فهرست مطالب

Renewable Energy and Environment - Volume:7 Issue: 2, Spring 2020

Journal of Renewable Energy and Environment
Volume:7 Issue: 2, Spring 2020

  • تاریخ انتشار: 1399/04/16
  • تعداد عناوین: 6
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  • Mehdi Zare, Barat Ghobadian *, Seyed Reza Hassan Beygi, Gholamhasan Najafi Pages 1-7

    In CI engines, the evaporation rate of fuel on various hot surfaces, including the combustion chamber, has a significant effect on deposit formation and accumulation, the exhaust emissions of PM and NOx, and their efficiency. Therefore, the evaporation of liquid fuel droplets impinging on hot surfaces has become an important subject of interest to engine designers, manufacturers, and researchers. The aim of this study is to investigate the evaporation characteristics based on droplet lifetime and critical surface temperature (the maximum heat transfer rate) of diesel and biodiesel fuel droplets on hot surfaces. In order to determine the effects of diesel fuel, canola oil biodiesel, and castor oil biodiesel, the droplets impinging on the hot surfaces of aluminum alloy (7075) and steel alloy (1.5920) and the evaporation lifetime of diesel and biodiesel fuels were measured. Statistical analysis (ANOVA and Duncan’s multiple-range test) was carried out using SAS software. The results showed the maximum critical surface temperature of 450 °C for the castor oil biodiesel on steel 1.5920 surface and the minimum one for diesel fuel (350 °C). In this case, both surfaces had the same droplet lifetimes of approximately 2 s. The results of ANOVA showed the significant effect of the surface material and fuel type on the evaporation lifetime of fuel droplet at 1 % probability.

    Keywords: Critical Surface Temperature, Evaporation Time, ANOVA, CI engine, Transesterification
  • Mahdieh Rezagholizadeh *, Majid Aghaei, Omid Dehghan Pages 8-18

    Concerning environmental pollution issues derived from fossil energy consumption, the application of renewable energies plays an important role in countries, especially in their energy sector policymaking. Since determining the relationship between different variables and renewable energy not only has significant policy applications in energy sector but also is necessary in achieving sustainable development goals, this study assesses the impact of effective factors on the development of renewable energy consumption in Iran with emphasis on the role of foreign direct investment (FDI) and financial sector development (especially stock market development). This study applies Auto-Regressive Distributed Lag (ARDL) bounding test method over the period of 1978-2016. The research findings show that there is a causal relationship between foreign direct investment and the stock market and renewable energy consumption in Iran such that the increase of foreign direct investment and stock market development will increase the consumption of renewable energies in Iran. On the other hand, a growth in renewable energies consumption will significantly reduce CO2 emission in the long run. Besides, increasing FDI and stock market development will raise the economic growth of a country and, in return, increase CO2 emission.

    Keywords: Foreign direct investment, Stock market, Renewable Energy, GDP, ARDL Test
  • MohammadHossein Jahangir *, Mahnaz Abolghasemi, Seyedeh Mahsa Mousavi Reineh Pages 19-26

    Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan Razavi from more than 35 years ago up to the year 2014. Input data were the calculations of the two indicators of PNPI and SPI by DIC software, and the output layer (drought intensity) was taken to the Matlab software and employed as the teaching data (from 25 years), experiment (from 5 years), and validation (from another 5 years). The 3-9-1 structure of the network of layers had the maximum accuracy with the error rate of less than 2 % and high correlation (more than 90 %). After trial and error for each station through sigmoid stimulation function in the Perceptron network, it was observed that the stations of Mashhad and Quchan had the minimum error and the maximum error was related to the station of Neyshabur. The results of comparisons and observations showed that the artificial neural network had high efficiency in simulation of the data. The obtained correlation amount of 0.999 for the base station represented the small error of the model in prediction. Drought forecasting was performed in this study by the trained algorithm in the artificial neural network without using the observation data. The results showed that rainfall, temperature, and speed models had a positive role in forecasting the provinces that would experience drought. Due to its lower amount of error, SPI indicator was selected for mapping, the findings of which showed that the highest drought intensity belonged to the near normal to normal wet lands.

