Journal of Renewable Energy and Environment
Volume:10 Issue: 3, Summer 2023

The height of buildings is one of the main features of urban configuration that affects energy consumption. However, to our knowledge, the complexity of relationships between the height parameters and energy use in urban blocks is poorly understood. In this context, the present study investigates the effect of the height distribution of buildings located in a residential complex on the energy consumption required for cooling and heating. This research simulates different possible layouts through computational software. For this purpose, first, the density of a residential complex was determined based on the rules and regulations of Tehran city and according to the site dimensions and certain site coverage. Then, the required building density was distributed in different layouts based on their diversity at different heights. The product of this stage involved 7 different layouts in which the height varied from 1 floor to the maximum number calculated in each part of the simulation. In the next step, the annual energy consumption for cooling and heating the complex was calculated for each of these layouts and compared with each other. The parametric generative model was created in the Grasshopper plugin from Rhino software, and the energy consumption was evaluated with the Honeybee plugin over one year. Also, the research findings were validated through DesignBuilder software using the EnergyPlus engine. The results of the energy simulation indicate that the height distribution of the blocks can have a significant effect on energy consumption. In the optimal case, proper layout reduces the annual cooling and heating energy consumption by 28 % and 13 %, respectively. Therefore, achieving an optimal value for each of the cooling and heating loads depends on the specific priorities and conditions of the design project. If the design project's priority is to reduce heating energy consumption, increasing the height and distributing the floors evenly between the blocks is a better answer. However, if the priority is to mitigate cooling energy consumption, the optimal layout can include low-rise blocks and a single very high-rise block.

In many middle- and high-income countries, existing buildings will occupy the majority of building areas by 2050 and measures are needed to upgrade the mentioned buildings for a sustainable transition. This research proposes a method to mitigate the energy consumption of existing educational buildings using four energy efficiency measures (EEMs). The proposed method divides simulations into two main parts: simulations with and without using heating, ventilating, and air conditioning (HVAC) systems. Four passive EEMs are used, including window replacement, proposed shading devices, new insulations, and installing a new partition wall for the entrance part of the building. This research uses a simulation-based method to examine the effect of each EEM on the energy consumption of the building using DesignBuilder software. The steps of data collection and modeling in this research include collecting raw data related to the physical characteristics of the building experimentally and creating a basic model. Afterwards, simulation scenarios were defined based on the proposed method, and several simulations were carried out to examine the impact of each EEM on the energy performance of the building. Two environmental parameters of the simulation process, including indoor air temperature (IAT) and relative humidity (RH), were used. The measures reduced the heating and cooling demands in the building by 80.14 % and 15.70 %, respectively. Moreover, the results indicated that the total energy consumption of the building were reduced by 10.44 % after retrofitting measures.

Furandicarboxylic acid (FDCA) is recognized as a valuable product of hydroxymethylfurfural (HMF) derived from cellulosic materials as an abundant renewable source. It could find future bioplastic application if a feasible separation process is developed. To find a commercially available solvent, FDCA should be selectively separated from HMF and the downstream process be supported by pyrolysis-gas chromatography-mass spectrometry experiments in line with density functional theory (DFT). Evaluation of the sigma potential and sigma surface analysis demonstrated that benzene and ethyl acetate enjoyed better extraction and HMF selectivity, whereas FDCA exhibited ideal behavior in the presence of DMF and DMSO solvents. It was proved that the hydrophobicity could be changed by improving the hydrogen-bonding interaction between them. Moreover, the up-down selection of classes of solvents based on the experimental data found by GC-MS revealed that polar molecular solvents (ethanol-water) were more compatible with carboxylic acids and alcohol compounds, while n-hexane was a desirable solvent for phenolic compounds. It was found that levoglucosan retained a significant fraction of water compared to other solvents, which need to be considered for further economic and environmental analysis under the multifaceted framework of biomass-derived products.

