Application of artificial neural networks in the prediction of sugarcane juice Pol

ABSTRACT Innovative techniques that seek to minimize the costs of production and the laboriousness of certain operations are one of the great challenges in the sugar-energy sector nowadays. Thus, the objective of the present study was to estimate the Pol values of sugarcane juice as a function of °Brix and wet cake weight (WCW) using artificial neural network (ANN) modeling. A database was organized consisting of 204 technological analyses from a field experiment with 15 treatments and 2 years of evaluation. 75% of the data were used for the calibration of the model and 25% for its validation. Multilayer Perceptron ANNs were used for calibration and validation of the data. Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. The ANNs were established with two hidden layers and the number of neurons ranging from 4 to 20 in each. The 15 ANNs with the lowest root mean square errors were randomly presented by the software, among which 6 were chosen to verify the accuracy. The ANNs had a high accuracy in the estimation of sugarcane juice Pol, both in the calibration phase (R2 = 0.948, RMSE = 0.36%) and in the validation (R2 = 0.878, RMSE = 0.41%), and can replace the standard method of analysis. Simpler networks can be trained to have the same accuracy as more complex networks.

摘要：当前，糖能产业面临的核心挑战之一，便是研发可降低生产成本、简化部分作业流程的创新技术。为此，本研究旨在借助人工神经网络（Artificial Neural Network, ANN）建模，基于糖度（°Brix）与湿滤饼重量（Wet Cake Weight, WCW）预测甘蔗汁的Pol值。本研究构建了涵盖15组处理、为期2年田间试验的数据集，包含204项工艺分析数据；其中75%的数据用于模型校准，剩余25%用于模型验证。本研究采用多层感知机人工神经网络（Multilayer Perceptron ANN）完成数据校准与验证，校准前先对变量进行归一化处理。训练算法选用反向传播法，激活函数采用Sigmoid函数；所构建的神经网络均包含两个隐藏层，每层神经元数量介于4至20之间。软件随机筛选出15个均方根误差最低的神经网络模型，从中选取6个进行精度验证。该神经网络模型在甘蔗汁Pol值预测任务中表现出较高精度，校准阶段决定系数R²为0.948、均方根误差RMSE为0.36%，验证阶段R²为0.878、RMSE为0.41%，可替代标准分析方法。相较复杂网络，结构更简单的神经网络经训练后亦可达到同等精度。