Recent developments in the controlled-release fertilizer (CRF) have led to the new modern agriculture industry, also known as precision farming. Biopolymers as encapsulating agents for the production of controlled-release fertilizers have helped to overcome many challenging problems such as nutrients’ leaching, soil degradation, soil debris, and hefty production cost. Mechanistic modeling of biopolymers coated CRF makes it challenging due to the complicated phenomenon of biodegradation. In this study, a machine learning model is developed utilizing Gaussian process regression to predict the nutrient release time from biopolymer coated CRF with the input parameters consisting of diffusion coefficient, coefficient of-variance of coating thickness, coating mass thickness, coefficient of variance of size distribution and surface hardness from biopolymer coated controlled-release fertilizer. The developed model has shown greater prediction capabilities measured with $R^2$ equalling 1 and a Root Mean Square Error ($RMSE$) equalling 0.003. The developed model can be utilized to study the nutrient release profile of different biopolymers’-coated controlled-release fertilizers.

In the current work, the heat flux in nucleate pool boiling has been predicted using the macrolayer and latent heat evaporation model. The wall superheat (ΔT) and macrolayer thickness (δ) are the parameters considered for predicting the heat flux. The influence of operating parameters on instantaneous conduction heat flux and average heat flux across the macrolayer are investigated. A comparison of the findings of current model with Bhat’s decreasing macrolayer model revealed a close agreement under the nucleate pool boiling condition at high heat flux. It is suggested that conduction heat transfer strongly rely on macrolayer thickness and wall superheat. The wall superheat and macrolayer thickness is found to significantly contribute to conduction heat transfer. The predicted results closely agree with the findings of Bhat’s decreasing macrolayer model for higher values of wall superheat signifying the nucleate boiling. The predicted results of the proposed model and Bhat’s existing model are validated by the experimental data. The findings also endorse the claim that predominant mode of heat transfer from heater surface to boiling liquid is the conduction across the macrolayer at the significantly high heat flux region of nucleate boiling.