S‌E‌N‌S‌I‌T‌I‌V‌I‌T‌Y A‌N‌A‌L‌Y‌S‌I‌S O‌N T‌H‌E E‌F‌F‌E‌C‌T‌I‌V‌E P‌A‌R‌A‌M‌E‌T‌E‌R‌S O‌F G‌R‌O‌U‌N‌D D‌E‌F‌O‌R‌M‌A‌T‌I‌O‌N V‌A‌L‌U‌E B‌Y M‌I‌C‌R‌O T‌U‌N‌N‌E‌L‌I‌N‌G M‌E‌T‌H‌O‌D W‌H‌I‌L‌E P‌I‌P‌E J‌A‌C‌K‌I‌N‌G O‌P‌E‌R‌A‌T‌E‌S

I‌n r‌e‌c‌e‌n‌t y‌e‌a‌r‌s, m‌i‌c‌r‌o t‌u‌n‌n‌e‌l‌i‌n‌g m‌e‌t‌h‌o‌d h‌a‌s b‌e‌e‌n d‌e‌v‌e‌l‌o‌p‌e‌d q‌u‌i‌c‌k‌l‌y i‌n c‌o‌m‌p‌a‌r‌i‌s‌o‌n t‌o o‌t‌h‌e‌r o‌p‌e‌n e‌x‌c‌a‌v‌a‌t‌i‌o‌n m‌e‌t‌h‌o‌d‌s. M‌i‌c‌r‌o t‌u‌n‌n‌e‌l‌i‌n‌g i‌s a m‌e‌t‌h‌o‌d f‌o‌r i‌n‌s‌t‌a‌l‌l‌i‌n‌g t‌h‌e p‌i‌p‌e‌l‌i‌n‌e‌s, c‌h‌a‌n‌n‌e‌l‌s a‌n‌d u‌n‌d‌e‌r‌g‌r‌o‌u‌n‌d c‌o‌n‌d‌u‌i‌t‌s.I‌n r‌e‌c‌e‌n‌t y‌e‌a‌r‌s, m‌i‌c‌r‌o t‌u‌n‌n‌e‌l‌i‌n‌g m‌e‌t‌h‌o‌d h‌a‌s b‌e‌e‌n d‌e‌v‌e‌l‌o‌p‌e‌d q‌u‌i‌c‌k‌l‌y c‌o‌m‌p‌a‌r‌e‌d t‌o o‌t‌h‌e‌r o‌p‌e‌n e‌x‌c‌a‌v‌a‌t‌i‌o‌n m‌e‌t‌h‌o‌d‌s. I‌n t‌h‌i‌s m‌e‌t‌h‌o‌d, h‌y‌d‌r‌a‌u‌l‌i‌c j‌a‌c‌k‌s a‌r‌e u‌s‌e‌d f‌o‌r p‌i‌p‌e d‌r‌i‌v‌i‌n‌g w‌i‌t‌h a s‌p‌e‌c‌i‌a‌l d‌e‌s‌i‌g‌n. T‌h‌e p‌i‌p‌e‌s a‌r‌e p‌l‌a‌c‌e‌d b‌e‌h‌i‌n‌d t‌h‌e e‌x‌c‌a‌v‌a‌t‌i‌o‌n d‌r‌i‌l‌l, d‌r‌i‌v‌e‌n i‌n‌t‌o t‌h‌e g‌r‌o‌u‌n‌d a‌l‌o‌n‌g w‌i‌t‌h t‌h‌e g‌r‌o‌u‌n‌d e‌x‌c‌a‌v‌a‌t‌i‌o‌n s‌i‌m‌u‌l‌t‌a‌n‌e‌o‌u‌s‌l‌y. T‌h‌e r‌e‌s‌u‌l‌t o‌f t‌h‌i‌s m‌e‌t‌h‌o‌d i‌s a f‌l‌e‌x‌i‌b‌l‌e, i‌m‌p‌e‌r‌m‌e‌a‌b‌l‌e a‌n‌d r‌e‌s‌i‌s‌t‌a‌n‌t s‌t‌r‌u‌c‌t‌u‌r‌e c‌o‌n‌s‌t‌r‌u‌c‌t‌i‌o‌n p‌i‌p‌e‌l‌i‌n‌e‌s. T‌h‌e m‌i‌c‌r‌o t‌u‌n‌n‌e‌l‌i‌n‌g m‌e‌t‌h‌o‌d r‌e‌d‌u‌c‌e‌s t‌h‌e r‌e‌q‌u‌i‌r‌e‌m‌e‌n‌t n‌u‌m‌b‌e‌r o‌f t‌r‌a‌n‌s‌p‌o‌r‌t‌i‌n‌g m‌a‌t‌e‌r‌i‌a‌l‌s f‌o‌r o‌p‌e‌r‌a‌t‌i‌o‌n a‌c‌t‌i‌v‌i‌t‌i‌e‌s a‌n‌d t‌h‌e m‌a‌c‌h‌i‌n‌e‌s t‌r‌a‌f‌f‌i‌c a‌n‌d d‌e‌v‌i‌c‌e‌s. T‌o‌o A‌s a r‌e‌s‌u‌l‌t, i‌t‌s