Research Publications

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Embargo
Computational Modelling of Drying Process in a Novel Solar Dryer Design with Experimental Validation
(SLIIT, Faculty of Engineering, 2024-10) Gunathilaka, R.A.C.K.; Kumar, R; Chatterjee, S; Bandara, R.M.P.S.
Crops and food products are dried by a variety of conventional methods, including open-air drying, smoking, and oven-drying for preservation purposes. Due to inherent drawbacks in the conventional drying methods, such as higher energy consumption, possible contamination and uncontrollable drying conditions, solar drying is preferred over the said drying methods. A solar dryer utilizes solar energy to dry crops, food products etc. by harnessing the heat energy from the sun to reduce the moisture content of the substances. The study focuses on modelling the drying process in an indirect type novel solar dryer through computational modelling with subsequent experimental validation of the temperature and air velocity profiles. The solar dryer is comprised of a divergent section, a convergent section, an absorber plate, a drying chamber, an outlet and trays. The Computational Fluid Dynamics (CFD) approach has been adopted in modelling the drying process and ANSYS Fluent has been used as the CFD tool. The computational mesh is comprised of 621,106 tetrahedral mesh elements. Pressurevelocity- coupling numerical scheme was used for discretizing the Navier-Stokes and other transport equations. A realizable k-ε model was applied in modelling turbulence. CFD simulations were conducted for three different mass flow rates of air: 0.0872 kg/s, 0.0636 kg/s, and 0.0447 kg/s at a solar insolation of 996 W/m². CFD simulations provided a comprehensive insight into the temperature and velocity profiles within the solar dryer. Furthermore, modelling results are well aligned with the experimental measurements taken on the solar dryer, confirming the reliability and accuracy of the computational model. The findings of this study will contribute as a platform for optimizing the performance of solar dryer designs.
Open Access
Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka
(Hindawi, 2021-05) Ekanayake, P; Rankothge, W; Weliwatta, R; Jayasinghe, J. M. J. W
This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.

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