Solar Organic Rankine Cycle (SORC) is a successful approach to sustainable development and exploiting clean energy sources. The research aims to improve and evaluate the energy efficiency of the SORC for combined heat and power generation for a residential home under the climatic conditions of Baghdad, Iraq. Thermoeconomic analysis was carried out for the proposed energy supply system. Refrigerant HFC-245fa was used as a working fluid in a solar organic Rankine cycle, and oil poly alkyl benzene (TLV-330) was suggested as a heat transfer fluid in the solar collector field. Parametric studies for some key parameters were conducted to examine the impact of various operating conditions on energy efficiency. The results showed a significant improvement in energy efficiency. The maximum efficiency of SORC CHPG reached 79.14 % when solar heat source temperatures were in the range of 100 to 150 °C and the solar radiation was at a maximum value of 870 W/m2 at noon on the 15th day of July in Baghdad. The maximum energy produced by SORC CHPG was 472.5 kW when the optimal average value of global solar radiation was 7.5 kWh/m2/day in June. The economic investigations revealed that the payback period of the new energy supply system was 10 years with the positive net present cost when the solar power plant was working 18 h/day.

In this study, the impact of digestate treatment after Anaerobic Digestion (AD) process in two scenarios is analyzed in the case of an industrial diary unit in the United States. The first scenario involves production of liquid fertilizer and compost, while the second scenario lacks such a treatment process. Aspen Plus is used to simulate the AD process and evaluate the general properties of biogas and digestate. The results of technical analysis show insignificant changes in the net power production from the CHP unit in Scenario 1. The economic analysis, however, indicates the necessity of digestate treatment for AD systems to be profitable. Furthermore, the results of environmental analysis indicate the mitigation of about 93.4 kilotonnes of greenhouse gas (GHG) emissions in Scenario 1, while AD in Scenario 2 saves only 12 kilotonnes of GHG emissions. In other words, digestate treatment has a more significant environmental impact than the power production and its profitability from CHP unit. The reason could be attributed to the enormous consumption of energy during the production of chemical fertilizers where the digestate treatment process (scenario 1) offsets the utilization of chemical fertilizers in the agriculture industry.

Around the globe, a 60 % increase in energy demand is predicted to occur by the end of the year 2030 due to the ever-increasing population and development. With a registered temperature up to 50 °C in August 2020, which is classified as one of the hottest regions in the world, the demand for cool temperatures in Gabes-Tunisia to achieve the thermal comfort of people ensuring the product storage has become more and more intense. Removing heat from buildings represents the most extensive energy consumption process. In this paper, an absorption-refrigeration system driven by solar energy is proposed. A parametric simulation model is developed based on the TRNSYS platform. A comparison between different models for global radiation calculation and experimental meteorological data was carried out. It has been proven that the Brinchambaut model seems to be the most convenient in describing the real global radiation, with an error of up to 3.16 %. An area of 22 m² of evacuated tube solar collector ensures the proper functioning of the generator and achieves a temperature up to 2 °C in the cold room.

The rising temperature of the earth's surface and the formation of heat islands in megacities have become two of the biggest environmental threats. This compound problem affects urban climatology, including urban vegetation and air pollution, human health, and the environment, including the group of vulnerable members of the society and public health, leading to the growing death rate. Hence, the purpose of this study is to investigate the leading causes of temperature changes and the development of a thermal island in the city of Tehran following the expansion of this metropolis in recent decades. This research uses thermal remote sensing and GIS techniques to analyze information from Landsat satellite images in (TM-ETM-TIRS) sensors from 1984 to 2020. The results of the research indicated that the surface temperature of the city of Tehran during the years 1984 to 1996, 1998 to 2008, and 2010 to 2020 experienced a relative increase in the summer and winter seasons. In the first decade, the average temperature of the green layer was -7, while the temperature of the magenta and red layers were 20 and 25 degrees, respectively. In the second decade, the average temperatures of the green and dark green classes were -1 and 3 while they were 23 and 27 degrees for the magenta and red classes, respectively. In the third decade, the average temperatures of the green and dark green classes were -1 and 3, and thost of the magenta and red layers increased to 28 and 31 degrees, respectively. Furthermore, the analysis of vegetation cover based on the NDVI index pointed to the continuing reduction of vegetation in the studied years. Regarding the direct correlation between the heat island and vegetation and the concentration of the heat island in the city center, further measures must be taken and the vegetation cover should be increased to reduce the heat island. The city center needs to be decentralized as part of the remedy via proper urban design and planning.

