基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测

郭子瑞; 陈志强; 池日光; 沈爱华

doi:10.13205/j.hjgc.202204024

基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测

doi: 10.13205/j.hjgc.202204024

1. 哈尔滨商业大学能源与建筑工程学院, 哈尔滨 150023;
2. 哈尔滨工业大学城市水资源与水环境国家重点实验室, 哈尔滨 150090

基金项目:

哈尔滨商业大学博士科研启动金项目(2019DS071)

黑龙江省普通本科高等学校青年创新人才培养计划(UNPYSCT-2020214)

哈尔滨商业大学"青年创新人才"支持计划(2019CX24)

详细信息

通讯作者:
郭子瑞(1985-),女,硕士生导师,主要研究方向为固体废物资源化与数值仿真。ziruiguo@126.com

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- 文章访问数: 177
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-04
- 网络出版日期: 2022-07-06

PREDICTION OF POLYHYDROXYALKANOATE (PHA) PRODUCTION UTILIZING FOOD WASTE BASED ON GA-BP NEURAL NETWORK METHOD

1. School of Energy and Civil Engineering, Harbin University of Commerce, Harbin 150023, China;
2. State Key Laboratory of Urban Water Resources and Water Environment, Harbin Institute of Technology, Harbin 150090, China

摘要

摘要: 为了预估混合底物碳源条件下活性污泥PHA合成产量预测的准确度,通过引入遗传算法对BP人工神经网络的权值和阈值进行优选,建立基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测模型。以餐厨垃圾发酵液为底物碳源,利用活性污泥在ADD模式下进行PHA合成。以实验数据为基础训练神经网络模型,通过实测数据与模型预测数据之间的对比,验证了人工神经网络预测模型的精确度,并对长期PHA合成能力进行了预测。结论表明:基于遗传算法改进的GA-BP网络模型表现出比传统BP神经网络模型更佳的预测准确度,为评估混合菌群PHA最大合成产量的长期发展趋势,确定合理富集时长探索了可行方法。
- 餐厨垃圾发酵液 /
- ADD工艺 /
- PHA产量 /
- GA-BP网络
Abstract: To evaluate the activated sludge PHA synthesis yield prediction under the condition of mixed carbon sources, genetic algorithms were proposed to optimize the weights and thresholds of the BP artificial neural network, and the research established the prediction model based on the GA-BP network. Food waste fermentation liquid was applied as the substrate and activated sludge was used to synthesize PHA under the ADD mode. Based on experimental data, the comparison between the measured data and the model predictions was carried out, to verify the accuracy of the network prediction model and the prediction of the long-term PHA synthesis ability. Results showed that the GA-BP network model improved based on the genetic algorithm had high prediction accuracy than the traditional BP neural network model, and the model explored a feasible method to evaluate the long-term variation of the maximum PHA production yield in mixed microbial cultures and determined the reasonable enrichment time.
- food waste fermentation liquid /
- ADD process /
- PHA yield /
- GA-BP neural network

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参考文献(16)

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