Modeling and Optimization of a Greenhouse-type Solar Dryer System

Abstract

Solar drying of agricultural products is a practice that has become popular not only because it uses renewable energy as the main source of heat but also because it can reduce the waste of products with a high water content that, under normal circumstances, have a short shelf life. Solar drying in controlled environments is the safest and most effective way to dehydrate from being contaminated during the process by dust, insects, birds, rain, and other environmental factors, which reduce the quality of the final product and increase the time it takes to dry as open sun drying depends on the ambient environmental conditions. Other advantages of drying in controlled environments, for instance in a greenhouse, include the ability to use the greenhouse effect in favor, heating the air before introducing it into the drying chamber or directly in the drying chamber, reaching temperatures higher than those present in the outdoor environment; the capacity for precision control and the reduction of UV radiation, which can decompose and change the color of the products, produce dehydrated foods in less time and with better qualitative characteristics without losing vitamins or nutritional substances that are desirable to preserve. The mathematical modeling of greenhouses as solar dryers is of interest due to the complexity of the drying process. Since drying is a process that involves mass and heat exchanges, changes of state in the matter, and with different scales, the modeling approach can be diverse for various products with different geometries, coefficients, and physical characteristics, as well as the dryer’s physical attributes. The mathematical modeling of [greenhouse as] solar dryers is to the complexity of the drying process. Since drying is a process that involves mass and heat exchanges, changes of state in the matter, and with different scales, the modeling approach can be diverse for various products with different geometries, coefficients, and physical characteristics, as well as the dryer’s physical attributes. The thin-layer models have predominated over the modeling of mass and energy exchange between air and the product in the form of ordinary differential equations, partial equations, or finite-difference equations. The thin-layer approach is limited when the temperature at which the product is dehydrated is not constant, as well as if the thickness is greater than 5 mm. The models with differential equations, based on energy and mass balances, have an advantage over those of thin layers because they contemplate more states and variables than just the drying time. However, one limitation is how the processes are modeled at the product level. If the air inside a dryer is considered, the volume will be greater than the volume of the product and the change in the mass of water that is inside the product causes problems when trying to model it with two different size scales. These differential equations usually use the thin-layer approximation, thus achieving that the change in the moisture content of the product can be modeled once the thin-layer drying of the product has been studied. Other approaches for product modeling solve mass and energy balances, but only up to the product boundaries where the surrounding drying medium is not considered. Computational Fluid Dynamics (CFD) allows for understanding the airflow that is generated in the different dryers and studying the variation in temperature due to the design and even the position of the sun. Nevertheless, as with the ordinary differential equations approach, product modeling remains a challenge to overcome. The CFD is based on partial differential equations that can be approximated with algebraic equations when the volume of the dryer is discretized and a numerical model is used, that converts each node into an equation to be solved for each variable. The associated computational time and cost increase as the problem domain volume increases and simulations can take up to days. The greatest advantage of this approach is its ability to understand the process in three-dimensional form and visualize the air inside the dryer providing greater details about the flow field. In addition, it allows to evaluate various design ideas and what-if scenarios cost and time effectively and offers recommendations for improvements. Of all the types of dryers, the most common is the cabinet dryer, a small structure (<1 m2) where the air is preheated in sections where there is only air. Then, it is introduced to the drying chamber, where the product is located. The air remains until the desired temperature is reached, or the air is saturated with the moisture of the product. Finally, the air is extracted from the drying chamber through a chimney and the cycle is restarted with a renewal of preheated air. The generally small size of the drying chamber and preheating element make this type of dryer favorable for home production or supervised conditions. This type of dryer is not an option for scale-up to increase capacity.The greenhouse design has the drying chamber and the air preheating completed in the same area. They are usually larger and have a semi-transparent cover allowing sunlight to pass through and warm the air by solar heating. With its much larger size (>100 m2), a greater quantity of product can be dried, and the quality of the product can be monitored periodically without affecting the internal drying conditions of the greenhouse. However, increased size creates problems including poor and non-uniform air distribution, the possibility of over-drying the product if the temperature is not well controlled, and the need for more labor to manage the greenhouse in terms of product preparation, positioning, cleaning, and collecting. The design of the