e‌v‌e‌n‌t‌s a‌r‌e r‌e‌d‌u‌c‌e‌d. A‌c‌c‌o‌r‌d‌i‌n‌g t‌o t‌h‌i‌s m‌e‌t‌h‌o‌d, a‌l‌m‌o‌s‌t a‌l‌l t‌h‌e a‌c‌t‌i‌v‌i‌t‌i‌e‌s a‌r‌e p‌e‌r‌f‌o‌r‌m‌e‌d u‌n‌d‌e‌r‌g‌r‌o‌u‌n‌d. H‌e‌n‌c‌e, t‌h‌e n‌e‌g‌l‌i‌g‌i‌b‌l‌e i‌n‌t‌e‌r‌f‌e‌r‌e‌n‌c‌e‌s a‌r‌e o‌c‌c‌u‌r‌r‌e‌d w‌i‌t‌h e‌n‌v‌i‌r‌o‌n‌m‌e‌n‌t. T‌h‌u‌s, t‌h‌e a‌m‌o‌u‌n‌t o‌f e‌n‌v‌i‌r‌o‌n‌m‌e‌n‌t‌a‌l d‌e‌t‌e‌r‌i‌o‌r‌a‌t‌i‌o‌n i‌s v‌e‌r‌y l‌o‌w. I‌n s‌o‌m‌e c‌a‌s‌e‌s, p‌e‌r‌f‌o‌r‌m‌i‌n‌g t‌h‌e o‌p‌e‌r‌a‌t‌i‌o‌n, i‌n‌c‌r‌e‌a‌s‌e‌s t‌h‌e n‌e‌g‌a‌t‌i‌v‌e e‌f‌f‌e‌c‌t‌s o‌f v‌i‌b‌r‌a‌t‌i‌o‌n‌s f‌r‌o‌m p‌i‌p‌e d‌r‌i‌v‌i‌n‌g a‌n‌d c‌a‌u‌s‌e‌s t‌h‌e p‌r‌o‌b‌l‌e‌m‌s s‌u‌c‌h a‌s s‌e‌t‌t‌l‌e‌m‌e‌n‌t‌s o‌r s‌w‌e‌l‌l‌i‌n‌g (h‌e‌a‌v‌i‌n‌g) o‌f t‌h‌e g‌r‌o‌u‌n‌d s‌u‌r‌f‌a‌c‌e. U‌p‌o‌n a‌n‌a‌l‌y‌s‌i‌s, s‌o‌m‌e p‌a‌r‌a‌m‌e‌t‌e‌r‌s h‌a‌v‌e m‌o‌r‌e i‌n‌f‌l‌u‌e‌n‌c‌e‌s r‌a‌t‌h‌e‌r t‌h‌a‌n o‌t‌h‌e‌r‌s, a‌n‌d s‌o‌m‌e o‌f t‌h‌e‌m c‌o‌u‌l‌d b‌e i‌g‌n‌o‌r‌e‌d d‌u‌e t‌o l‌o‌w i‌m‌p‌o‌r‌t‌a‌n‌c‌e i‌n t‌h‌e m‌o‌d‌e‌l. T‌h‌e‌r‌e‌f‌o‌r‌e, i‌n‌v‌e‌s‌t‌i‌g‌a‌t‌i‌n‌g t‌h‌e e‌f‌f‌e‌c‌t o‌f v‌a‌r‌i‌o‌u‌s i‌n‌d‌e‌p‌e‌n‌d‌e‌n‌t p‌a‌r‌a‌m‌e‌t‌e‌r‌s i‌s i‌m‌p‌o‌r‌t‌a‌n‌t, i.e. s‌e‌n‌s‌i‌t‌i‌v‌e a‌n‌a‌l‌y‌s‌i‌s. T‌h‌e a‌i‌m o‌f t‌h‌i‌s s‌t‌u‌d‌y i‌s t‌o c‌o‌m‌p‌a‌r‌e t‌h‌e a‌c‌t‌u‌a‌l d‌e‌f‌o‌r‌m‌a‌t‌i‌o‌n‌s o‌c‌c‌u‌r‌r‌e‌d i‌n g‌r‌o‌u‌n‌d s‌u‌r‌f‌a‌c‌e d‌u‌e t‌o b‌e‌h‌a‌v‌i‌o‌r s‌u‌r‌v‌e‌y w‌i‌t‌h t‌h‌e v‌a‌l‌u‌e‌s o‌f n‌u‌m‌e‌r‌i‌c‌a‌l m‌o‌d‌e‌l‌i‌n‌g. I‌n a‌d‌d‌i‌t‌i‌o‌n, t‌h‌i‌s s‌t‌u‌d‌y a‌i‌m‌s t‌o i‌n‌v‌e‌s‌t‌i‌g‌a‌t‌e t‌h‌e e‌f‌f‌e‌c‌t o‌f v‌a‌r‌i‌o‌u‌s p‌a‌r‌a‌m‌e‌t‌e‌r‌s o‌n s‌o‌i‌l s‌e‌t‌t‌l‌e‌m‌e‌n‌t v‌a‌l‌u‌e‌s

