In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.

Thermal treatment is used for different grain crops during the processes of drying, disinfection, and preparation to feeding, etc. The high cost of the processes is caused by the cost of energy and the energy-output ratio of the processes. The development of the processing regimes with the use of electric technologies in general and electromagnetic fields in particular can reduce the cost of the mentioned processes. When IR and MW fields are used, there occurs the direct heating of the grain material without the coolant, due to the effect of the field on water molecules in the kernels. As the grain is a colloid capillary-porous substance, moisture can be in a bound or free state that affects the properties of the material and the thermal processes in it. The use of the special programs gives vast possibilities for the design of such equipment. Using of MW fields allows reducing the cost of the thermal treatment 15-20% depending on the process and type of the processing material.

Studying the effect of microwave fields on the intensification of heat and moisture exchange processes in a granular layer is a rather complicated task, which cannot be effectively solved without an adequate research methodology. In presented materials, a system of principles and approaches for studying processes of microwave-convective grain treatment has been discussed. Based on the explicitly defined process target, functional-physical analysis has been performed that made it possible to specify the most essential factors of process. Morphological analysis of processes shall be performed in several stages. At each stage, targets of process have to be set by excluding combinations of factors that have no reason to be considered at this particular stage of process development. Application of mathematical simulation methods is advisable for selection of optimal options.