greenhouse, such as the use of special covers with filters that can capture long-wave radiation and reduce the amount of UV light can also be considered in the design of these dryers. In addition, studies suggest the possibility of having greenhouses designed for the dual purpose of agricultural production and product drying. Even with the disadvantages of increasing the size, less contamination, the possibility to control the environmental conditions, and reducing the drying time is enough to consider greenhouses for drying. A greenhouse-type solar dryer was built and evaluated at the Universidad Autonoma Chaping (Mexico). The initial efforts focused on investigating sliced tomato product drying with data collection, and with 35 different thin-layer drying models that were evaluated for drying tomatoes to determine the best model for this type of drying system and the dried product. The innovation and contribution of this work were the evaluation of semi-theoretical and empirical models, analyzing the number of parameters, the assumptions, and the accuracy of the model predictions confirmed with the data sets available that were not used as part of the model calibration. The latter has not been included in other thin layer model-based studies, as well as identifying the models with less complexity and better prediction accuracies. The results indicate that the best empirical thin layer model was the Regression model with two parameters, an R2 of 0.994 and an RMSE of 0.059 when presented with new data (data not used during the calibration process). The best semi-theoretical model was the modified Page VI model with two parameters, an R2 of 0.993, and an RMSE of 0.06 with new experimental data. The Page VI model was recommended for this study since it is a theoretical model with only two parameters which reduces the complexity during model calibration. The study also concluded not to continue developing thin-layer models since most of them have already been demonstrated with good performance, or to model with other approaches where more parameters and variables are involved as they remain under development. Although it was concluded that the use of thin-layer models greatly simplifies the modeling of moisture content in agricultural products, they are still limited to a detailed understanding of the driving mechanisms and phenomena behind solar drying. The second part of this dissertation research designed a controller based on black box models with the subspace state space system identification (N4SID) approach. The control simulation was performed using the methodology of the Model Predictive Controller (MPC), The results indicated that there was a reduction in the amount of fan operation required with the use of the MPC control when compared to a controller based on a setpoint and an ON/OFF strategy. The idea to use an MPC control was for its ability to use measurements and models to decide the optimal control strategy over time given the real system actuators limitations. The innovation presented with the MPC based control approach is the use of system identification to generate a model in state space for the greenhouse-type solar dryer system considering the product temperature, air temperature, air relative humidity, greenhouse soil, and cover temperatures. The ventilation rate was the input control variable while the air temperature, relative humidity, and solar radiation outside the greenhouse were considered as disturbances. The MPC controllers usually require actuators with analog feedback; however, the fans used in this study, ran only at full capacity as soon as they were turned ON. This allowed the fans to be considered separate (being able to turn just one or both) and together (both activated at the same time) to define the ventilation rate required, which is a novelty used for this control approach. Another important feature of this control strategy was to consider the product temperature as the variable to make the control decision instead of the air temperature which is traditionally controlled. The study indicated that the air temperature can be at a value much higher than that required in tomato slices, something not previously studied. Finally, a CFD model was developed, validated, and used to study a greenhouse-type solar dryer. An extensive literature review was performed on the state of the art in CFD modeling for greenhouse-type solar dryers. The review revealed that the CFD modeling studies for greenhouse solar drying systems are still limited. The current study evaluated air temperature distribution in the internal volume of the dryer when the exhaust fans were not operating and there was a cloudy day. The dryer, by design, had its air inlets always open, so there could be an exchange between the external and internal conditions of the air. The model was evaluated along with measurements and once the model was validated, it was used to evaluate design alternatives in the dryer. An alternative air distribution system design was proposed and evaluated that can enhance environmental uniformity and provide desired air temperatures, especially at the drying product locations in the greenhouse solar dryer system. Due to the observed stratification in the air temperature inside the dryer, it was decided to test whether forcing the air from the greenhouse attic to flow under the drying tables improved the process conditions (air velocity and temperature). Various configurations, including cases with two different systems for moving the air from the greenhouse attic to below the benches, with two and three rows of tubing with holes distributed along their lengths were tested. The cases with three air-distributing lines positioned under the drying benches improved the homogeneity of the air temperature both at the level of the drying benches and above it within the greenhouse.