Drying of organic blackberry in combined hot air-infrared dryer with ultrasound pretreatment

In this study, prediction and analysis of energy and exergy in a combined hot air-infrared dryer with ultrasound pretreatment for organic blackberry was carried out. The effect on product color and greenhouse gas (GHG) emission was assessed. To predict energy and exergy parameters such as energy utilization ratio, energy utilization, exergy loss, and exergy efficiency, both the artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods were employed. Drying experiments were undertaken at three temperature levels of 50, 60, and 70 degrees C in air speed of 1 m/s and ultrasound pretreatment time 15, 30, and 45 min, as compared to controlled samples (without pretreatment). Results demonstrated that by raising the inlet air temperature and ultrasound pretreatment time, color change rate decreased, while energy utilization and exergy efficiency increased. Energy and exergy prediction results by means of ANN and ANFIS methods showed that ANFIS method achieved a higher (R)(2) and lower (RMS) as compared to ANN. The highest level of GHG emission (NOx, CO2) was obtained at 50 degrees C temperature for samples without pretreatment.

Automated summary

The study predicted and analyzed energy and exergy of a combined hot air-infrared dryer with ultrasound pretreatment for organic blackberry. Results showed that increasing inlet air temperature and ultrasound pretreatment time can reduce color change rate and improve energy utilization and exergy efficiency. Prediction results by ANN and ANFIS methods indicated that ANFIS achieved higher (R)(2) and lower (RMS) than ANN, with the highest GHG emissions observed in samples without pretreatment at 50°C temperature.