Design of a Micro-Probe For Direct Measurement of Convection Heat Transfer on a Vertical

A proximity probe with two k-type thermocouples, 1.5 mm apart, was designed, built to simultaneously measure local surface and air temperatures on the PV and to quantify local convention heat transfer coefficient. Experimental investigations of natural convection on a vertical photovoltaic (PV) panel exposed to solar radiations are presented. The variation of non-isothermal surface temperature of a PV is expressed with a second-order polynomial relation. In the absence of any correlation to predict the natural convection heat transfer coefficient on a PV, experimental results are presented in the form of variations of the local Nusselt numbers (Nuz), and the average Nusselt numbers (Nu), with Rayleigh number (Ra). The variations are best expressed with a power law correlation form of Nu=a*(Ra)^b for the range 10^6 <Ra<10^8 where a and b are determined experimentally. The power-law correlations for photovoltaic were compared with a number of correlations developed from natural convection research in laboratories. The analysis showed that for a given Rayleigh number, the predicted value of Nusselt number by the PV correlations are within the range covered by others. However, the PV correlations overestimate the Nusselt number by 20% in Rayleigh number higher than 10^6 . The work is in progress to further extend the correlation to predict the combined radiation and convection on all PV configurations, as required in the efficient design of building integrated photovoltaic (BIPV) systems

Comprehensive analysis for air supply fan faults based on HVAC mathematical model

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems. © (2012) Trans Tech Publications

Residual compressive stress–strain relationship of lightweight aggregate concrete after exposure to elevated temperatures

This experimental study investigated the compressive behavior of lightweight concrete after exposure to elevated temperatures. In total, 240 samples from 30 different mixtures were prepared and tested to evaluate the compressive strength, elastic modulus, strain at peak stress, and stress–strain relationships of lightweight aggregate concrete (LWAC) after being exposed to elevated temperature of 250, 500, and 750 °C. Test variables were composed of cement content varied between 300 and 700 kg/m3, volumetric percentage of lightweight expanded clay aggregates (Leca) substituting natural sand and gravel at 0, 25, 50, 75, and 100% (by weight), silica fume replacing cement at 5, 7.5, 10, 12.5, and 15% (by weight) and different water to cement ratios of 0.250, 0.313, 0.375, 0.438, and 0.500. The compressive strength, elastic modulus, and strain in the maximum stress were compared with predictions from the American and European standards and the proposed analytical models. The results showed that the compressive strength and elastic modulus of LWAC declined with increasing the temperature. The samples S6, S4 and S23 performed better compared to other specimens at elevated temperature as they retained about 96%, 75% and 42% of compressive strength, respectively after exposure to 250, 500 and 750 °C. A higher residual compressive strength was observed in the sample including 75% Leca at 750 °C. The experimental stress–strain relationship was verified according to the available analytical models and an analytical model was proposed to estimate the compressive behavior of LWAC at elevated temperature

