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| 1 | 一种基于种群多样性的粒子群优化算法设计及应用显示文摘针对粒子群优化算法早熟收敛及搜索精度较低的问题,提出一种基于种群多样性的改进型粒子群优化算法(PDPSO).首先,利用种群多样性描述粒子的分布状态,获得了进化过程中粒子飞行的非线性特征;其次,基于粒子的种群多样性设计了自适应惯性权重调整策略,实现了粒子全局探索能力及局部开发能力的平衡;最后,利用标准测试函数检验PDPSO算法性能,并将PDPSO算法应用于污水处理过程能耗模型优化,实验结果表明:与标准粒子群及其他改进粒子群算法相比,PDPSO算法具有较高的搜索精度,并有效地避免粒子陷入局部最优;同时,PDPSO算法能够实现污水处理过程的优化,保证污水出水水质的前提下,降低了污水处理过程的运行能耗. | 韩红桂 卢薇 乔俊飞 | 2017 | 信息与控制2017，46，6： | 19 |
| 2 | 基于多目标粒子群算法的污水处理智能优化控制显示文摘为了满足污水处理过程出水水质排放达标的同时降低运行能耗,提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先,通过分析污水处理运行数据,建立了基于自适应回归核函数的污水处理能耗和出水水质模型;其次,设计出一种污水处理多目标优化方法,利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化,获得溶解氧和硝态氮浓度的优化设定值;最后,利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制,实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示,该多目标优化控制方法不但能够保证出水水质达标,而且能有效降低污水处理过程的能耗。 | 韩红桂 张璐 乔俊飞 | 2017 | 化工学报2017，68，4： | 18 |
| 3 | 污水处理决策优化控制显示文摘以抑制出水氨氮浓度、总氮浓度峰值和降低能耗为目标,提出污水处理决策优化控制方法.首先利用神经网络建立出水氨氮和总氮预测模型;其次使用多目标进化算法得到溶解氧浓度和硝态氮浓度设定值;最后,根据出水氨氮和总氮浓度预测结果选择控制策略(优化控制和抑制控制).以仿真基准模型(BSM1)为平台,采用提出的决策优化控制方法进行控制,实验结果表明,该控制方法有效抑制了出水氨氮和总氮浓度峰值,出水超标时间和能耗明显少于所对比决策控制方法. | 栗三一 乔俊飞 李文静 顾锞 | 2018 | 自动化学报2018，44，12： | 12 |
| 4 | Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network显示文摘Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods. | Junfei Qiao Hongbiao Zhou | 2018 | IEEE/CAA Journal of Automatica Sinica2018，5，5： | 8 |
| 5 | 造纸废水处理中温室气体减排的溶解氧智能优化控制显示文摘造纸工业普遍采用活性污泥法处理废水,然而对该过程中温室气体排放却关注很少,缺少温室气体在线监测和减排措施。针对这一问题,提出了一种基于自适应回归神经网络PI控制(Adapted Kernel Regression Back Propagation Neural Network-Proportional Integral Control, AKRBP-PI)的溶解氧分层优化控制策略,旨在保证出水质量的同时,减少温室气体排放。该优化控制基于溶解氧对温室气体排放的作用机制,采用分层思想,根据出水污染物含量与溶解氧的函数约束关系,采用遗传算法求解溶解氧优化设定值,实现神经网络跟踪控制。仿真结果表明,对比开环控制,AKRBP-PI方案的出水污染物含量均未超标,温室气体总排放量减少了8.6%。其中,减少的温室气体主要来自曝气机耗电量的下降。 | 黄菲妮 沈文浩 | 2020 | 中国造纸2020，39，8： | 6 |
| 6 | Statistical regression modeling for energy consumption in wastewater treatment显示文摘Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand(COD) reduction was the response variable of interest. Via the proposed approach,the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen(TN)-rich wastewater,and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving. | Yang Yu Zhihong Zou Shanshan Wang | 2019 | Journal of Environmental Sciences2019，31，1： | 3 |
| 7 | 基于案例推理的曝气过程智能控制方法显示文摘为保证出水水质,降低运行成本,污水处理过程的优化需要动态更新污水处理过程操作变量的最优设定值。因此,提出使用进化算法对溶解氧的设定值进行优化,并结合案例推理(case-based reasoning,CBR),提出一种污水处理的曝气过程智能控制方法。首先,建立入水数据与出水指标的神经网络模型,针对不同工况,使用优化算法获取操作变量的优化设定值,建立动态案例库,使用最近相邻法于案例匹配过程中,将案例重用后取得操作变量的优化设定值应用于基准仿真模型1号(benchmark simulation model No.1,BSM1)中,并得到性能评价指标。根据性能评价指标,更新操作变量的优化设定值和神经网络模型。使用BSM1对优化系统进行仿真,优化系统较原系统曝气能耗减少了18.5%,同时出水水质(effluent quality,EQ)指标得到了改善。 | 吴桐 于广平 袁德成 刘坚 李健 孙宏存 | 2023 | 控制工程2023，30，11： | 0 |
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