Determination of Greenhouse Time Constant Using Steady-state Assumption

A study was conducted to determine the applicability of a steady-state solution in predicting the changes in temperatures of the inside air and thermal mass of a bench-top-heated greenhouse in response to a step change in outside temperature. The steady-state analysis is simpler than that of the transient. However, a steady-state solution would only be appropriate if the time constant of the greenhouse is short compared to the total time under which the conditions outside the greenhouse are considered to be approximately constant. A lumped parameter method based on Albright et at. (1985) was used to estimate the time constant of the bench-top-heated greenhouse. The time constant was found to be very sensitive to the heat transfer coefficient, It.., between the thermal mass and inside air. A high value of It.. results in a longer time constant. For the estimated thermal mass properties, the value of It,. for the temporarily approximately constant outside conditions was calculated to be 0.23 Wm·2 K' for which the estimated time constant was about 0.75 hour. This time was reasonably short compared to the six-hour experimental period; thus the steady-state analysis was appropriate

Factors affecting the cold flow behaviour of biodiesel and methods for improvement – a review

Biodiesel is an attractive renewable energy source, which is suitable as a substitute to the non renewable petroleum diesel. However, it is plagued by its relatively bad cold flow behaviour. In this review, the factors affecting the cold flow of biodiesel, vis-à-vis the contradicting requirement of good cold flow and good ignition properties, are discussed. Fuel filter plugging, and crystallization of biodiesel are considered, together with the cold flow properties such as Pour Point (PP), Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Low Temperature Filterability Test (LTFT). In addition, various methods used to improve the cold flow of biodiesel are also presented, with a special emphasis laid on the effects of these methods in reducing the Cloud Point. Strategies to improve cold flow, and yet maintaining the good ignition quality of biodiesel, are also proposed. As far as the cold flow of biodiesel is concerned, desirable attributes of its esters are short, unsaturated and branched carbon chains. However, these desirable attributes present opposing properties in terms of ignition quality and oxidation stability. This is because esters with short, unsaturated and branched carbon chains possess very good cold flow but poor ignition quality and oxidation stability. The target is therefore to produce biodiesel with good cold flow, sufficient ignition quality, and good oxidation stability. This target proves to be quite difficult and is a major problem in biodiesel research. New frontiers in this research might be the design of the new cold flow improvers that is similar to those used in the petroleum diesel but is tailored for biodiesel. Genetic modifications of the existing feedstock are also desirable but the food uses of this particular feedstock should always be taken into consideration

Effect of water content on backscattering parameters

Water content is one of the parameters that used to determine fruit quality. In this study, the effect of water content on the changes of backscattering parameters was investigated. The experiment was conducted on 240 bananas which vary from ripening stages 2 to 4. About half of the samples were stored at 6°C to induce chilling injury symptoms while the other were stored at 13°C and used as a control samples. The water content values were measured destructively on each sample based on the wet basis method. The results were compared with backscattering data that acquired using backscattering imaging. Results indicated there were significant differences (P<0.05) on the water content values and backscattering parameters as ripening stages increased. Results also revealed there were significant differences on the collected data as chilling injury developed. Hence, backscattering imaging is potentially useful for determining water content values and textural properties of fresh produce

Variability and predictability of Malaysian export-grade starfruit properties

The properties of commercially-graded B10 cultivar starfruit (Averrhoa carambola) namely length, diameter, mass, volume, the five colours values (L, a*, b*, C, h°), firmness, total soluble solids (TSS), titratable acidity (TA) and pH were measured. Coefficients of variation (CV), correlation and property tolerances were evaluated to develop a relationship between the non-destructively measurable external properties and the non-destructively immeasurable internal properties. The length, diameter, mass and hue were found to have low CV levels. Mass appears to have a very strong correlation with volume while hue has a strong correlation with firmness

Development of a disaster action plan for hospitals in Malaysia pertaining to critical engineering infrastructure risk analysis

Critical engineering infrastructure (CEI) risk analysis is one of the tools used to measure potential risks in order to develop a disaster action plan (DAP). For hospitals, electricity and water supplies are two of the critical engineering infrastructures and when affected can become threats themselves. The objectives of this study are: i) to identify and analyse threats to water and electricity supplies, the threats being of high tendency to be realised during floods and socio-technical disasters; and ii) to review existing disaster action plans and subsequently develop a comprehensive one for water and electricity supplies and to be referred together with existing hospital DAPs. The HIRARC (Hazard Identification, Risk Assessment and Risk Control) guidelines provided by DOSH (Department of Occupation Safety and Health, Malaysia) were employed in the risk analysis process. This process was conducted through questionnaire surveys at several hospitals around Kota Bharu, Kuala Krai, Kajang, Serdang and Klang which have been hit by floods before. The survey involved 400 hospital and their concession company employees through interviews, meetings and workshop sessions. The development of the action plan followed the UNISDR guidance note on Emergency and Disaster Preparedness for Health Facilities and fulfilled the four phases of disaster management; mitigation, preparedness, response and recovery. Results of the analysis revealed that threats faced by the hospitals fall into four major categories; (i) submergence (ii) technical failure (iii) unpreparedness and (iv) unexpected events. The study also revealed that while the threats faced by Kota Bharu and Kuala Krai hospitals were of high risk those faced by Kajang, Serdang and Klang hospitals were mostly medium or low. Therefore, it is concluded that a comprehensive DAP based on the set objectives of ensuring continuous water and electricity supply and the enhancement of coordination among state health departments, hospital managements, concession companies and utility providers must be developed