Application of Impregnated Almond Shell Activated Carbon by Zinc and Zinc Sulfate for Nitrate Removal from Water

In this study impregnated almond shell activated carbon by Zn° and ZnSO4 were used as adsorbent with a particle size of 10-20 mesh. The objective of this research was to determine the ability of impregnated activated carbon in nitrate removal. The modified activated carbon had 1mm effective size, with a uniformity coefficient of 1.18. Potassium nitrate solution was used in batch adsorption experiments for nitrate removal from water. The effects of nitrate concentration, activated carbon dosage and time of contact were studied. Experimental data showed that modified activated carbon by Zn° and ZnSO4 was more effective than virgin almond activated carbon for nitrate removal. The maximum nitrate removal was 64%-80% and 5%-42% for modified activated carbon and virgin activated carbon, respectively. While virgin activated carbon used, nitrate-N decreased from 20 to 15mg/L in 30min reaction. The final nitrate concentration was not in the standard range of WHO recommendations for water quality; while impregnated activated carbons were used, nitrate drcreased to <10mg/L. Maximum removal was over 16-17mg nitrate-N per 1g activated carbon for impregnated activated carbon. The experiments were conducted at pH=6.2, 20ºC and initial concentrations of 20mg/L nitrate-N. Increase in modified activated carbon dosage increased the nitrate removal efficiency. The equilibrium time was found to be 45min for modified activated carbon

Isolation And Characterization Of A Novel Denitrifying Bacterium With High Nitrate Removal: Pseudomonas Stutzeri

The aim of this study was to isolate and characterize a high efficiency denitrifier bacterium for reducing nitrate in wastewater. Six denitrifier bacteria with nitrate removal activities were isolated from a petrochemical industry effluent with high salinity and high nitrogen concentrations without treatment. The isolated bacteria were tested for nitrate reomoval activity. One of the bacterium displayed the highest reduction of nitrate. The strain was preliminarily identified using biochemical tests and further identified based on similarity of PCR-16S rRNA using universal primers. Biochemical and molecular experiments showed that the best bacterium with high nitrate removal potential was Pseudomonas stutzeri, a member of the α subclass of the class Proteobacteria. The extent of nitrate removal efficiency was 99% at 200 mg/L NO3 and the nitrite content of the effluent was in the prescribed limit. The experiments showed the ability of Pseudomonas stutzeri to rapidly remove nitrate under anoxic conditions. The strain showed to be potentially good candidate for biodenitrification of high nitrate solutions

Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model

Efficient heating, ventilation, and air-conditioning (HVAC) systems are one of the big challenges today around the world. The fault detection and isolation (FDI) play a significant role in the monitoring, repairing and maintaining of technical systems for the final destination of cost reduction. FDI makes it possible to reduce total cost effective of maintenance and thus increase the capacity utilization rates of equipment. Reduction of energy wasting in the system by on time fault detection is another goal. Therefore, this work proposes a new fault detector based on a black box Artificial Neural Network (ANN) model and online support vector machines (SVM) classifier which integrates a dimension reduction scheme to analyze the failure of air fan supply and dampers fault. The key advantage of this algorithm is to make robustness for SVM to recognize a faulty condition with unexpected sensors values. The ANN generates a high accurate model which is based reference for SVM classifier. Now by using this black box model we make possibility of robustness for SVM to increase detection probability. Finally, a series of faulty experimental data are applied to evaluate the effectiveness of the robust classifier. Final results show that online SVM can detect accurately the air supply fan fault and damper fault of a HVAC system with minimum usage data. It is also outperforms offline SVM on such energy systems for classification. © 2013 EUCA