Implementation of a fuzzy forecasting model to estimate and optimize subsurface irrigation efficiency

Lack of drinking water and fresh water for agricultural purposes is one of the biggest problems in most arid and semi-arid countries. Therefore, desalination of saline water is necessary for the production of drinking water and irrigation. Due to the irradiative potential of south regions of Iran, the study of a solar subsurface irrigation system was performed experimentally and theoretically in Dehdasht. The system used a solar still and a subsurface reverse flow heat exchanger. The mixture of hot and humid air inside the solar still was directed to the underground pipe by a fan. Using the condensation process, the required water for subsurface irrigation was obtained. Using a suitable system for injecting compost tea into the pure water, a solution with a suitable composition was produced to irrigate and supply soil and plant nutrients. By measuring and monitoring environmental information and using MATLAB software, a fuzzy inference model was designed and Temperature changes, humidity ratio and mass of produced water were investigated. According to the simulation performed in MATLAB, the maximum temperature of the still reached 90°c in August and 60°c in April. Compared with its experimental values, the error values were reached 5.8% and 9%, respectively. The maximum and minimum amount of condensed water in the pipe was 6.13 liters and 3.09 liters per day for August and April, respectively. Compared to the experimental value obtained, the error values were 7% and 15%, respectively. According to the results, increasing the parameters such as solar radiation, ambient temperature, water temperature, diameter of condenser pipe and diameter of hot air pipe, as well as decreasing parameters such as condenser water temperature, relative humidity and pool water height, increased the amount of water produced. Therefore, this method is suitable for arid and semi-arid areas.