Problem statement:This study was conducted to examine the effects of irrigation water pH and Salinity on the growth and absorption of P, Na, Ca, K by tomato. Approach:The study includes two Salinity and pH factors and is consisted from 12 treatment and three repetitions. Tomato seeding grown in foam trays were transplanted in the joune 2010 to bags filled with perte in an Greenhouse at Damghan Islamic Azad University of Iran. Plant were divided into groups then irrigated with the targeted sane and pH levels. Plants were hand-irrigated with fresh water and fertized with required nutritional solutions were prepared based on bed nutrients mitation. Greenhouse temperature was maintained in suitable level using air conditioner and its humidity was controlled by hygrometer and adjusted in the range of 60-80%. Water Salinity factors were consisted from four levels (0, 3, 6 and 9 dsm −1 ) and pH factor was consisted from three levels (6.5, 7.5 and 8.5). Salinity and pH treatments were adjusted with Nacl and H 2 SO 4 /N 2 CO 3 salts respectively. Study of the effects of Salinity and pH level on tomato were recorded and controlled depending on number of growing fruit, fertized flowers, plant dry weight, plant height, percentage of P, Na, Ca, K in leaves. Then results were studied by Anova Variance Analysis using SAS software and obtaining significant results, Dunken test was used for comparison of average levels in probabity level of 5%. Results: Data showed that all growth parameters such as plant height, leaf area, plant dry weight, percentage of P,Ca,K in leave responded negatively as the Salinity and pH level increased. Only Na + content in the leaves responded positively to increment in Salinity and pH level. Conclusion: Based on results, Salinity reduced plant height as well as dry weight and increasing of Salinity and pH increased supply of Na+ in tomato leaf.
In the present paper we develop an application of the optimal predictive control to building heating, ventilating and air conditioning (HVAC) systems. Explicit inequality constraints on the input and on the output of the system are considered. A specific model-based recursive parameter identification and fault detection approach is described. Simplified physical modeling of the process establishes the model structure of an adaptive predictor to be used both for identification and control. The adaptive predictor is based on the estimation of the states and the parameters of the process are updated using a two stages filtered instrumental projection method, described in the paper, that insures the promptness of fault detection.Fault detection is based on specific processing of the prediction errors and parameters of the predictor. Process diagnosis is ensured by appropriate use of the qualitative knowledge about the process. In order to increase the robustness of the fault detection scheme. additional test signals are introduced in the process. The resulting algorithm is a unified supervisory scheme for control identification and fault detection that provides information of failures in sensors and amators via diagnosis of abrupt changes in process parameters and discrimination of unmeasured disturbances that act on the system. Experimental results are presented.
Abstract:In this study a two-phases, single-domain and non-isothermal model of a Proton Exchange Membrane (PEM) fuel cell has been studied to investigate thermal management effects on fuel cell performance. A set of governing equations, conservation of mass, momentum, species, energy and charge for gas diffusion layers, catalyst layers and the membrane regions are considered. These equations are solved numerically in a single domain, using finite-volume-based computational fluid dynamics technique. Also the effects of four critical parameters that are thermal conductivity of gas diffusion layer, relative humidity, operating temperature and current density on the PEM fuel cell performance is investigated. In low operating temperatures the resistance within the membrane increases and this could cause rapid decrease in potential. High operating temperature would also reduce transport losses and it would lead to increase in electrochemical reaction rate. This could virtually result in decreasing the cell potential due to an increasing water vapor partial pressure and the membrane water dehydration. Another significant result is that the temperature distribution in GDL is almost linear but within membrane is highly non-linear. However at low current density the temperature across all regions of the cell dose not change significantly. The cell potential increases with relative humidity and improved hydration which reduces ohmic losses. Also the temperature within the cell is much higher with reduced GDL thermal conductivities. The numerical model which is developed is validated with published experimental data and the results are in good agreement.
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