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[Display omitted] • A comprehensive study on renewable energy resources in North America is conducted. • A detailed evaluation of renewable energy potentials is provided. • Important steps for better use of renewable energy resources are summarized. • Emerging technologies for development and management of renewable energy are addressed. • Theoretical and practical challenges in development of renewable energies are highlighted. Energy is an important drive for the economic development and social growth. The energy demand has remarkably escalated due to the technological developments in various governmental, industrial, and municipal activities. The fast growing rate of the energy demand as well as the fuel prices, along with the necessity to control the greenhouse gas emissions are the leading driving forces for effective exploitation of the renewable energy resources. In fact, satisfying the growing global energy demands and mitigating the climate change are of the utmost importance in the modern days. In this review paper, a detailed overview of the development and evolution of the renewable and sustainable energy sources is provided in terms of their types, characteristics, applications, production processes, advantages and disadvantages. Moreover, we discuss the impacts of the renewable energies on health and environment, along with the relevant policies and regulations. Further, the emerging technologies and theoretical and practical challenges in the development of the renewable energies are analyzed. Particularly, this review provides information on renewable and sustainable energies' status and prospects in North America. Central large-scale technology and distributed configurations of the renewable energies might be improved in the North American countries to effectively utilize the renewable energies for producing electricity, biomass-based fuels, and heating in both local/rural and industrial divisions. In addition to employment opportunities stemmed from manufacturing of the renewable energies, its significant impact on the economic development can be enhanced in the US, Canada, and Mexico through development of the renewable energy sources. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two effective predictive tools are introduced to accurately estimate biodiesel cetane number. • ET is more reliable and precise, compared to LSSVM. • Linoleic (18:02) has the highest importance in biodiesel cetane number. • The strongest correlation exists between Oleic (18:01) and Lauric (12:00), among input variables. • The results of sensitivity analysis are in good agreement with the literature. The self-ignition ability of fuels is characterized using a property called cetane number (CN). The main objective of this research work is to develop accurate models using smart algorithms to predict the CNs associated with 149 samples of biofuels collected from the literature. In this study, we use two machine learning (ML) algorithms, namely least squares support vector machine (LSSVM) and extra trees (ET) and an experimental dataset containing a variety of fatty acids methyl esters (FAME). To assess the predictive performance of the developed models, various statistical parameters including average relative deviation in percentage (ARD%), average absolute relative deviation in percentage (AARD%), and coefficient of determination (R2) are calculated. The statistical and graphical analyses presented in this study reveal that the ET model outperforms the LSSVM model in predicting the CN associated with the biodiesel systems from the collected dataset so that the value of AARD% is less than 3% for the ET method. It is also found that Linoleic (18:02) is the most important factor affecting the CN value of the biodiesels. According to the sensitivity analysis, the CN value decreases upon an increase in the concentration of Palmitoleic (16:01), Oleic (18:01), Linoleic (18:02), and Linolenic (18:03); this is in agreement with the experimental studies. The smart modelling techniques used in this study can be utilized as alternative methods to direct measurements for determination of biodiesel CN. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vital parameters affecting control and utilization of co-culture systems are evaluated. • Utilization of microalgal microbial fuel cells in wastewater treatment is reviewed. • Microalgal co-cultivation systems for biomass/biofuel production are analyzed. • Techniques for biomass and lipid content measurement are comprehensively discussed. • Current challenges and future prospects in microalgal co-cultures progress are addressed. The adverse impacts of fossil fuels on the environment, specifically climate change, have intensified the need for finding a sustainable alternative source of energy. Numerous studies have postulated that biotechnology development, focusing on biofuel production processes, could be a suitable solution for sustainable energy production. The utilization of microorganisms, such as microalgae, is one of the basic strategies to produce biodiesel, pharmaceuticals, and nutraceuticals. Co-cultivation has overtaken mono-cultivation to improve the production of microalgae due to its endurance, foreseeability, and stability. However, further development of the co-cultivation process requires elaborate efforts to make it safe, practical, and optimal. Some dominant factors affecting the co-culture system control are the diversity of the cell groups, mass transfer, scale-up, population ratio, and time. In this review article, we will discuss some critical topics related to the co-cultivation process, such as data collection, modelling, cultivation methods, and interaction varieties. An overview of the quantification techniques for biomass concentration and lipid content is also provided. Moreover, the utilization of microalgal co-cultures will be analyzed, depicting the difficulties associated with their efficient control. As knowledge of the reactions and their entailing kinetics is elemental for analyzing the microalgal systems, which are used to synthesize intermediate products and chemicals, some studies focusing on the kinetics of microalgal biomass conversion into biofuels are presented. Since the microbial fuel cells (MFCs), as a new bioelectrochemical process, are utilized in the mixed-culture systems to treat wastewater and produce biofuels and other valuable by-products, a summary of the microalgal MFCs is also provided. Finally, arguments about the challenges and advantages of the co-cultivation systems are presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two integrated configurations for H 2 production from solid oxide fuel cell waste heat are compared. • PEM electrolyzer and Cu-Cl electro-thermochemical are considered in two different scenarios. • Water scrubbing-based biogas upgrading system is used to supply input feed to a solid oxide fuel cell. • Opting for Cu-Cl cycle over PEM/CO 2 power cycle enhances economic and thermodynamic conditions. • Exergy, economic, optimization and uncertainty analyses are used to determine optimal conditions. The efficient and cost-effective design of an integrated structure relying on a biogas-based fuel cell for hydrogen (H 2) production can represent a key step to attain the goal of net-zero carbon emissions. In this paper, waste heat and generated power of the hybrid configuration based on the biogas purification cycle and solid oxide fuel cell are utilized to produce H 2. In the first H 2 production scenario, the waste heat and power are used in a copper-chlorine (Cu-Cl) thermochemical plant and a polymer electrolyte membrane (PEM) electrolysis unit. In the second H 2 production scenario, the power and waste heat are employed in a carbon dioxide power process and a PEM electrolyzer. Using an energy analysis, the thermal efficiency of the Cu-Cl/PEM-based system is 25.19% greater than that of the PEM-based system. The exergy efficiency of the Cu-Cl/PEM- and PEM-based systems are calculated at 48.10% and 40.12%, respectively. The prime cost of H 2 for the Cu-Cl/PEM-based system is 27.64% smaller compared to that of the PEM-based system. From the thermodynamic and economic perspectives, the first H 2 production scenario seems superior compared to the second one. Sensitivity analysis, machine learning, and multi-objective optimization approaches are used to extract the optimum conditions on the Pareto front. Biogas flow rate, cupric chloride change ratio, and inlet air to fuel cell as the inlet parameters and H 2 flow rate, net annual profit, and irreversibility as the output parameters in the machine learning analysis are considered. The parameters used in the machine learning analysis are obtained from the sensitivity analysis of the first scenario. Various decision criteria are employed to select the optimum operating conditions in the Pareto front. Using particle swarm optimization, the TOPSIS technique determines the operating conditions that have the highest H 2 production (i.e., 171.3 kg/h) and net annual profit (i.e., 3.407 MMUSD/yr). The fuzzy Bellman-Zadeh method introduces the conditions that have the lowest irreversibility for the first scenario. The uncertainty levels of the objective functions are examined using a Monte Carlo-based uncertainty quantification. [ABSTRACT FROM AUTHOR]