Prediction of total soluble solids and pH in banana using near infrared spectroscopy

The potential application of near infrared (NIR) spectroscopy in the range of wavelength from 1000 to 2500 nm to non-destructively determine total soluble solids (Brix) and pH values of bananas were evaluated. Thirty banana samples were measured at five different maturity stages. Each banana sample was scanned at three different locations (top, middle and bottom). The Brix and pH values were associated with the absorbance spectral data for the model development which were split into prediction and calibration sets. The partial least squares (PLS) model was built based on both data sets of banana samples. The prediction model for the Brix values obtained a coefficient of determination of 0.81 and root means square error of predictions of 3.91 Brix. The prediction model for pH values had an R2 of 0.69 and RMSEP of 0.36 pH. These findings proposed that near infrared spectroscopy has great potential to predict sugar content in bananas

Evaluation of a suitable thin layer model for drying of pumpkin under forced air convection

The thin layer drying kinetics of pumpkin slices (Cucurbita moschata) were experimentally investigated in a convective hot air dryer. In order to select the appropriate model for predicting the drying kinetics of pumpkin (Cucurbita moschata), twelve thin layer semi theoretical, theoretical and empirical models, widely used in describing the drying behaviour of agricultural products were fitted to the experimental data. The Page and Two term exponential models showed the best fit under certain drying conditions. The Hii et al. (2009) model, which was adopted from a combination of the Page and Two term models was compared to the other 11 selected thin layer models based on the coefficient of determination (R2) and sum of squares error (SSE). Comparison was made between the experimental and model predicted moisture ratio by non-linear regression analysis. Furthermore, the effect of drying temperature and slice thickness on the best model constants was evaluated. Consequently, the Hii et al. (2009) model showed an excellent fit with the experimental data (R2 > 0.99 and SSE < 0.012) for the drying temperatures of 50, 60, 70 and 80 °C and at different sample thicknesses of 3 mm, 5 mm and 7 mm respectively. Thus, the Hii et al. (2009) model can adequately predict the drying kinetics of pumpkin

Application of computer vision in the detection of chilling injury in bananas

Bananas were chilled at 6°C and the appearance of brown spots when exposed to ambient air, a phenomenon known as chilling injury (CI), was detected using computer vision. The system consisted of a digital colour camera for acquiring images, an illumination set-up for uniform lighting, a computer for receiving, storing and displaying of images and software for analyzing the images. The RGB colour space values of the images were transformed into that of HSI colour space which is intuitive to human vision. Visual assessment of CI by means of a browning scale was used as a reference and correlation between this reference values and hue was investigated. Results of the computer vision study successfully demonstrate the potential of the system in substituting visual assessment in the evaluation of CI in bananas. The results indicate significant influence, at α=0.05, of treatment days and temperature on hue. A strong correlation was also found between hue and visual assessment with R>0.85

Modelling the convective drying process of pumpkin (Cucurbita moschata) using an artificial neural network

This study investigated the drying kinetic of pumpkin under different drying temperatures (50, 60, 70 and 80°C), samples thickness (3, 4, 5 and 7mm), air velocity (1.2m/s) and relative humidity (40 - 50%). Kinetic models were developed using semi-theoretical thin layer models and multi-layer feed-forward artificial neural network (ANN) method. The Hii et al. (2009) semi-theoretical model was found to be the most suitable thin layer model while two hidden layers with 20 neurons was the best for the ANN method. The selections were based on the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and sum of squares error (SSE). Results indicated that the ANN demonstrated better prediction than those of the theoretical models with R2, RMSE and SSE values of 0.992, 0.036 and 0.207 as compared to the Hii et al. (2009) model values of 0.902, 0.088 and 1.734 respectively. The validation result also showed good agreement between the predicted values obtained from the ANN model and the experimental moisture ratio data. This indicates that an ANN can effectively describe the drying process of pumpkin

PREDICTION OF TOTAL SOLUBLE SOLIDS AND PH IN BANANA USING NEAR INFRARED SPECTROSCOPY

The potential application of near infrared (NIR) spectroscopy in the range of wavelength from 1000 to 2500 nm to non-destructively determine total soluble solids (Brix) and pH values of bananas were evaluated. Thirty banana samples were measured at five different maturity stages. Each banana sample was scanned at three different locations (top, middle and bottom). The Brix and pH values were associated with the absorbance spectral data for the model development which were split into prediction and calibration sets. The partial least squares (PLS) model was built based on both data sets of banana samples. The prediction model for the Brix values obtained a coefficient of determination of 0.81 and root means square error of predictions of 3.91 Brix. The prediction model for pH values had an R2 of 0.69 and RMSEP of 0.36 pH. These findings proposed that near infrared spectroscopy has great potential to predict sugar content in bananas