Solar Thermal Aquaculture System Controller Based On Artificial Neural Network

Temperature is one of the most principle factors affects aquaculture system. It can cause stress and mortality or superior environment for growth and reproduction. This paper presents the control of pond water temperature using artificial intelligence technique. The water temperature is very important parameter for shrimp growth. The required temperature for optimal growth is 34oC, if temperature increase up to 38oC it cause death of the shrimp, so it is important to control water temperature. Solar thermal water heating system is designed to supply an aquaculture pond with the required hot water in Mersa Matruh in Egypt. Neural networks are massively parallel processors that have the ability to learn patterns through a training experience. Because of this feature, they are often well suited for modeling complex and non-linear processes such as those commonly found in the heating system. Artificial neural network is proposed to control water temperature due to Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems. Moreover this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the aquaculture system. The simulation results indicate that, the control unit success in keeping water temperature constant at the desired temperature by controlling the hot water flow rate. {"references": ["J. J. Carbajal1, L. P. S\u251c\u00ednchez, \"Classification based on fuzzy inference\nsystems for artificial habitat quality in shrimp farming\", In proc. Of\nIEEE conference Seventh Mexican International on Artificial\nIntelligence, 2008.", "Xiaojing Shena, Ming Chena, Jiang Yub, \"Water Environment\nMonitoring System Based on Neural Networks for Shrimp\nCultivation,\" In proc. Of IEEE International Conference on Artificial\nIntelligence and Computational Intelligence, 2009.", "Phillip G. Lee, \"Process control and artificial intelligence software for\naquaculture\", Aquacultural Engineering Vol., 23, PP., 13-36, 2000.", "S.A. Kalogeria, \"Applications of artificial neural networks in energy\nsystems A review\", Energy Conversion & Management, Vol., 40, PP.,\n1073-1087, 1999.", "Jin Woo Moon, Sung Kwon Jung, and Jong-Jin Kim, \"application of ann\n(artificial-neural-network) in residential thermal control\", building and\nsimulation, in proc of Eleventh International IBPSA Conference\nGlasgow, Scotland ,2009.", "Hossein Mirinejad, Seyed Hossein Sadati, Maryam Ghasemian and\nHamid Torab, \"control techniques in heating, ventilating and air\nconditioning (hvac) systems\" Journal of Computer Science Vol., 4 (9),\nPP., 777-783, 2008.", "Govind N. Kulkarni, Shireesh B. Kedare, Santanu Bandyopadhyay,\n\"Determination of design space and optimization of solar water heating\nsystems,\" Solar Energy, Vol. 81, PP. 958-968, 2007.", "John Gelegenis a, Paschalis Dalabakis b, Andreas Ilias, \" Heating of\na fish wintering pond using low-temperature geothermal fluids, Porto\nLagos, Greece,\" Geothermics, Vol. 35, PP. 87-103, 2006.", "Duffie, J., Beckman, W., Solar Engineering of Thermal Processes,\nsecond ed. John Wiley & Sons Interscience, NewYork, 1991.\n[10] Soteris A Kalogiroua, Soa Pantelioub, Argiris Dentsoras, \"Artificial\nneural networks used for the performance prediction of a thermosiphon\nsolar water heater\", Renewable Energy, Vol., 18, PP., 87-99, 1999.\n[11] James A. Freeman, David M. Skapura, Neural Networks Algorithms,\nApplications, And Programming Techniques, Addison-Wesley\nPublishing Company, Inc., Paris, 1991.\n[12] [M.N. Cirstea, A. Dinu, J.G. Khor, M. McCormick, \"Neural and Fuzzy\nLogic Control of Drives and Power Systems\", Replika Press Delhi ,\nIndia, 2002.\n[13] Soteris A. Kalogirou, \"Prediction of flat-plate collector performance\nparameters using artificial neural networks\", Solar Energy, Vol., 80, PP.,\n248-259, 2006.\n[14] Adnan Sozen, Tayfun Menlik, Sinan Unvar, \"Determination of\nefficiency of flat-plate solar collectors using neural network approach\",\nExpert Systems with Applications, Vol., 35, PP., 1533-1539, 2008."]}