    Keywords: Forecasting of drought intensity, Artificial Neural Network, Simulation, Multi-layer Perceptron, Levenberg-Marquardt, Razavi Khorasan
  • Fatemeh Boshagh, Khosrow Rostami * Pages 27-42

    The current review purpose is to present a general overview of different experimental design methods that are applied to investigate the effect of key factors on dark fermentation and are efficient in predicting the experimental data for biological hydrogen production. The methods of two levels full and fractional factorials, Plackett–Burman, and Taguchi were employed for screening the most important factors in dark fermentation. The techniques of central composite, Box–Behnken, Taguchi, and one factor at a time for optimization of the dark fermentation were extensively used. Papers on the three levels full and fractional factorials, artificial neural network coupled with genetic algorithm, simplex, and D-optimal for the optimization of the dark fermentation are limited, and no paper on the Dohlert design has been reported to date. The artificial neural network coupled with genetic algorithm is a more suitable method than the RSM technique for the optimization of dark fermentation. Literature shows that the optimization of critical factors plays a significant role in dark fermentation and is useful to improve the hydrogen production rate and hydrogen yield.

    Keywords: Biohydrogen production, Dark fermentation, Experimental Design, optimization
  • Saeed Hosseinpour, Seyed Alireza Haji Seyed Mirza Hosseini *, Ramin Mehdipour, AmirHooman Hemmasi, HassanAli Ozgoli Pages 43-51

    In this study, an advanced combined power generation cycle was evaluated to obtain sustainable energy with high power and efficiency. This combined cycle includes biomass gasification, the Cascaded Humidified Advanced Turbine (CHAT), and the steam turbine. The fuel consumed by the system is derived from the gas produced in the biomass gasification process. The biomass consumed in this study is wood because of its reasonable supply and availability. The economic analysis conducted in the present research has produced significant gains. The proposed cycle with current prices intended to sell electricity in Iran has a positive Net Present Value (NPV). Therefore, the presented cycle in terms of energy supply has good economic value. Due to the significantly higher purchase/sale price of electricity from renewable power plants in developed countries in Europe or the United States, the power generation cycle proposed in this study may be more economically feasible in other regions than Iran. Of course, with a slight price increase in electricity sales in Iran (3 US₵ kWh-1), the proposed system will have acceptable NPV. Because of the complicated equipment used in high-pressure and low-pressure turbines and compressors sets, the equipment used in this cycle requires a higher initial investment cost than conventional power generation systems. The results showed that the investment cost per unit of energy was approximately 909 USD kW-1.

    Keywords: Biomass Gasifier, Cascaded Humidified Advanced Turbine, Steam Turbine, combined cycle, Economic Analysis
  • Arash Abedi, Behrooz Rezaie *, Alireza Khosravi, Majid Shahabi Pages 52-63
    This paper presents a novel local control method for the converter-based renewable energy resources in a stand-alone DC micro-grid based on energy analysis. The studied DC micro-grid comprises the renewable energy resources, back-up generation unit, and battery-based energy storage system, which are connected to the common DC-bus through the buck and bidirectional buck-boost converters. The proposed control method satisfies the stability of the micro-grid output variables, along with current control and voltage regulation by controlling the switching functions of the converters, regardless of the energy resource dynamics. The dynamic component of the switching function is extracted as a control signal using the state-feedback through a mathematical method. The control inputs are designed based on Lyapunov stability theorem to guarantee the stability of output variables (DC-bus voltage and generated currents) in a stand-alone DC micro-grid through an energy analysis. The proposed distributed controller can be easily generalized as a platform with all kinds of the stand-alone DC micro-grids comprising any type or number of distributed generations such as renewable energy resources, fossil-fuel-based generations, and energy storage units. Other features of this local control method are simplicity, celerity, comprehensiveness, and independence of the distributed generations. The dynamic performance assessment of the proposed controller is verified through a simulation in MATLAB/SIMULINKÒ environment. The results validate the accuracy and stability of the proposed control strategy in various operating conditions.
    Keywords: DC, DC converter, Stability Analysis, stand-alone DC micro-grid, switching function