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- Adsorption refrigeration technology has been intensively investigated in many countries of the world because of its potential for competing with conventional vapour compression refrigeration and its environmental friendliness. A solar-powered adsorption refrigerator using activated carbon/methanol pair was designed and fabricated. A mathematical model was developed based on the thermodynamics of the adsorption process, heat and mass transfer equations of the collector/generator/absorber components and simplified idealization of the condenser and evaporator components. The partial differential equations generated from the analysis were transformed into explicit finite difference forms for numerical solution. The model was used to compute the collector plate, bond and adsorbent temperatures, and the COP. The model was validated by using data from experiments performed on a solar powered activated carbon/methanol refrigerator and from published works. The predicted peak plate, tube and adsorbent temperatures were 102, 88 and 86°C respectively which compared favourably with 109 peak plate, 95 tube, and 85°C adsorbent temperatures from published works. The COP of the modelled refrigerator using imported activated carbon ranged from 0.0340 to 0.0345 compared to 0.0300 to 0.0550 recorded in the literature while the COP achieved from the experimental rig using locally manufactured activated carbon ranged from 0.0163 to 0.0200. Reducing the tube thickness from 5mm to 1.5mm led to a gain of 80.0% in COP. The adsorbent parking density of 550 kg/m3 gave an optimum COP, while a decrease of plate thickness from 1.5mm to 1.0mm increased the COP from 0.0338 to 0.0352. 1 results 1
- Adsorption, 1 results 1
- Autocorrelation 1 results 1
- Effective management planning tools for forest require growth and yield functions that can produce detailed predictions of stand development. Models such as Gamma Distribution Function (GDF), Weibull, Beta, and similar functions have been used to predict growth and yield of forest stands. However, information on the use of GDF in forest management has not been fully documented. The development of a program using Java programming language for GDF to predict growth and yield of Tectona grandis was studied in Akinyele Local Government Area, Oyo State, Nigeria. Stratified random sampling was used to select four different age classes of teak plantation namely; 11, 13, 22 and 59 years. Based on the size of each plantation, 7 and 8 temporary sample plots of 0.04 ha were selected from 11, 13, 22 and 59 year-old plantations respectively. Complete enumerations of trees (n = 433.) was done. Growth data sets collected include Diameter at Breast Height (DBH), total and merchantable heights. Basal Area (BA) and Volume were computed from measured variables. Data obtained were processed into tree level, stand level and size class. Parameters α and β for GDF were estimated from growth data. Based on the algorithm of GDF, α, β and n parameters, for the Java Program (JP) was written. Values obtained were fitted into the JP for growth and yield prediction. Linear and non- linear models were used to compare their predictive ability to the JP developed. At individual tree level using JP, the Observed and Predicted (O&P) values for height and BA ranged from 16.80-43.80 m, and 16.10-39.30 m; 2.49-4.51m2, and 2.45-4.31m2. Volume ranged from 2.09-10.54m3 and 2.04-12.03m3. Error rate varied from 0.00-9.00, -23.09-4.99 and -14.09-5.27 for height, BA and volume respectively. At stand level the O&P values for height, BA and volume from JP ranged from 17.10-28.30 and 17.90- 32.10 m; 2.55-3.69 m2 and 2.58-3.69m2; 2.25-3.69m3 and 2.28-3.69 m3 with error rate of -2.77-13.4; -0.10-5.65 and -0.10 -0.40 respectively. Size class level shape and scale parameter of GDF for diameter distribution ranged from 0.96-25.20 and 0.07-2.28 respectively. These values have better predictive power than non-linear and linear models which at individual tree level, O&P values for height and BA models of best fit ranged from 16.80-43.80m and 15.86-39.00 m; 2.49-4.51m2 and 2.50-4.98m2 . For volume, it ranged from 2.09-10.54m3 and 2.02-12.05m3 with error rate of -14.32-6.37. At stand level, O&P ranged from 17.10-28.30m and 17.95-32.18m for height; 2.55-3.69 and 2.59-3.72 m2 for BA and 2.25-3.69 and 2.29-3.65m3 for volume with error rate from -2.88-13.71; -4.58 -0.81 and -1.77-1.08 respectively. The R2 values for height, BA and volume models of best fit were 0.9490, 0.8981 and 0.9800 with the equations given as H= [1.31.08 + (H1.08 dom -1.31.08)1-e-0.06dbh/1-e-0.06*1.08 dom]1/1.08, 1n(B) = ln(0.32)+ 0.42(1/A)+ 0.77(lnH)+1.82(lnN)+1.89(H/A) and V= 1.62+22.38*DBH. The predictive ability of gamma distribution function for height, basal area and volume for teak plantation from the developed Java program consistently performed better than other models and could therefore be used for prediction of growth and yield in forest stands. Keywords: Gamma distribution function, Teak plantation, Growth and yield models, Forest management Word count: 498 1 results 1
- Estimators 1 results 1
- First-order Autoregressive [AR (1)] process 1 results 1
- Forest management 1 results 1
- Gamma distribution function 1 results 1
- Growth and yield models 1 results 1
- Methanol 1 results 1
- Monte Carlo 1 results 1
- Refrigeration, 1 results 1
- Simultaneous equation model 1 results 1
- Solar, 1 results 1
- Teak plantation 1 results 1
- The estimation of the parameters of simultaneous equation problem is usually affected by the existence of mutual correlation between pairs of random deviates, which is a violation of the assumption of no autocorrelation between the error terms. In practice the form of correlation between the pairs of random deviates is not known. This study therefore examined a two-equation model in which the correlation between the random deviates is assumed to follow a first-order Autoregressive [AR (1)] process. Data was simulated using Monte Carlo approach with varying sample sizes each replicated 1000 times. The behaviour of OLS, 2SLS, LIML and 3SLS were evaluated using Variance, Root Mean Square Error (RMSE) and Absolute Bias (AB). The absolute bias estimates decrease in most cases as the sample size increases. The variances obtained by all the estimators reduced consistently as the sample size increases. There was no clear pattern in the behaviour of the RMSE across sample sizes. The results for = 0.3 were better than when = 0.0 with respect to each criterion but retained the same pattern. This work established that when was different from zero, the estimators performed better, hence the choice of should be carefully made as this may significantly affect the performances of the estimators 1 results 1
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