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In this paper, an exergetic optimization of simple and finned flat plate solar air collectors is established to determine the optimal design and operational parameters for humid subtropical climatic conditions. Solar air collectors are commonly used for space heating, irrigation, greenhouses, grain drying, hot air generation, etc. A detailed solar energy radiation model is developed for solar energy conversion. A comprehensive optical, energy, and exergy analysis is carried out for evaluating the performance, energy and exergetic efficiency for simple and finned flat plate solar air collectors under humid subtropical climatic conditions. A simulation program is developed for solar energy model, energy and exergetic calculations. The following geometric and operating parameters are considered as decision variables: absorber plate area, dimensions of simple and finned solar collectors, fluid inlet and outlet temperatures, average velocity, overall loss coefficient, glass cover temperatures, plate temperature, and useful heat gain. The proposed model is optimized using computational intelligence technique (single phase multi-group teaching learning optimization) for maximum mean exergy efficiency in simple and finned solar air collectors for humid subtropical regions. The maximum exergy efficiency is achieved in the case of finned solar air collectors with a yearly optimal average exergy efficiency of 6.10% for a collector area of 5 m

This article proposes a model to determine the optimal performance and design conditions for a flat plate solar water collector. The model uses the hourly solar irradiation data over a year for humid subtropical climatic conditions for estimating the thermal, optical, and exergy efficiency. The proposed model has been validated with the data in the literature. Six single-objective computational intelligence (CI) techniques are used to determine the maximum exergy efficiency by optimizing the plate area of the absorber, mass flow rate, and inlet temperature of the working fluid. The statistical analysis shows that the performance of water cycle algorithm is superior in every statistical parameter. Six multi-objective CI techniques are used to evaluate the trade-off solutions between the conflicting objectives of maximizing exergy efficiency and minimizing the area of the absorber plate. Three of these algorithms are able to determine the maximum exergy efficiency with the minimum absorber plate area. A MATLAB-based GUI has also been provided to help in determining the optimal values of the decision variables under various scenarios.

Evolutionary algorithms are effective in solving complex nonlinear optimization problems with multiple conflicting objectives. League Championship Algorithm (LCA) is a recently proposed single-objective evolutionary algorithm which has shown impressive results on benchmark problems used in Conference on Evolutionary Computation (CEC). In this work, two multi-objective versions of LCA, viz. NS-LCA and e-dominance LCA are proposed which utilizes non-dominated sorting of solutions and e-dominance concept respectively to solve multi-objective problems. Performance of both algorithms has been tested on three multi-objective optimal control problems.

The design of optimal job schedules on parallel machines with finite time is a combinatorial optimization problem and plays a crucial role in manufacturing and production facilities. In this work, we evaluate the performance of two recently proposed computational intelligence techniques, Grey Wolf Optimizer (GWO) and JAYA on ten datasets arising from five job shop scheduling problems with parallel machines. The computational results have shown GWO to be efficient than the JAYA algorithm for problems with higher number of orders and machines.

Catalytic combustion is a highly efficient and modern combustion technique. It incorporates noble metal catalysts to convert the fuel into the products of combustion and provide flameless heat. Flameless heat is appropriate to use in industrial hazardous areas. In this paper catalytic combustion of LPG is carried out with an intension of optimizing the combustion process considering residence time and quantity of excess air as parameters. The optimization was carried out for minimizing amount of unburned fuel. The exhaust gas was analysed using gas chromatograph. The experimentation was designed and optimized using Response Surface Methodology.

An optimal production of biofuels from biomass is necessary in order to ensure its competitiveness. In this article, we consider the efficient production and procurement of biofuel to satisfy the demands of multiple users considering a multitude of factors such as the availability of multiple technologies, procurement of biomass from a variety of sources, optimal location for the production of biofuels along with various costs involved with respect to transportation, bio-feedstock and investment. This combinatorial problem is efficiently modelled so as to enable the use of computational intelligence based optimization techniques. The proposed strategy is successfully demonstrated with the help of Genetic Algorithm on a case study involving 3 feedstock sources, 2 central processing facilities, 4 industrial facilities with 8 users and 10 technologies.

In this work, we demonstrate the performance of a variant of the Heat Transfer Search algorithm which considers simultaneous heat transfer through all the three modes. It has been demonstrated on the thirty real parameter single objective, bound constrained benchmark functions that have been provided as part of one of the competition in IEEE Congress on Evolutionary Computation 2016. It was observed that the Simultaneous Heat Transfer Search (SHTS) algorithm has a very low time complexity and is competitive to other algorithms in terms of its ability to determine the optimal solutions.