is found that there are several approaches applicable for improving the overall performance of the solar dryers such as utilizing thermal energy storage units, applying solar tracker and using modified materials. Furthermore, there is high potential for integrating the solar dryers with other systems to achieve higher efficiency and reliability. In addition to energy analysis and drying capacity, the solar dryers have been investigated more deeply by applying exergy analysis and the dependency of exergy efficiency on the operating factors are discussed. Environmental analysis conducted on solar dryers reveals high potential of these systems in carbon dioxide mitigation. In addition, the determined payback periods of these systems are acceptable in majority of the investigated cases which shows their advantage in term of economy. [ABSTRACT FROM AUTHOR]

Hydrogen is rapidly turning into one of the essential energy carriers for future sustainable energy systems. The main reason for this is the possibility of off-peak excess power production and storage of renewable stations such as wind farms, photovoltaic plants, etc. For hydrogen (itself) or its sub-productions methanol, ammonia, etc. Such energy systems are so-called power2X technologies. For hydrogen and other biogases, using a fuel cell is a promising method for returning the fuel to the power grid or electric cars in the form of electricity. In this paper, a novel hybrid energy system consisting of a molten carbonate fuel cell (MCFC) and different options to generate hydrogen from the waste heat of the MCFC is investigated. The system consists of two scenarios of weather using proton exchange membrane electrolyzer (PEME) of vanadium chloride (VCL) cycle. The article presents a comprehensive thermodynamic, economic, and environmental analysis of the system optimized by tri-objective optimization (as an innovative optimization) methods. The aim of the optimization task here is to minimize the costs and emissions while maximizing efficiency. A parametric study is conducted to see the effect of different design parameters on the system's performance. Results demonstrate that fuel utilization factor, stack temperature, and current density have the most critical effect on the system performance. In addition, the system coupled with the VCL cycle exhibits better performance than the system with PEME. In addition, at the optimized point, the efficiency, cost rate, and emission become 69.28%, 3.73 ($/GJ), and 1.16 kg/kWh, respectively. In addition, the produced hydrogen in VCL and PEME are 585 kg/day and 293 kg/day respectively. • The hybrid system based on gasifier and MCFC is modeled from 4E perspectives. • The cutting-edge tri-objective optimization is considered to minimize cost and emission and maximize the efficiency. • Two novel methods of producing hydrogen from waste heat is presented and analyzed in detail. • In optimum conditions, the exegetic efficiency and unit product cost become 69.28% and 3.73 ($/GJ) respectively. [ABSTRACT FROM AUTHOR]

In this present research study a multi-generation energy system which is coupled with CO 2 capture unit which is based on Rankine cycle, organic Rankine cycle, ejector cooling system and absorption chiller has been analyzed via energy, exergy, exergy-economic aspects by developing MATLAB, also to achieve the optimum operating condition genetic algorithm has been applied for system optimization. The objective of this study is to propose an optimized efficient integrated energy system to recycle the energy waste of a typical industrial factory. The optimization has been illustrated on a Pareto frontier to achieve the optimum scheme of the multi-generation system regarding technical and economic viewpoints. Results indicate the optimal condition of this system has occurred at 0.37 exergy efficiency with 0.03 $/s. Furthermore, by surging the mass flow rate of waste gases up to 70 kg/s, net power output augmented up to 7500 kW. Besides, hydrogen production and produced desalinated water rise up to 8.5 g/s and 16 kg/s, respectively. • Design a waste heat recovery system for a steel production factory. • Perform exergy, exergo-economic and optimization of suggested system. • The produced hydrogen can be reach 8.5 gr/s. • Optimization results indicate that the exergy efficiency can improve up to 37%. [ABSTRACT FROM AUTHOR]

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results. [ABSTRACT FROM AUTHOR]