摘要
本文通过原位合成的方式,开发了以Ca O2为活性物质,海藻酸为骨架材料的新型CaO_(2)-海藻酸缓释剂,并借助神经网络、遗传算法的机器学习技术定量研究了在模拟蓝藻水华暴发前夕的自然水体环境下该缓释剂不同投加策略对不同浓度水平铜绿微囊藻生长的抑制效果。实验表明,CaO_(2)-海藻酸缓释剂对各浓度水平铜绿微囊藻的生长抑制效果均表现良好,最大抑制率可达到74.41%。通过模型性能评价,确定GA-BP神经网络模型为该缓释剂抑藻效果的最优化模型。本文对CaO_(2)-海藻酸缓释剂的开发与基于GA-BP神经网络的抑藻模型的确定为该新型缓释抑藻剂的具体投加策略以及蓝藻水华的防治提供了可行思路与方法。
In this paper,a novel sustained-release algal suppressant of CaO_(2)-alginate was developed using CaO2 as the active substance and alginate as the skeleton material through in situ synthesis.In the simulated natural water environment before cyanobacteria bloom,the inhibitory effects of the sustained-release agent on the growth of microcystis aeruginosa at different concentration levels were quantitatively studied by using neural networks and genetic algorithms.The results showed that CaO_(2)-alginate sustained-release algal suppressant had good inhibitory effect on the growth of microcysts aeruginosa at all concentrations,and the maximum inhibitory rate was up to 74.41%.Through the evaluation of the model performance,it was determined that the GA-BP neural network model was the optimal model of the inhibitory effect of sustained-release algal suppressant.In this paper,the development of CaO_(2)-alginate sustained-release algal suppressant and the algal inhibition model based on GA-BP neural network provide a specific adding strategy of the algal suppressant and a method for the practical control of cyanobacterial blooms.
作者
王昊岩
李慧敏
李彭
何义亮
WANG Haoyan;LI Huimin;LI Peng;HE Yiliang(School of Environmental Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《水处理技术》
CAS
CSCD
北大核心
2023年第9期39-43,52,共6页
Technology of Water Treatment
基金
国家自然科学基金资助项目(41977387)。
关键词
CaO_(2)-海藻酸缓释抑藻剂
铜绿微囊藻
神经网络
遗传算法
CaO_(2)-alginate sustained-release algal suppressant
microcystis aeruginosa
neural network
genetic algorithm(GA)
