Hybrid solar parabolic dish power plant and high‐temperature phase change material energy storage system

In this study, a conventional steam power plant with two regenerative boilers is considered, and one of its boilers is replaced with parabolic solar dish collectors and storing the produced thermal energy by the phase change material (PCM) in a storage tank. The results show the necessity of the existence of an auxiliary fired‐gas boiler to provide constant load during the whole 24 hours. The performance of the proposed hybridized system is evaluated through energy and exergy analyses. It was demonstrated that substituting solar collectors with one of the boilers marginally lowers the energy efficiency but increases the exergy efficiency of the whole power plant up to 41.76%. Moreover, it is found out that this hybridization decreases the total irreversibility of the power plant in comparison with the base case, from 51.1 to 47.2 MW. The parametric analysis states that raising the mass flow rate of the heat transfer fluid in the solar collectors not only enhances the system performance but also increases the volume of the PCM tank.     

Energy and exergy analyses of five conventional liquefied natural gas processes

In this paper, five conventional LNG processes were investigated by energy and exergy analysis methods. On the basis of the energy analysis, three‐stage process of Linde AG and Stat oil (mixed fluid cascade [MFC]) has less energy consumption than the other ones (0.254 kWh/kg liquefied natural gas). Also, coefficient of performance of the cycles of this process is higher compared with the other ones. Exergy analysis results showed that the maximum exergy efficiency is related to the MFC process (51.82%). However, performance of the MFC process in terms of quality and quantity of energy consumption is considerable. But using three cycles in this process needs more components and consequently more fixed costs. In this study, sensitivity of coefficient of performance, specific energy consumption, and indexes of exergy analysis were also analyzed versus important operating variables for all cases. Copyright © 2014 John Wiley & Sons, Ltd.     

Introducing a novel integrated NGL recovery process configuration (with a self-refrigeration system (open–closed cycle)) with minimum energy requirement

In this study a novel process configuration for recovery of hydrocarbon liquids from natural gas is proposed. The required refrigeration in this configuration is obtained by a self-refrigeration system (open–closed cycle). High performance of the multi-stream heat exchangers, high recovery levels of the hydrocarbon liquids and low required compression power (in the internal refrigeration section) are three of most important characteristic of the proposed configuration. The effects of the mixed self-refrigerant flow rate and pressure on the performance of the process are discussed. Various values for feed composition are tested and the results show that the process can work efficiently with different feeds. In order to analyze the need of external refrigeration by a close or open cycle that is related to the composition of the inlet gas, a configuration with external refrigeration is designed the manner that it is similar with the purposed configuration in the separation section.     

Simulation and exergy‐method analysis of an industrial refrigeration cycle used in NGL recovery units

The behaviour of an industrial refrigeration cycle with refrigerant propane has been investigated by the exergy method. An natural gas liquid recovery unit with its refrigeration cycle has been simulated to prepare the exergy analysis. Using a typical actual work input value; the exergetic efficiency of the refrigeration cycle is determined to be 26.51% indicating a great potential for improvements. The obtained simulation results reveal that the exergetic efficiencies of the air cooler(s) and chilling sections get the lowest rank among the other compartments of refrigeration cycle. Refrigeration calculations have been carried out through the analysis of T–S and P–H diagrams where coefficient of performance (COP) was obtained as 1.8. The novelty of this article include the suggestions for increasing efficiencies, along with the discussion about the reasons for deviation from ideal cycles and also the effect and sensitivity analysis of pressure drops on the coefficient of performance of the cycle. Copyright © 2006 John Wiley & Sons, Ltd.     

Design of an integrated process for simultaneous chemical looping hydrogen production and electricity generation with CO2 capture

This study was aimed at proposing a novel integrated process for co-production of hydrogen and electricity through integrating biomass gasification, chemical looping combustion, and electrical power generation cycle with CO₂ capture. Syngas obtained from biomass gasification was used as fuel for chemical looping combustion process. Calcium oxide metal oxide was used as oxygen carrier in the chemical looping system. The effluent stream of the chemical looping system was then transferred through a bottoming power generation cycle with carbon capture capability. The products achieved through the proposed process were highly-pure hydrogen and electricity generated by chemical looping and power generation cycle, respectively. Moreover, LNG cold energy was used as heat sink to improve the electrical power generation efficiency of the process. Sensitivity analysis was also carried out to scrutinize the effects of influential parameters, i.e., carbonator temperature, steam/biomass ratio, gasification temperature, gas turbine inlet stream temperature, and liquefied natural gas (LNG) flow rate on the plant performance. Overall, the optimum heat integration was achieved among the sub-systems of the plant while a high energy efficiency and zero CO₂ emission were also accomplished. The findings of the present study could assist future investigations in analyzing the performance of integrated processes and in investigating optimal operating conditions of such systems.     

