Two artificial neural network (ANN) models were developed to predict the main quality parameters of the corn used for ethanol production. Five Croatian corn hybrids were evaluated in this study (introducing the hybrid type as the first input categorical variable for ANN modelling), grown during three vegetation periods (the second categorical variable), under two levels of agrotechnology (the third categorical variable), dried at four temperatures (the fourth input variable), using two different heating and pressure pre-treatments of corn kernels (the fifth variable for ANN calculation) in order to improve the properties of corn for ethanol production. The first model (ANN1) was used to predict the hectolitre weight, 1000-kernels weight, the gelatinisation rate, and the contents of: glucose, reducing sugars and ethanol during the drying process, according to the type of the corn hybrid and the drying temperature. The ANN2 model was developed to predict the corn weight and moisture during the process, based on the input parameters. The artificial neural network models gave a good fit to experimental data and were able to predict the output variables successfully, showing a reasonably good predictive capability (overall r² for the corn kernel weight and moisture was 0.989, while r² for other outputs was 0.856). On the basis of a developed ANN models, multi-objective optimization was performed showing the possible practical use in the corn kernel drying process.

开发了两个人工神经网络（ANN）模型来预测用于乙醇生产的玉米的主要质量参数。在本研究中评估了五种克罗地亚玉米杂交种（将杂交种作为用于ANN建模的第一个输入分类变量），在两个植被时期（第二个分类变量）在两个农业技术水平（第三个分类变量）下生长，干燥在四个温度下（第四个输入变量），使用两种不同的玉米粒加热和压力预处理（用于ANN计算的第五个变量），以改善用于乙醇生产的玉米的特性。第一个模型（ANN1）用于预测百公克重量，1000内核重量，糊化率以及干燥过程中葡萄糖，还原糖和乙醇的含量，根据玉米杂种的类型和干燥温度而定。根据输入参数，开发了ANN2模型来预测加工过程中的玉米重量和水分。人工神经网络模型非常适合实验数据，并且能够成功预测输出变量，显示出相当好的预测能力（总体而言，²用于玉米仁的重量和水分为0.989，而[R ²为其它输出为0.856）。在已开发的ANN模型的基础上，进行了多目标优化，显示了在玉米籽粒干燥过程中可能的实际应用。