A comprehensive review on effective parameters on microalgae productivity and carbon capture rate.

Rising carbon emissions caused by population growth and industrialization is a significant environmental challenge in various countries. To combat this issue, Renewable Energy (RE) and Carbon Capture and Storage (CCS) technologies should be commercialized to reduce Greenhouse Gas (GHG) emissions and generate carbon-free energy. One such technology is the use of microalgae, which can directly capture CO2 from the air through photosynthesis and potentially produce biofuels due to their high energy content. However, the carbon capture rate of microalgae varies globally due to numerous parameters and variables affecting microalgae productivity. Additionally, microalgae productivity and carbon capture formulas yield different results worldwide, especially in outdoor industrial-scale cultivation. This research aims to comprehensively review the effective variables and parameters in carbon capture by microalgae based on microalgae productivity and carbon capture formulas. The research also ranked countries based on CO2 production in four different categories to determine whether the biggest carbon producer countries could exhibit suitable weather conditions for microalgae cultivation. Findings reveal optimal ranges of critical variables in the microalgae growth formula, including temperature, solar radiation intensity, Photon Flux Density (PFD), and sunlight duration. The study also analyzes microalgae cultivation, carbon capture, and oxygen production formula in different systems such as Open Ponds (OP), Tubular Photobioreactors (TPBR), and Flat Plate Photobioreactors (FPPBR), while discussing other influential parameters. In conclusion, emphasizing the adjustment and utilization of optimal values of effective parameters in microalgae cultivation not only holds promise for future carbon capture by microalgae but also pushes human beings toward sustainable development goals.

Advanced bibliometric analysis on water, energy, food, and environmental nexus (WEFEN).

The relationship between water, energy, food, and the environment has piqued the interest of the global community due to the critical interdependence of these resources for long-term development. This article investigates research within the field of the quadruple nexus. Data from Scopus documents, with the keywords "water, energy, food, and environment" from 2011 to 2022, were processed and analyzed. Further research revealed that scientific exploration of the water-energy-food-environment relationship is rapidly expanding. The Scopus database was used to extract information about countries, institutions, highly cited publications, keywords, hot topics, and future research trends for this study. Additionally, the VOSviewer bibliometric software was employed to evaluate the scientific citations in this article. The results indicated that the USA, compared to other nations, publishes a larger quantity of articles in this field. Recently, China, India, and Middle Eastern countries have garnered significant attention and have been extensively researched. The Philippines, Finland, and Iran have also emerged among the top nations publishing recent articles on the water-energy-food-environment nexus (WEFEN). This article attempts to study the bibliography on the WEFEN connection, identify popular topics, and discern the fields of future studies in this discussion. Furthermore, it investigates the effects of economic and social factors as well as the impact of the COVID-19 pandemic on this quadruple nexus.

Forecasting and gap analysis of renewable energy integration in zero energy-carbon buildings: a comprehensive bibliometric and machine learning approach.

This paper investigates biomass and solar energy's present and future perspectives in low/zero energy and carbon emissions. Its data source is published articles indexed in the Scopus database. By analyzing the articles extracted in Vos viewer software, four main areas of research are found: sustainable development, economic and managerial issues, methods, algorithms, modeling technologies, and renewable energy and its sources and types. In all four sections, research gaps were observed in the field of the third generation of photovoltaics (semi-transparent solar cells )organic)) and algae. As part of the study, advanced bibliometric analysis was carried out by VOS viewer software, and 34129 articles were examined from Scopus, alongside a patent analysis using Google patents, in addition to the bibliometric analysis. It has been shown by machine learning that about 9% of future articles in all energy fields will consist of building articles, and a quarter of these articles will be in the field of renewable energy. While residential and commercial sectors are the dominant areas of renewable energy utilization and commercialization research, the potential of new generations of renewable energy technologies will create significant opportunities to achieve low/zero energy-carbon emission buildings. The paper concludes by predicting the increasing rate of renewable energy and building articles compared to energy articles by 2030, emphasizing the critical role of research in advancing sustainable energy solutions. This data mining analysis helps to identify the current gaps and opportunities. Therefore, great potential will be created to develop and commercialize a new generation of technologies in this industry.

