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Ammonia production through more efficient technologies can be achieved using exergy analysis. Ammonia production is one of the most important but also one of most energy consuming processes in the chemical industry. Based on a panel of solutions previously developed, this study helps to identify potential areas of improvement using an exergy analysis that covers all aspects of conventional ammonia synthesis and separation. The total internal and external exergy losses are calculated as 3,152 and 6,364 kJ/kg, respectively. The process is then divided into five main functional blocks based on their exergy losses. The reforming block contains the largest exergy loss (3,098 kJ/kg) and thus the largest potential for improvement including preheating cold feed through an economizer, developing technology towards isobaric mixing, and pressure drop reduction in the secondary reformer as the main contributors to the irreversibility (1,302 kJ/kg) in this block. The second largest exergy loss resides in the ammonia synthesis block (3,075 kJ/kg) where solutions such as reduced temperature rise across the compressor, proper compressor isolation, reducing undesired components such as argon in the reactor feed, and using lower temperatures for reactor outlet streams, are proposed to decrease the exergy losses. Throttling process in the syngas separator is the key contributing mechanism for the irreversibility (1,635 kJ/kg exergy losses) in the gas upgrading block. The exergy losses in the residual ammonia removal block (833 kJ/kg exergy losses) are mainly due to the stripper and the absorber column where a modified column design might be helpful. The highest exergy loss in the preheating block belongs to the compressors (518 kJ/kg exergy losses) where a lower inlet temperature and better system isolation could help to reduce losses.

Abstract In this paper, a three-dimensional numerical simulation was carried out to characterize a high-ash coal gasification in a pressurized, oxygen-blown, two-stage E-Gas entrained flow gasifier. The Tabas coal was used as a high-ash feedstock, with 32 wt% ash and was compared with the Illinois #6 low-ash coal, with 10 wt% ash. The computational model was conducted in ANSYS FLUENT 14 based on the conservation equations described in the Eulerian–Lagrangian approach. Also, four heterogeneous combined with seven homogeneous reactions were solved via the finite rate and eddy-dissipation/finite rate model, respectively. Two different parametric analyses were conducted to characterize the gasification process in the E-Gas gasifier. These analyses cover the effects of the O2/Coal ratio and coal slurry water content on the gasifier performance characteristics such as the exit temperature, main species mole fractions, carbon conversion, high heating value, yield syngas fraction and cold gas efficiency. It was found that using the higher ash content coal decreases the cold gas efficiency about 5%, limits the gasifier economical operating range, and reduces the exit temperature, yield syngas and total higher heating value.

Abstract Rising penetrations of variable renewable generation in electric power systems can raise operational challenges. One is that renewables can increase the need for dispatchable generation with fast-ramping capabilities. This can be costly, because in many instances flexible generators can be more expensive than baseload units that have slower ramping capabilities. If ramping capacity is not available, renewable curtailment may be needed. An alternate solution to this need for ramping is to use energy storage. A question that this raises is how renewable and conventional generators and energy storage would interact in a market environment, and whether certain asset-ownership structures would result in more efficient coordination. To this end, this paper presents a multi-period market-equilibrium model of interactions between these different types of market agents. The impacts on renewable integration of conventional generators having limited ramping capabilities are studied through an illustrative case study. We also examine a variety of structures for the participation of energy storage in the market. We find that co-ownership and co-operation of renewable generators and energy storage brings about the best results from the perspective of alleviating market inefficiencies. Having energy storage directly controlled by the market operator or participating as an independent profit-maximizing firm is less efficient.

Abstract Combined cooling, heating, and power systems have been studied extensively because of their great potentials. Accordingly, in the present study, an innovative trigeneration system including a gas turbine cycle, a gasification unit, a heating unit, alongside a single effect absorption refrigeration cycle is proposed. The system operates on natural gas and municipal solid waste (MSW) for cooling, heating, and power generation. The designed system was simulated using Engineering Equation Solver (EES) software through two scenarios; constant power output and constant biomass feed rate, considering seasonal and annual periods. In the first scenario, considering the constant power capacity, the basic design state was considered with the biomass mixing ratio of 50%, and the results of the seasonal study showed that the system capacity is 30 MW , 41.9 MW , and 39.24 MW in terms of electricity, heating, and cooling, respectively. The exergy analysis revealed that the combustion chamber, the evaporator of Heat Recovery Steam Generator (HRSG), and the gasifier in both hot and cold seasons have the highest exergy destruction rate, while the economizers and the evaporators of both HRSGs have the lowest exergy efficiency. The constant mass flow rate of MSW was assumed to be 1.5 kg / s and accordingly, the feed rate of natural gas was also 1.5 kg / s for the mixing ratio of 50% in basic design state of the second scenario, and the results indicated that the annual average capacity of the system for electricity, heating, and cooling generation is 27.43 MW , 40 MW , and 34.15 MW , respectively. Furthermore, the system was capable of providing the domestic hot water supply of end-user with an average capacity of 7.5 MW during a year. The annual Energy Utilization Factor (EUF) and the annual exergy efficiency of the overall system were shown to be 71.25% and 30.79%, respectively.

