Response Surface Methodology (RSM) Optimization of the Batch Process in a Rectangular Passive Greenhouse Dryer

Abstract

The prototyping of dryer design and performance by application of the trial-and-error technique in one-factor-at-a-time (OFAT) testing is completely arbitrary, expensive and time consuming. Reducing product development lead-time and cost while concurrently improving customer satisfaction for a good manufacturer enhance rapid response to market demand which is a highly effective way of improving returns on investment. In this study a numerical model for the digital prototyping of the rectangular passive greenhouse dryer design and the optimization of the batch process in the solar dryer was developed. An interactive, user-friendly computer package ANSYS 14.0 was used to develop an empirical model. The package was used among others, for the response surface methodology (RSM) optimization to specify the dryer parameters that maximize the dryer mean temperature. The factorial experiments in a central composite design (CCD) revealed that only the inlet vent dimensions influence the mean temperature within the greenhouse dryer. The parametric analysis for robust design yielded the inlet vent height of 0.27m and inlet vent width of 0.45m as the optimum design variables that maximize the mean temperature of the drying air as 320.48K (47.30 °C). The numerical approach established facilitated the prototyping and optimization of the batch process in the passive greenhouse dryer.The prototyping of dryer design and performance by application of the trial-and-error technique in one-factor-at-a-time (OFAT) testing is completely arbitrary, expensive and time consuming. Reducing product development lead-time and cost while concurrently improving customer satisfaction for a good manufacturer enhance rapid response to market demand which is a highly effective way of improving returns on investment. In this study a numerical model for the digital prototyping of a rectangular passive greenhouse dryer design and the optimization of the batch process in the solar dryer was developed. Multiple regression was used as the data-analytic system for the factorial experiment to develop an empirical model, predict the response variable and then test hypothesis in an interactive, user-friendly computer package ANSYS 14.0. The package was further used for the response surface methodology (RSM) optimization to specify the dryer parameters that maximize the dryer mean temperature. The factorial experiments in a central composite design (CCD) revealed that only the inlet vent dimensions influence the mean temperature within the dryer. Appraisal of the model through the coefficient of determination ( =0.99973) showed that the model can account for 99.973% variability observed in the dryer mean temperature consequently, the suitability of RSM for the analysis of the dryer variables. The parametric analysis for robust design yielded the inlet vent height of 0.27m and inlet vent width of 0.45m as the optimum design variables that maximize the mean temperature of the drying air as 320.48K (47.30°C). The numerical approach established facilitated the prototyping and optimization of the batch process in the passive dryer.