| 基于CGA-BP神经网络的好氧堆肥曝气供氧量预测模型 |
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| 引用本文： | 丁国超,施雪玲,胡军.基于CGA-BP神经网络的好氧堆肥曝气供氧量预测模型[J].农业工程学报,2023,39(7):211-217. |
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| 作者姓名： | 丁国超 施雪玲 胡军 |
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| 作者单位： | 1. 黑龙江八一农垦大学信息与电气工程学院，大庆 163319;;2. 黑龙江八一农垦大学工程学院，大庆 163319; |
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| 基金项目： | 农业废弃物好氧发酵技术与智能控制设备研发（2016YFD0800600） |
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| 摘 要： | 为提高好氧堆肥曝气供氧量的曝气效率以及预测精度，该研究利用遗传算法（genetic algorithm, GA）对标准反向传播（back propagation, BP）神经网络的初始权值和阈值进行优化，再利用克隆选择算法（clonal genetic algorithm, CGA）优化遗传算法中的变异算子并复制算子，加快获取最优参数的速度，构建基于CGA-BP神经网络的曝气供氧量预测模型。为验证CGA-BP模型的有效性，与BP模型、GA-BP模型预测结果进行对比。试验结果表明：克隆遗传算法优化BP神经网络能加快获得最优解，效率相比BP模型和GA-BP模型分别提高了75.36%、51.30%；在曝气供氧量预测模型中，CGA-BP模型具有更准确的预测效果，预测精度为99.65%，而BP模型与GA-BP模型预测精度分别为96.99%、99.26%;CGA-BP模型评价指标的均方误差、平均绝对误差、平均绝对百分误差分别为0.003 4、0.038 9和0.350 6，均小于BP神经网络和GA-BP神经网络模型评价指标的误差；利用CGA-BP好氧堆肥曝气供氧量预测模型对好氧堆肥发酵过程进行精准...
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| 关 键 词： | 模型 试验 遗传算法 好氧堆肥 曝气供氧 BP神经网络 CGA-BP神经网络 |
| 收稿时间： | 2022/11/9 0:00:00 |
| 修稿时间： | 2023/2/6 0:00:00 |
Prediction model of the aeration oxygen supply for aerobic composting using CGA-BP neural network |
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| DING Guochao,SHI Xueling,HU Jun.Prediction model of the aeration oxygen supply for aerobic composting using CGA-BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(7):211-217. |
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| Authors: | DING Guochao SHI Xueling HU Jun |
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| Institution: | 1. College of Information and Electrical Engineering, Bayi Agricultural Reclamation University, Daqing 163319, China;; 2. College of Engineering, Bayi Agricultural Reclamation University, Daqing 163319, China; |
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| Abstract: | Aerobic compost has been commonly used to efficiently dispose of resources recycling and environmental protection in modern agriculture. Among them, aeration can be one of the most important environmental factors to affect composting fermentation. It is necessary for a feasible network model to accurately control the oxygen supply of aeration. This study aims to improve the aeration efficiency and prediction accuracy of aerobic composting aeration. In-depth learning was selected to train a network model, in order to predict the oxygen supply of aeration during aerobic composting fermentation in this experiment. Raw materials were taken as cow dung, cow dung biogas residue, chicken manure, and corn straw in the Haihua Biogas Plant in Miyun District, Beijing, China. The corn straw was crushed by 1-2 cm in grain size. The cow dung, cow dung biogas residue, and chicken manure were uniformly mixed with the crushed corn straw for composting and fermentation. The sensor was used in the composting fermentation tank to collect the parameter data during aerobic fermentation. 268 groups of data were selected as the sample data, 218 groups of data were randomly selected as the input data, and 50 groups of data were selected as the test data. Clonal genetic algorithm (CGA) was used to predict the standard back propagation (BP) neural network model for the aeration oxygen supply, whereas, the 6-14-1 three-layer network structure was used as the basic structure of the prediction model. The input parameters were the temperature, humidity, oxygen concentration, room temperature, pH value, and electrical conductivity (EC). The mean square error (MSE) of the number of hidden layer nodes was determined to be 14 after training and calculation. The output data was aeration. This article establishes BP neural network model for predicting aeration oxygen supply. Then the genetic algorithm (GA) and clonal selection algorithm were used to improve the prediction accuracy of the model. The experiment shows that the CGA-BP neural network model has the best prediction effect on aeration oxygen supply. 1) The CGA-BP neural network model accelerated the obtaining of the optimal solution, with an efficiency improvement of 75.36% and 51.30% compared to the BP model and GA-BP model, respectively. 2) In the prediction model of aeration oxygen supply, the CGA-BP model had a more accurate prediction effect, with a prediction accuracy of 99.65%. The prediction accuracy of aeration oxygen supply was 96.99% and the prediction accuracy of the GA-BP neural network model reached 99.26%. A comparison was made to evaluate the errors of BP, GA-BP and CGA-BP neural models. The model evaluation showed that the best performance was found in the CGA-BP neural network model with the smallest error, as shown by the mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE). 3) The improved CGA-BP neural network model can be predicted the aeration oxygen supply of aerobic composting, increasing the aeration control efficiency by 3.22%. The improved model can be expected to accurately predict the aeration oxygen supply of aerobic compost. The finding can provide a strong reference for accurate data for the next aeration. |
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| Keywords: | model trial genetic algorithms aerobic composting aerated oxygen supply BP neural networks CGA-BP neural networks |
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