Wind energy has been identified as a critical component in the growth of all countries throughout the world. Nigeria has been identified as having energy issues as a result of poor maintenance of hydro and thermal energy generating stations. As a result, the current study uses some machine learning approaches over wind speed data for energy generation in the country. Machine learning models were employed for wind speed using selected meteorological parameters. Little research was done using some meteorological data and machine learning to investigate wind speed across Nigerian sub-stations, resulting in the need for further research. This research, on the other hand, focuses on a neural network for forecasting, a Long Short-Term Memory (LSTM) network model based on several fire-work algorithms (FWA). The data for this study came from the archive of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Web service, which was modeled. The LSTM predicts the wind speed model based on the FWA, which used hyper-parameter optimization and was based on a real-time prediction model that was dependent on the change and dependence of the neural network. The study data was split into two categories: test and training. According to the validation technique, the sample data was reviewed, and the first 80 % of the data was utilized for training, as revealed by the (LSTM) network model. The remaining 20 % of the data was used as forecast data to ensure that the model was accurate. The normalization of the data for the wind speed range of 0 to 1 which illustrates the process data, the high peak in 1985 (a = 0.12 m/s, b = 0.11 m/s, c = 0.13 m/s, d = 0.08 m/s, e = 0.06 m/s, f = 0.10 m/s) was discovered. However, the summary result of the performances of different 11 Machine Learning algorithms of regression type for each of the seven locations in Nigeria has different values. As a result, it is recommended that this study will facilitate the prediction of wind speed for energy generation in Nigeria.

The percentage of production and utilization of hydrocarbon resources from the livestock sector has raised concerns regarding the worldwide issue of global warming. A total of CH4 emissions from enteric fermentation and waste management has been estimated at 78.3 %. Meanwhile, N2O emissions are 75-80 % of total agricultural emissions. This raises questions about the extent of global warming due to increased CO2 resulting in changes in weather and global warming. This research aims to predict Green House Gas (GHG) emissions from manure management and present policy alternatives for Indonesian livestock development. Secondary data was taken from a related website (fao.org) with coverage throughout Indonesia from 1961 to 2021, containing 12,480 rows and 5 column features including item, Element, Year, Unit, and Value emission. LSTM and GRU are used to predict the trend of emission from manure management to provide alternative policies on greenhouse gas mitigation in Indonesia. The results showed that based on 15 types of livestock that emit GHG emissions, 3 types of livestock produce the highest emissions from 1961 to 2021: (a) cattle, (b) cattle and non-dairy, and (c) poultry. Significant reduction in the emission of carbon dioxide (CO2eq) in 2020 is indicated by reduced public consumption and hampered supply chains with large-scale social restrictions (covid-19 pandemic policy). Based on these results, fertilizer storage duration can be used as a policy to reduce CO2eq emissions, hence it is desired that fertilizer management techniques can be properly regulated. Mitigation can also be accomplished by utilizing livestock waste as biogas and upgrading animal feed additives with chitosan or potassium nitrate.

In this study, in addition to assessing the conditions in the coastal region of Bandar Abbas, the feasibility of utilizing Archimedes torsional turbines for renewable energy production in this area was investigated through a combination of field measurements and numerical simulations. Field studies included the measurement of environmental conditions, depth, and vessel traffic. The determination of a safe depth was based on these measurements. Additionally, the current patterns were assessed in the field, measuring key parameters like salinity, electrical conductivity, and density. To further develop the results, a numerical simulation was conducted using the ROMS numerical model to establish the hydrodynamic current patterns in the target area. Upon reviewing the outcomes with the SOLVER program and employing linear programming methods, effective constraints derived from field monitoring were created. The study explored the optimal energy efficiency of Archimedes torsional turbines under different inclinations relative to the seabed and angular velocities. The research and simulations revealed that varying the tilt of the vertical axis of the turbine within the range of 5 to 15 degrees significantly impacted the turbine's efficiency. The highest efficiency, at 75 %, was achieved at a 15-degree angle with a turbine rotation speed of 150 rpm. This result is particularly notable, considering the low slope of the studied area.