Rotary cement kiln as heart of a cement production plant plays an important role in overall energy consumption and product yield. Therefore, the process behavior and physical parameters affecting the final production need to be investigated. Modeling and simulation of the rotary cement kiln have been done by several researchers considering dynamic and steady state behavior of the process. In this research, different models presented by other researchers were evaluated and their advantages and disadvantages were studied. Next, a general and comprehensive model was developed with the aim of optimization of the process. Due to the complexity of the Differential and Algebraic Equations (DAEs) constructing the model, a powerful method should be employed in order to solve the complex system. Simulation results were compared to the result from a case study presented in the literature. Moreover, GENETIC ALGORITHMS (GA) was used as a strong tool to find optimum parameters and operating conditions for production of different cement types. Having maximum production as objective function, different values for the parameters and conditions were resulted from the numerical optimization of the process. It was found that for some fixed plant geometries, different optimum values exist for production of a specified cement type.

Land use planning is the process of allocating different land uses into different units of land with consideration of ecological and physical characteristics of land. In this process not only the type of activity but also its location is determined. The goal of this process is to achieve sustainable development that considers economic, social and environmental issues. So we face with multi-objective optimization problems. For solving these problems, multi-objective optimization methods are used. One of the popular methods for land use management problems are GENETIC ALGORITHMS. In this paper, coding and defining objectives of these problems are discussed. Also we used NSGA-II for industrial planning of Barkhar with economic and social consideration, environmental issues were used as constraints.

In engineering science, the word design from the perspective of people with different definitions and the selection of inputs for the model in different parts of the design and modeling of chemical processes have a special place. A GENETIC algorithm is one of the methods that it has been using with a simulator to turn it into a powerful tool for optimizing the target function. Due to the widespread of this method in recent years and its significant results in various fields of chemical engineering, in this article, the method of operation of this method and its applications in different fields are discussed in order to get more familiar. In this research, the efficiency of the GENETIC algorithm in the optimization of chemical engineering-related industries, such as the optimization of agitated reactors, the design of process control equipment, the membrane process parameters, and the optimization of thermal systems have been investigated. The results of this study showed the great ability of the GENETIC algorithm to optimize the processes associated with the chemical engineering industry.

In traditional Adjustable Speed Drives (ASDs) voltage controller low power factor and high-energy are some of the major drawbacks. These can be eliminated using GENETIC Algorithm based PWM optimization. This GENETIC Algorithm optimization eliminates the harmonics in the inverter output and provides power factor close to unity. The increased use of power electronics-based loads, (adjustable speed drives, switch mode power supplies (SMPS), etc.) for improving system efficiency and controllability results in increase in the harmonic distortion levels in end use facilities and overall power system. In this paper, the harmonic contents and distortion factor are less in the output of GENETIC Algorithm optimized pulse width Modulated impedance source inverter when compared to standard PWM and random PWM based inverters. It is the most economic methodology to improve the power quality of inverters.

Most of the major projects in the construction industry are faced with earthwork operations, so that one can rarely find a construction project that does not have such operations. For infrastructure projects, ground operations are one of the major areas of the project, and a wide range of construction machinery is used to carry out earthwork operations. Soil operations are one of the most important and costly parts of construction projects and projects for harvesting materials from surface mines. One of the important factors in the success of large projects and projects such as dam, road, tunnel and more is the role of machinery and, consequently, the way of selecting and managing it properly. Due to the large scale of construction projects, even a small improvement in operation operations can save a lot of work. One of the major costs of infrastructure is the cost of ground operations. Therefore, the purpose of this study is to use the GENETIC algorithm to optimize heavy soil operations for proposing a method for developing a method to optimize large-scale earth-borne operations for the lowest cost of land operations.

Structural optimization is a process by which the optimum design is aimed while satisfying all the defined constraints. In recent years, using laminated composite materials in fabrication of mechanical, airspace, marine and machine industries are of major attention, due to their high strength and light weight. One of the objectives of the present paper is seeking the optimum weight and cost of a laminated composite plate, while undergoing the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill is used as the failure criterion. The multiobjective function introduced here consists of weight, cost and failure loading. Therefore, one realizes that in this work a minimal-maximal objective function is to be optimized, thus, the weight and the cost will be minimized while the failure load for all the laminated plies is to be maximized. The design variables could be any combination of thickness, orientation of fibers and the material for each layer. The thickness of the layers could be considered continuous whereas the cost and the material for each layer to be discrete. With regard to the problem in hand, a decision was made to employ a newly introduced type of optimization technique called the GENETIC ALGORITHMS, based on a new type of selection, to handle the optimization process. The theory of analysis was based on the Classical Lamination Theory (CLT). Therefore, attempts were made to produce software for the analysis and the optimum design of laminated composite plates under any combination of design parameters. A number of problems were solved under two different models. First, a multi-objective optimization procedure under a new approach was introduced, where the problem is considered unconstrained. The second model, namely the constrained optimization problem, consists of secondary valued terms which were defined as constraints, while the objective function contained only the major term, as selected by the user. The verification of the results was made satisfactorily, as a consequence of which some benchmark examples were also attempted and recorded.

A numerical method for shape optimisation in forging is presented. The goal of the optimisation is to eliminate work-piece defects that may arise during the forging process. A two dimensional finite element code has been developed for the simulation of the mechanical process. The material is incompressible and it follows the Norton-Hoff law. To deal with contact constraint the velocity projection algorithm is used. The optimisation method considers a GENETIC algorithm supported by an elitist strategy. A new GENETIC operator called adaptive mutation has been developed in order to increase the efficiency of the search. The developed scheme is used to design optimal pre-form shapes for several axisymmetric examples. Comparing the obtained optimal results with the literature validates the proposed optimisation method.Continuous and discrete design variables are considered.