Exergy-economic assessment of a hybrid power, cooling and heating generation system based on SOFC.

In this research, a combined cycle using a solid oxide fuel cell system, a single-stage H2O-NH3 absorption chiller and a residential hot water HX, is developed for the electricity production, hot water and cooling all at the same time, and it is studied from an exergy, energy, and exergoeconomic standpoint. Performance of system under the design condition is analyzed and the mathematical model is simulated. After analyzing the results in the initial input mode, changing the fuel cell current density effect and fuel utilization factor on the system efficiency is evaluated. The result indicates that total energy is 4.418 kW, the total exergy efficiency is 37.8%. And the overall irreversibility is 1.650 kW. On the other hand, the air HX, fuel cell and water HX are designed as elements that must be given more attention than others from the exergoeconomic perspective, because they have nearly the most amount of price compared to other parts.

Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm.

Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NOx has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NOx calculation. The Zeldovich method calculated the value of NOx with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NOx anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NOx.

Optimal sizing of residential photovoltaic and battery system connected to the power grid based on the cost of energy and peak load.

The use of renewable energy is necessary to achieve the goals of sustainable development, and sooner or later all countries are forced to plan and make policies for the use of this equipment. Considering the growing trend of smart systems and the ability of these systems to control and use renewable resources, it is necessary to investigate how to control and optimally use these resources in smart systems. Considering the geographical conditions and significant solar energy radiation in Iran, the most suitable option for using renewable energy in residential buildings is solar energy. Among the types of solar energy used around the world, photovoltaic panels are used more due to their wide range, being cheaper than other sources of electric power from solar energy and more durable than other sources. In order to reduce widespread losses and reduce the cost of transmission and distribution, increase efficiency, the possibility of the presence of private sector investors and increase the security and stability of the power grid, distributed production of electrical energy at consumption locations using small-scale units is the most cost-effective way to use home solar panels. Also, the production of energy from wind turbines can be done in the areas where anemometer data determine it to be suitable. The combination of solar energy and wind energy can effectively reduce the need for batteries, but studies show that this combination is only economically viable when it is used on a large scale and with high powers, which requires a lot of investment. Large initial capital is one of the biggest problems of distributed production systems, so the use of artificial intelligence methods for accurate capacity determination of renewable energy production systems becomes doubly important. The economic results show that the least cost of electricity and net price cost are 0.44 $ per kWh and 15.0 million $ respectively, when the converter size was gradually changed, with a renewable fraction of 46.7%.

Reduction of the environmental impacts of the hydropower plant by microalgae cultivation and biodiesel production.

Hydropower plants supply their energy needs for electricity generation from rivers or water canals, so these power plants cannot be used for sustainable electricity generation, and the best time to use these power plants is during peak power consumption. These power plants are less polluting in terms of environment than other power plants, but they also have negative environmental effects, such as freshwater eutrophication and water salinity or microalgae. This study focused its attention on microalgae extraction as an environmentally friendly method to reduce water salinity and how they can be used for biodiesel production as an auxiliary fuel to enhance the energy production by hydropower plants. The information of the sample hydroelectric power plant (Gotvand Dam) that was required for the processing and simulation process is stated. The step-by-step simulation is reviewed and the results and optimizations are described. The highest separation of microalgae for 1 min electrolysis with distance of 1 cm between the two electrodes is 90%, which reduces the salt content of the water in which the microalgae is grown by 13%. The maximum separation of salt from water is 19.5%, which is reduced to 9.5% in the centrifuge method. Water salinity reduction to microalgae extraction ratio is 14.45%. The optimal combination of diesel and biodiesel is 80%-20%. As can be seen from the results it is recommended to use microalgae for reducing negative environmental impacts in addition to increasing the power generation capacities of hydropower plants. Also more specific studies on terms of the culture of the microalgae and its individual cultivation methods for hydropower plants beneficial programs should be taken into account and be used by policy makers in the future.