Thermodynamic analysis and evolutionary algorithm based on multi-objective optimisation of the Rankine cycle heat engine

This study demonstrates the outcomes of a research implement for the power and efficiency optimisation of a Rankine cycle heat engine employing the non-dominated sorting genetic algorithm (NSGA-II) algorithm. Two objective functions comprising the efficiency and power were included concurrently maximised. To assess this idea, multi-objective optimisation approach founded on NSGA-II method has been utilised in which following variables have been considered as decision variables: (1) the inlet temperatures of a heat source, (2) the inlet temperatures of a heat sink, (3) temperature difference (x), (4) temperature difference (y), (5) heat conductance and (6) heat capacitance. By applying the addressed multi-objective optimisation approach, Pareto optimal frontier was determined and utilising different decision-making techniques that include the LINMAP, TOPSIS and fuzzy Bellman–Zadeh approaches help us to figure out the final optimal solution.     

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Water Resources Management - Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water...     

Desalinated water and hydrogen generation from seawater via a desalination unit and a low temperature electrolysis using a novel solar-based setup

This study investigates the proficiency of employing solar energy in a novel setup geared towards simultaneous production of desalinated water and hydrogen wielding parabolic trough solar collectors (prime mover) in three solar radiation approaches; low radiation, high irradiation and no radiation. Targeted for coastal areas, this setup generates electricity using an organic Rankine cycle; utilizing its waste heat, a desalination unit applying humidification and dehumidification processes, yields desalinated water. Subsequently, hydrogen is produced through exploiting a proton exchange membrane electrolyser as a low temperature electrolyser fed by electricity and water. One of the cardinal points of this system is the production of hydrogen by means of electricity and desalinated water obtained from previous stages. With the purpose of determining the efficiency of this setup, a parametric study has been conducted grounded on the effect of important parameters on production rates and different efficiencies. Ensuing, multi-objective optimization is set forth by implementing a genetic algorithm in order to effectuate the optimal design state. The results indicated that the desalination rate in the three solar radiation approaches mentioned are 1.76 kg/s, 1.07 kg/s and 1.36 kg/s, respectively, and the hydrogen production rate are 4.33 g/s, 2.62 g/s and 3.54 g/s, correspondingly.     

Artificial neural networks modelling of the performance parameters of the Stirling engine

The Stirling engine can theoretically be very efficient to convert heat into mechanical work at Carnot efficiency. Various parameters could affect the performance of the addressed Stirling engine which is considered in optimisation of the Stirling engine for designing purpose. Through addressed factors, torque has the highest effect on the robustness of the Stirling engines. Due to this fact, determination of the referred parameters with low uncertainty and high precision is needed. To solve the mentioned obstacle, throughout this paper, a generation of intelligent model called ‘artificial neural network’ (ANN) was implemented to estimate the torque of the Stirling heat engine. In addition, highly accurate actual values of the required parameters which were gained from open literature surveys from previous studies were implemented to develop a robust intelligent model. Based on the outcomes of the ANN approach, the output results of an ANN model were close to relevant actual values with a high degree of performance.     

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

This work was aimed at proposing a flexible and reliable framework based on combination of three soft computing techniques, i.e., artificial neural network, genetic algorithm, and fuzzy systems for multi-objective exergetic optimization of continuous photobiohydrogen production process from syngas by Rhodospirillum rubrum bacterium. To this end, artificial neural network (ANN) coupled with fuzzy clustering method (FCM) to model exergetic outputs on the basis of input variables. The outputs of modeling system were then fed into a novel optimization approach developed by hybridizing additive linear interdependent fuzzy multi-objective optimization (ALIFMO) and the elitist non-dominated sorting genetic algorithm (NSGA-II). The optimization was carried out to minimize the normalized exergy destruction and maximize the rational and process exergetic efficiencies, simultaneously. The solutions of the proposed approach were also compared with conventional fuzzy multi-objective optimization procedure with independent objectives. Overall, the modeling system predicted the exergetic parameters of photobioreactor with a coefficient of determination higher than 0.90. Furthermore, the optimization approach suggested syngas flow rate of 13.35 mL/min and agitation speed of 383.34 rpm as the best operational condition by considering the preferences of process exergy efficiency, rational exergy efficiency, and normalized exergy destruction, respectively. This condition could yield the normalized exergy destruction of 1.56, process exergetic efficiency of 21.66%, and rational exergetic efficiency of 85.65%. The obtained results showed the superiority of the proposed approach over the conventional fuzzy method in optimizing the complex biofuel production systems.