Abstract

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 °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 _R2 and lower _RMS as compared to ANN. The highest level of GHG emission (NO_x, CO₂) was obtained at 50 °C temperature for samples without pretreatment.

摘要

本研究对带超声波预处理的有机黑莓热风-红外联合干燥机的能量和火用进行了预测和分析。评估了对产品颜色和温室气体 (GHG) 排放的影响。为了预测能量和火用参数，例如能量利用率、能量利用率、火用损失和火用效率，采用了人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 方法。与对照样品（未预处理）相比，干燥实验在 50、60 和 70 °C 的三个温度水平下进行，空气速度为 1 m/s，超声预处理时间为 15、30 和 45 分钟。结果表明，通过提高进气温度和超声预处理时间，颜色变化率降低，同时能源利用率和火用效率提高。ANN 和 ANFIS 方法的能量和火用预测结果表明，ANFIS 方法取得了更高的与 ANN 相比， _{R 2}和更低的_RMS。对于未经预处理的样品，在 50 °C 温度下获得了最高水平的 GHG 排放（NO _x、CO ₂）。