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In this study, the use of computer vision and artificial intelligence technology is examined as a means of improving the efficacy and sustainability of fish feeding in aquaculture. The proposed approach employs machine learning techniques to estimate feeding requirements and optimise feeding schedules, while image analysis is employed to track fish behaviours and appetite. The method aims to increase the growth and health of fish while reducing waste and the environmental impact of aquaculture operations.

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This paper presents a closed cycle gas turbine power plant to utilize low temperature and low quality geothermal steam and water, that cannot be utilized in a steam turbine type of power plant. Fluids which have critical temperatures below 300 F, the usual geothermal water temperatures, can be utilized. This paper compares performances of power cycles using various fluids and establishes that carbon dioxide is the most suitable fluid for this application. The paper then compares performances of carbon dioxide cycles and establishes the best thermodynamic region for cycle operation. The generalized characteristics approach was used to analyze cycle performances and is briefly presented. It enables quick comparisons to be made for selection of an optimum cycle. The paper then considers a 2000-kw power plant operating on a closed CO2 cycle, establishes the geothermal water requirements and the system design. System control, and start-up problems are also discussed.