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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.

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.