Abstract A precise model of a counter-flow indirect dew-point evaporative cooler was developed using the group method of data handling-type neural network while the network was trained by extracted data from a validated numerical model. After validating the model, it was employed in a multi-objective optimization problem that implements the non-dominated sorting genetic algorithm-II method. The system was optimized in diverse climatic conditions. In this regard, Yazd, Masjed-Soleiman and Ahvaz were chosen as representatives of cities with hot, hot semi-humid and hot-humid climates in Iran. In each city, optimum values of channel length, channel gap, inlet air velocity and return to intake air ratio were found so that these decision variables maximize the average coefficient of performance and minimize the specific area of the cooler, simultaneously. The results indicated that the developed model can predict the supply air temperature accurately with less than 1 °C error and due to its quick calculation process it is possible to optimize the design based on hourly climate data without any needs to very fast processors. Moreover, using the optimization, the coefficient of performance and specific area for the system designed to be used in Yazd were improved 36.3% and 30.9%, respectively. This figure at Masjed-Soleiman and Ahvaz was 16% and 7.92% improvement in the specific area at the cost of 2.63% and 2.19% reduction in the coefficient of performance, respectively. These improvements allowed the system to reach its full potential and making dew-point evaporative coolers as a suitable cooling system in diverse climatic conditions.

Abstract Non-flammable characteristic of dry-type cast-resin transformers make them suitable for residential and hospital usages. However, because of resin’s property, thermal behavior of these transformers is undesirable, so it is important to analyze their thermal behavior. In this paper temperature distribution of cast-resin transformers is modeled by two different approaches. A FEM-based model which uses experimental-analytical formula for air–cooling vertical ducts and a 3D finite volume based CFD model which is established in the ANSYS CFX software. In order to evaluate and compare the models, the simulation results were compared with the experimental data measured from an 800 kVA transformer. Finally, the influences of some construction parameters and environmental conditions on temperature distribution of cast-resin transformers were discussed.

Abstract High titer ethanol production from rice straw using Mucor indicus fungus was investigated through the solid-state simultaneous saccharification and fermentation (SSSF) process. The straw was pretreated with 0.5 M sodium carbonate solution for 3, 5, and 10 h to improve the efficiency of the process. Effects of the pretreatment on the composition, structural morphology, cellulose crystallinity, swelling capacity, and buffering capacity of the straw were studied. Moreover, the effects of SSSF reaction time, enzyme loading, and solid loading on glucose and ethanol production were investigated. Additionally, the nutritional value of the residue from the SSSF process, as an animal feedstock, was determined in terms of lipid and protein contents. The highest total sugar concentration was 89.2 g/L, obtained from the straw pretreated for 10 h after hydrolysis with 10 FPU/g straw at 15% (w/w) solid loading. Total sugars concentration was not significantly improved by increasing the pretreatment time at low enzyme loadings, whereas it was significantly improved at high enzyme loadings. The highest ethanol concentration and SSSF yield were 99.4 g/L and 89.5% (71.8% based on the raw material), achieved from the straw pretreated for 10 h through the 72-h SSSF at 30% and 15% solid loading, respectively, using 20 FPU/g straw. The low enzyme loadings of 2.5 and 5 FPU/g straw yielded ethanol with concentrations as high as 66.3 and 90.9 g/L, respectively, after 120-h fermentation at 30% solid loading from the straw pretreated for 5 h.

Abstract In last decades, nano technology developed. Since, nano scale thermal cycles will be possibly employed in the near future. In this research, a nano scale irreversible Brayton cycle is investigated thermodynamically for optimizing the performance of the aforementioned cycle. Ideal Maxwell–Boltzmann gas is employed as a working fluid in the system. In this paper, two scenarios are employed in the multi-objective optimization process; however, the outcomes of each of the scenarios are evaluated independently. In the first scenario, in order to maximize the dimensionless Maximum available work and energy efficiency of the system, multi-objective optimization algorithms is employed. Furthermore, in the second scenario, two objective functions comprising the dimensionless Maximum available work and the dimensionless Ecological function are maximized concurrently via employing multi objective optimization algorithms. The multi objective evolutionary approaches on the basis of non-dominated sorting genetic algorithm method are employed in this paper. Decision making is done via three methods including linear programming techniques for multidimensional analysis of preference and Technique for order of preference by similarity to ideal solution and Bellman–Zadeh. Finally, error analysis is implemented on the results obtained from each scenario.

In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified.