基于不确定性理论与方法的城市污水处理厂优化决策研究
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摘要
城市污水处理厂的规划、设计和运行是涉及到多变量的、受多种不确定性影响的一系列决策过程。传统的优化决策理论与方法不能有效地考虑系统固有不确定性的影响,存在一定的片面性和局限性,使模型的实际应用受到限制。本文应用不确定性理论与方法对城市污水处理厂优化决策进行研究,内容包括费用模型的建立、设计阶段的工艺方案优选、设计参数对优化设计的影响分析和不确定性优化设计模型的求解,以及影响运行效果的关键参数污水量的非线性动力学特性研究及其短时预测。这对优化污水处理厂的设计和运行,具有重要的指导意义。
污水处理费用模型的非线性、基础数据收集和统计中的不确定性以及费用影响因子内相关等特点,都限制了传统方法的应用。考虑到神经网络自适应、自学习能力以及理论上逼近任意非线性连续映射的优点,本文采用BP神经网络对污水处理厂的费用模型进行探索性的研究。研究以台湾地区26组污水处理厂数据为基础,选取设计水量、处理程度、入流BOD5浓度和集水区域面积作为网络输入,总造价和工厂建设费为输出。采用动量-自适应学习率调整算法、“留一法”和“提前停止”来克服传统BP神经网络收敛速度慢、易陷入局部极小值的缺点,以及样本容量有限、过拟合等问题。结果表明,建立的BP神经网络模型学习能力强,能够较好地预测台湾地区污水处理厂的经济费用,比多元线性回归模型有了很大的改进,预测值与样本之间的相对误差显著降低,预测精度提高。
城市污水处理厂的设计首先要涉及到污水处理工艺方案的选择。针对污水处理工艺方案优化决策多目标、多指标、确定性与多种不确定性共存的特点,本文建立了多层次模糊灰色耦合模型和多属性集对分析模型,以弥补现有优化方法单纯考虑费用因素,受人为主观影响较大的不足,为决策提供科学的依据。多层次模糊灰色耦合模型结合了灰色关联分析和层次分析法的优点,采用多层次多指标评判,较常用的单层次评判更能反映方案的实际特性;综合关联度作为优劣评判的不确定度量,具有全面性和提高方案可比性的优点。多属性集对分析模型考虑了确定性和不确定性共存的特点,拓展了结构函数——同异反联系度的概念;独特的稳定性分析通过基序分析其稳定区域,判定扩展序的存在,保证了决策的准确性和可靠性。实例研究验证了两个模型的有效性。某污水处理厂提供了四种候选方案,即A2/O法、三沟式氧化沟、厌氧-单沟式氧化沟和SBR法。考察的指标有项目投资、经营成本、占地面积、氮磷去除效果、污泥处理效果、运行稳定性、工艺成熟性和操作难易程度等8项。研究结果表明,对该污水处理厂而言,厌氧-单沟式氧化沟为最佳方案,其综合效益最高。
污水处理厂传统的优化设计很少考虑模型不确定性参数的影响,故一般情况下产生的设计方案往往不是最优的。本文采用典型的Lawrence-McCarty模式(即泥龄法)对活性污泥系统进行优化设计,在这个基础上对模型参数分别开展了不确定性分析和灵敏度分析,考察各参数对系统优化设计的影响。对优化设计影响较大的参数有:进水水量和水质、与氧转移效率相关的校正参数以及二沉池的浓缩常数。这对于建立低灵敏度系统,对提高设计方案理论最优解的工程实用性有重要的指导意义。
城市污水处理厂优化设计模型经过40多年的发展,在理论上渐趋完善,但由于模型本身的可解性差而使其实用性变差,尤其是不具有对设计方案进行动态优化调控的能力。区间数优化设计模型的表达和求解均以区间数的形式直接反映了系统的不确定性信息。决策者在决策时,可结合实际情况和各种新的信息,根据个人或集体经验、偏好在这一行为区间中确定具体行为方案,与传统优化模型相比更科学更实用,具有更大的灵活性和可操作性。但是由于模型的高维非线性,利用传统优化方法计算繁琐,且解的质量和计算效率对迭代初始点比较敏感。本文尝试将遗传算法与传统的步长搜索法相结合,对城市污水处理厂区间数优化设计模型的求解进行了探索。实例验证表明,这种新方法不涉及求导等复杂的数学运算,同时将大大提高区间解的搜索速度。与常规数值解法所得结果相比,目标函数总费用改善了20%左右,具有显著的经济效益。
城市污水处理厂的污水量对生产合理调配具有很大的参考价值。它是系统内在和外在随机因素共同作用的结果,其预测准确与否不仅仅受外在随机因素的影响,更重要的是由系统内在动力学特征所决定。本文运用相空间重构理论,通过计算最大Lyapunov指数,判断城市污水水量时间序列存在混沌特性,可以短时预测。对长沙市第二污水处理厂污水量时间序列的相空间重构,确定延迟时间为2天,嵌入维数为9,最大Lyapunov指数为0.0274。据此确定神经网络的结构,输入层、输出层和隐含层节点分别为9,1和16。该法避开了传统BP网络结构设计依赖经验,易出现偏差的缺点。将整个时间序列10%的数据(36个)作为验证数据,其余90%(329个)作为训练数据。结果表明,建立的污水量短时预测混沌神经网络模型效果良好,最大绝对预测误差为0.2449,预测的平均误差为0.077。
The planning, design and operation of municipal wastewater treatment plant is a series of decision-making processes with characteristics of multivariables and uncertain. Traditional optimization theories and methods failed to deal with intrinsic uncertainty effectively. Consequently optimization models were unilateral and limitative to a certain extent, so that it was difficult to popularize actual application. In the paper, uncartainty theories and methods were applied to optimization decision-making of municipal wastewater treatment plant, including establishment of cost function, optimal selection of wastewater treatment schemes, effect analysis of design parameter on optimal design, and solution of uncertain optimal design model, and nonlinear dynamic characteristic analysis and short-time forecast of influent flow, one of the most influencing parameter on operation of wastewater treatment plant. This research will of great significance for the design, operation and management for municipal wastrewater treatment plant.
Not only the assumption of linearity for cost function but also the uncertainty resulting from collection and statistical analysis process of basic data, together with the inter-relationships among cost related factors, limited the application of traditional approaches in literatures. Considering the advantages of self-organizing, self-learning, and approximating arbitrary nonlinear continuous maps in theory, BP neural network was introduced into cost function of municipal wastewater treatment plant based on 26 data sets of Taiwan region. Design flow rate, influent BOD5 concentration, treatment degree and collection area were chosen as input variables of network, and total construction cost and plant construction cost as the output. The momentum constant and adaptive learning rate training algotithm was adopted to train network to overcome the problems of conventional BP algorithm such as falling into the local minima easily and converging slowly. Due to finite samples, leave-one-out method was applied. Early-stopping strategy was implemented to avoid over-learning. The comparative research revealed that the built BP neural network with strong learning capability outperformed multiple linear regression and can estimate cost of wastewater treatment effectively by reducing relative prediction error and improving the accuracy greatly.
One important issue before design and construction of any municipal wastewater treatment plant is optimal selection of wastewater treatment schemes. Aimed at the multi-index, multi-objective and coexistence of certainty and various uncertainties involved in the optimization decision-making, this paper was intended to develop two novel approaches, that is hierarchy grey relational analysis and multi-attribute set pair analysis, to offset the shortages of conventional approaches such as considering the sole objective of minimizing system costs, and affected greatly by subjective factors, and to provide scientific basis for decision-making. The hierarchy grey relational analysis combined grey relational analysis with the idea of the hierarchy of analytic hierarchy process. It allowed for more effective reflection of actual characteristics of the problem as compared to mono level-based evaluation. In addition, the quantified evaluating scale, namely integrated grey relational grade, made wastewater treatment alternative selection more comparable and comprehensive. The multi-attribute set pair analysis developed the concept of identity - discrepancy - contrary connection degree, which should be regarded as structural function. Its unique stability analysis for basic ranking to determine other extra ranking could ensure the veracity and stability of decision results. The effectiveness of these two approaches was verified through case study. For some a municipal wastewater treatment plant, four alternatives (A2/O, triple oxidation ditch, anaerobic single oxidation ditch and SBR) were evaluated and compared against multiple economic, technical and administrative performance criteria, including capital cost, operation and maintenance cost, land area, removal of nitrogenous and phosphorous pollutants, sludge disposal effect, stability of plant operation, maturity of technology and professional skills required for operation and maintenance. The results showed that anaerobic single oxidation ditch was the optimal scheme and would obtain the maximum general benefits.
Traditional optimal design of activated sludge system rarely took the parameter uncertainty into account, so that the obtained optimal solution was by no means the best for actual situation. In the paper, the typical Lawrence-McCarty mode, that is, sludge age-based design mode, was selected for optimal design of activated sludge system. Based on this, sensitivity analysis and uncertainty analysis were carried through to examine the influence of uncertain parameters on optimal design. Results indicated that influent flow and strength, parameters related with oxygen transfer efficiency, and thickening parameters of final clarifier had the most significantly influence on activated sludge system optimal design. This research was of great significance for the establishment of low-sensitivity system and the improvement of engineering practicability of theoretic optimal design solution.
With the development of optimization theories during the past more than forty years, optimal design models of activated sludge system trended towards integrity. But it was difficult to obtain optimal solutions, as well as to provide dynamic modulation for design schemes. Both disadvantages weakened the practicability of optimal models. Interval optimization model can directly reflect the uncertainties that exist in actual systems, and a group of result intervals can be obtained from the solution of the model. According to personal or collective experience and prejudice, decision makers could determine detailed schemes in the result intervals combining with some other actual conditions. Obviously, interval optimization model was more scientific, applicable and operable than other optimization models. However, due to high-dimensional nonlinearity of interval optimization model, the solution process was laborious and time-consuming. In addition, the quality of ultimate solutions and calculation efficiency were sensitive to initial point for iteration. Genetic algorithm combined with classic step-search method was introduced to solve the interval optimization model for wastewater treatment plant. The verification by case study indicated that this synthetical optimization algorithm could improve searching efficiency distinctly without complicated mathematical operation such as derivative calculation. The objective, i.e. total cost, was reduced by 20 percent compared with that of classical numerical solution, which would bring great economic benefit.
Influent inflow is important for the operation and control of municipal wastewater treatment plant, which is codetermined by both intrinsic factors and extrinsic stochastic factors. Its prediction accuracy not only depends on extrinsic stochastic factors, but greatly on intrinsic dynamic character. Phase space reconstruction theory was applied to calculate the largest Lyapunov exponents, based on which to recognized the chaos in influent flow time series. If there is chaos in time series, then the influent flow can be short-time forecasted. For influent flow of the No. 2 Changsha wastewater treatment plant, delay times were 2 days, and embedding windows were 9, and largest Lyapunov exponent was 0.0274. According to above results, structure of neural network was determined. The nodes of input, output and hidden layer were 9, 1, 16, respectively. This approach to determine the structure of neural network could avoid the disadvantages of dependency on experience and easy deviation. The data were divided into 36 validation data and 329 training data. Result showed that the built short-term forecasting chaos neural network for influent inflow performed well, and the largest absolute error was 0.2449 and the average prediction error was 0.077.
污水处理费用模型的非线性、基础数据收集和统计中的不确定性以及费用影响因子内相关等特点,都限制了传统方法的应用。考虑到神经网络自适应、自学习能力以及理论上逼近任意非线性连续映射的优点,本文采用BP神经网络对污水处理厂的费用模型进行探索性的研究。研究以台湾地区26组污水处理厂数据为基础,选取设计水量、处理程度、入流BOD5浓度和集水区域面积作为网络输入,总造价和工厂建设费为输出。采用动量-自适应学习率调整算法、“留一法”和“提前停止”来克服传统BP神经网络收敛速度慢、易陷入局部极小值的缺点,以及样本容量有限、过拟合等问题。结果表明,建立的BP神经网络模型学习能力强,能够较好地预测台湾地区污水处理厂的经济费用,比多元线性回归模型有了很大的改进,预测值与样本之间的相对误差显著降低,预测精度提高。
城市污水处理厂的设计首先要涉及到污水处理工艺方案的选择。针对污水处理工艺方案优化决策多目标、多指标、确定性与多种不确定性共存的特点,本文建立了多层次模糊灰色耦合模型和多属性集对分析模型,以弥补现有优化方法单纯考虑费用因素,受人为主观影响较大的不足,为决策提供科学的依据。多层次模糊灰色耦合模型结合了灰色关联分析和层次分析法的优点,采用多层次多指标评判,较常用的单层次评判更能反映方案的实际特性;综合关联度作为优劣评判的不确定度量,具有全面性和提高方案可比性的优点。多属性集对分析模型考虑了确定性和不确定性共存的特点,拓展了结构函数——同异反联系度的概念;独特的稳定性分析通过基序分析其稳定区域,判定扩展序的存在,保证了决策的准确性和可靠性。实例研究验证了两个模型的有效性。某污水处理厂提供了四种候选方案,即A2/O法、三沟式氧化沟、厌氧-单沟式氧化沟和SBR法。考察的指标有项目投资、经营成本、占地面积、氮磷去除效果、污泥处理效果、运行稳定性、工艺成熟性和操作难易程度等8项。研究结果表明,对该污水处理厂而言,厌氧-单沟式氧化沟为最佳方案,其综合效益最高。
污水处理厂传统的优化设计很少考虑模型不确定性参数的影响,故一般情况下产生的设计方案往往不是最优的。本文采用典型的Lawrence-McCarty模式(即泥龄法)对活性污泥系统进行优化设计,在这个基础上对模型参数分别开展了不确定性分析和灵敏度分析,考察各参数对系统优化设计的影响。对优化设计影响较大的参数有:进水水量和水质、与氧转移效率相关的校正参数以及二沉池的浓缩常数。这对于建立低灵敏度系统,对提高设计方案理论最优解的工程实用性有重要的指导意义。
城市污水处理厂优化设计模型经过40多年的发展,在理论上渐趋完善,但由于模型本身的可解性差而使其实用性变差,尤其是不具有对设计方案进行动态优化调控的能力。区间数优化设计模型的表达和求解均以区间数的形式直接反映了系统的不确定性信息。决策者在决策时,可结合实际情况和各种新的信息,根据个人或集体经验、偏好在这一行为区间中确定具体行为方案,与传统优化模型相比更科学更实用,具有更大的灵活性和可操作性。但是由于模型的高维非线性,利用传统优化方法计算繁琐,且解的质量和计算效率对迭代初始点比较敏感。本文尝试将遗传算法与传统的步长搜索法相结合,对城市污水处理厂区间数优化设计模型的求解进行了探索。实例验证表明,这种新方法不涉及求导等复杂的数学运算,同时将大大提高区间解的搜索速度。与常规数值解法所得结果相比,目标函数总费用改善了20%左右,具有显著的经济效益。
城市污水处理厂的污水量对生产合理调配具有很大的参考价值。它是系统内在和外在随机因素共同作用的结果,其预测准确与否不仅仅受外在随机因素的影响,更重要的是由系统内在动力学特征所决定。本文运用相空间重构理论,通过计算最大Lyapunov指数,判断城市污水水量时间序列存在混沌特性,可以短时预测。对长沙市第二污水处理厂污水量时间序列的相空间重构,确定延迟时间为2天,嵌入维数为9,最大Lyapunov指数为0.0274。据此确定神经网络的结构,输入层、输出层和隐含层节点分别为9,1和16。该法避开了传统BP网络结构设计依赖经验,易出现偏差的缺点。将整个时间序列10%的数据(36个)作为验证数据,其余90%(329个)作为训练数据。结果表明,建立的污水量短时预测混沌神经网络模型效果良好,最大绝对预测误差为0.2449,预测的平均误差为0.077。
The planning, design and operation of municipal wastewater treatment plant is a series of decision-making processes with characteristics of multivariables and uncertain. Traditional optimization theories and methods failed to deal with intrinsic uncertainty effectively. Consequently optimization models were unilateral and limitative to a certain extent, so that it was difficult to popularize actual application. In the paper, uncartainty theories and methods were applied to optimization decision-making of municipal wastewater treatment plant, including establishment of cost function, optimal selection of wastewater treatment schemes, effect analysis of design parameter on optimal design, and solution of uncertain optimal design model, and nonlinear dynamic characteristic analysis and short-time forecast of influent flow, one of the most influencing parameter on operation of wastewater treatment plant. This research will of great significance for the design, operation and management for municipal wastrewater treatment plant.
Not only the assumption of linearity for cost function but also the uncertainty resulting from collection and statistical analysis process of basic data, together with the inter-relationships among cost related factors, limited the application of traditional approaches in literatures. Considering the advantages of self-organizing, self-learning, and approximating arbitrary nonlinear continuous maps in theory, BP neural network was introduced into cost function of municipal wastewater treatment plant based on 26 data sets of Taiwan region. Design flow rate, influent BOD5 concentration, treatment degree and collection area were chosen as input variables of network, and total construction cost and plant construction cost as the output. The momentum constant and adaptive learning rate training algotithm was adopted to train network to overcome the problems of conventional BP algorithm such as falling into the local minima easily and converging slowly. Due to finite samples, leave-one-out method was applied. Early-stopping strategy was implemented to avoid over-learning. The comparative research revealed that the built BP neural network with strong learning capability outperformed multiple linear regression and can estimate cost of wastewater treatment effectively by reducing relative prediction error and improving the accuracy greatly.
One important issue before design and construction of any municipal wastewater treatment plant is optimal selection of wastewater treatment schemes. Aimed at the multi-index, multi-objective and coexistence of certainty and various uncertainties involved in the optimization decision-making, this paper was intended to develop two novel approaches, that is hierarchy grey relational analysis and multi-attribute set pair analysis, to offset the shortages of conventional approaches such as considering the sole objective of minimizing system costs, and affected greatly by subjective factors, and to provide scientific basis for decision-making. The hierarchy grey relational analysis combined grey relational analysis with the idea of the hierarchy of analytic hierarchy process. It allowed for more effective reflection of actual characteristics of the problem as compared to mono level-based evaluation. In addition, the quantified evaluating scale, namely integrated grey relational grade, made wastewater treatment alternative selection more comparable and comprehensive. The multi-attribute set pair analysis developed the concept of identity - discrepancy - contrary connection degree, which should be regarded as structural function. Its unique stability analysis for basic ranking to determine other extra ranking could ensure the veracity and stability of decision results. The effectiveness of these two approaches was verified through case study. For some a municipal wastewater treatment plant, four alternatives (A2/O, triple oxidation ditch, anaerobic single oxidation ditch and SBR) were evaluated and compared against multiple economic, technical and administrative performance criteria, including capital cost, operation and maintenance cost, land area, removal of nitrogenous and phosphorous pollutants, sludge disposal effect, stability of plant operation, maturity of technology and professional skills required for operation and maintenance. The results showed that anaerobic single oxidation ditch was the optimal scheme and would obtain the maximum general benefits.
Traditional optimal design of activated sludge system rarely took the parameter uncertainty into account, so that the obtained optimal solution was by no means the best for actual situation. In the paper, the typical Lawrence-McCarty mode, that is, sludge age-based design mode, was selected for optimal design of activated sludge system. Based on this, sensitivity analysis and uncertainty analysis were carried through to examine the influence of uncertain parameters on optimal design. Results indicated that influent flow and strength, parameters related with oxygen transfer efficiency, and thickening parameters of final clarifier had the most significantly influence on activated sludge system optimal design. This research was of great significance for the establishment of low-sensitivity system and the improvement of engineering practicability of theoretic optimal design solution.
With the development of optimization theories during the past more than forty years, optimal design models of activated sludge system trended towards integrity. But it was difficult to obtain optimal solutions, as well as to provide dynamic modulation for design schemes. Both disadvantages weakened the practicability of optimal models. Interval optimization model can directly reflect the uncertainties that exist in actual systems, and a group of result intervals can be obtained from the solution of the model. According to personal or collective experience and prejudice, decision makers could determine detailed schemes in the result intervals combining with some other actual conditions. Obviously, interval optimization model was more scientific, applicable and operable than other optimization models. However, due to high-dimensional nonlinearity of interval optimization model, the solution process was laborious and time-consuming. In addition, the quality of ultimate solutions and calculation efficiency were sensitive to initial point for iteration. Genetic algorithm combined with classic step-search method was introduced to solve the interval optimization model for wastewater treatment plant. The verification by case study indicated that this synthetical optimization algorithm could improve searching efficiency distinctly without complicated mathematical operation such as derivative calculation. The objective, i.e. total cost, was reduced by 20 percent compared with that of classical numerical solution, which would bring great economic benefit.
Influent inflow is important for the operation and control of municipal wastewater treatment plant, which is codetermined by both intrinsic factors and extrinsic stochastic factors. Its prediction accuracy not only depends on extrinsic stochastic factors, but greatly on intrinsic dynamic character. Phase space reconstruction theory was applied to calculate the largest Lyapunov exponents, based on which to recognized the chaos in influent flow time series. If there is chaos in time series, then the influent flow can be short-time forecasted. For influent flow of the No. 2 Changsha wastewater treatment plant, delay times were 2 days, and embedding windows were 9, and largest Lyapunov exponent was 0.0274. According to above results, structure of neural network was determined. The nodes of input, output and hidden layer were 9, 1, 16, respectively. This approach to determine the structure of neural network could avoid the disadvantages of dependency on experience and easy deviation. The data were divided into 36 validation data and 329 training data. Result showed that the built short-term forecasting chaos neural network for influent inflow performed well, and the largest absolute error was 0.2449 and the average prediction error was 0.077.
引文
[1] 刘善建. 水的开发与利用. 北京:中国水利水电出版社,2000,1-4
[2] 国家环境保护总局.中国城市环境保护. http://www.zhb.gov.cn/cont/mhcity/ mfcsxx/gldt/200506/t20050602_67180_7.htm,2005-06-02
[3] 建设部. 建设部通报全国城市污水处理情况. 中国环保产业,2005,11(10):27
[4] 国家环境保护总局. 2005 年中国环境统计公报. 中国环保产业,2006,12(6):4-5
[5] 羊寿生,张辰. 城市污水处理厂设计中热点问题剖析. 给水排水,1999,25(9):1-3
[6] 柯崇宜,石淑倩,潘宁等. 现有污水处理厂存在的若干问题探讨. 环境保护,2000,27(2):21-22
[7] 周雹,谭振江. 中、小型城市污水处理厂的优选工艺. 中国给水排水,2000,16(10):21-24
[8] 邵林广. 南方城市污水处理工艺的选择. 给水排水,2000,26(6):32-34
[9] 沈光范. 关于城市污水处理厂设计的若干问题. 中国给水排水,2000,16(3):20-23
[10] 许劲. 关于城市污水处理厂设计的若干问题讨论. 给水排水,2001,27(7):15-18
[11] 尹 文 选 , 尹 卫 红 . 中 小 城 市 污 水 处 理 厂 设 计 的 几 个 问 题 . http://www.gpszx.com/disquisition/detail.php?id=1640,2003-06-07
[12] 林晓明. 论城市污水处理厂建设规模与处理标准的确定. 给水排水,1997,23(9):20-23
[13] 邵林广. 南方城市污水处理厂实际运行水质远小于设计值的原因及其对策. 给水排水,1999,25(2):11-13
[14] 庞子山. 活性污泥法工艺系统优化设计模型及应用研究:[重庆大学博士学位论文]. 重庆:重庆大学,2004,6-7
[15] 孙振世,陆芳. 我国城市污水处理厂运行状况及加强监管对策. 中国环境管理,2003,22(5):1-2
[16] Lynn W R, Logan J A, Charnes A. System analysis for planning wastewater treatment plants. Journal of Water Pollution Control Federation, 1962, 34: 565-581
[17] Chia S S, Krishman P. Dynamic optimization for industrial wastewater treatmentdesign. Journal of Water Pollution Control Federation, 1969, 41(10): 1787-1802
[18] Paul M Berthouex, Lawrence B Polkowski. Optimum waste treatment plant design under uncertainty. Journal of Water Pollution Control Federation, 1970, 42(9): 1589-1613
[19] Rossman L A. Synthesis of waste treatment systems by implicit enumeration. Journal of Water Pollution Control Federation, 1980, 52(1): 148-160
[20] Tyteca D. Nonlinear programming model of wastewater treatment plant. Journal of the Environmental Engineering Division, ASCE, 1981, 107(4): 747-766
[21] Tyteca D, Smeer Y. Nonlinear programming design of wastewater treatment plant. Journal of the environmental Engineering Division, ASCE, 1981, 107(4): 767-779
[22] Tang C C, Brill E D jr, Pfetter J T. Optimization techniques for secondary wastewater treatment system. Journal of the Environmental Engineering Division, ASCE, 1987, 113(5):935-951
[23] Tang C C, Brill E D jr, Pfetter J T. Comprehensive model of an activated sludge wastewater treatment system. Journal of the Environmental Engineering Division, ASCE, 1987, 113(5):952-969
[24] Uber J G, Pfetter J T, Brill E D jr. Robust optimal design of wastewater treatment I: General approach. Journal of the Environmental Engineering Division, ASCE, 1991, 117(4):425-437
[25] Uber J G, Pfetter J T, Brill E D jr. Robust optimal design of wastewater treatment II: Application. Journal of the Environmental Engineering Division, ASCE, 1991, 117(4):438-456
[26] 林玉鹏. 考虑不确定性因素影响的城市污水处理厂优化设计的理论和方法研究:[湖南大学硕士学位论文]. 长沙:湖南大学,2000,42-49
[27] 邢可霞,郭怀诚. 环境模型不确定性分析方法综述. 环境科学与技术,2006,29(5):112-114
[28] 刘毅,陈吉宁,杜鹏飞. 环境模型参数识别与不确定性分析. 环境科学,2002,23(6):6-10
[29] 张贤根. 论认识系统中的不确定性. 湖北民族学院学报(社会科学版), 1994,4(12):16-18
[30] 万咸涛. 江河水质监测中不确定性的应用研究. 江苏环境科技,1999(2):1-4
[31] 曾光明,黄国和. 环境科学与工程中的不确定性理论与方法. 长沙:湖南大学出版社,2002
[32] 梅亚东,吴贻名. 城市污水处理厂群动态投入问题研究. 武汉水利电力学院学报,1990,23(2-3):66-73
[33] 梅亚东. 城市污水处理厂系统最优规划研究. 见:中国青年科学技术论文精选. 北京:中国科学技术出版社,1994,786-791
[34] 徐祖信. 河流污染治理规划理论与实践. 北京:中国环境科学出版社,2003
[35] L Akca, C Kinaci, M Karpuzcu. A model for optimum design of activated sludge plants. Water Research, 1993, 27(9):1461-1468
[36] Beck M B. Systems analysis in management. The Netherlands: Pergamon Press, 1987
[37] 刘新春,任光耀,潘晓玲等. 资源利用中的不确定性、不可逆性分析. 新疆环境保护,2003,25(2):40-42
[38] 刘国东,丁晶. 水环境中不确定性方法的研究现状与展望. 环境科学进展,1996,4(4):46-53
[39] 徐宗学. 洪水风险率模型:[武汉水利电力大学博士论文]. 武汉: 武汉水利电力大学,1988,10-12
[40] Yacov Y Haimes. Integrated risk and uncertainty assessment in water resources within a multi-objective. Journal of Hydroology, 1984, 68(2): 222-234
[41] Benjamin J R, Cornell C A. Probability: statistics and decision for civil engineers. New York:Mograw-Hill, 1970, 70-92
[42] 王清印,倪天智,刘开第等. 不确定性数学及其研究方向. 见:灰色系统学术论文集. 开封:河南大学出版社,1993,151-154
[43] 王清印. 不确定性系统科学概要. 河北经贸大学学报,1997,18(1):48-50
[44] 龙腾锐,冯裕钊,郭劲松. 考虑不确定因素的污水厂日进水量预测法. 中国给水排水,2001,17(5):1-5
[45] 陈吉宁.环境系统不确定性分析的理论与发展. http://www.h2o-china.com/ paper/viewpaper.asp?id=1503,2001-11-08
[46] Beck M B. Principles of modeling. Water Science and Technology, 1991, 24(1):1-8
[47] Charles S Melching, Ben Chie Yen, Harry G Wenzel jr. A reliability estimation in modeling watershed runoff with uncertainties. Water Resources Research, 1990, 26: 2275-2286
[48] J Chen, H S Wheater. Identification and uncertainty analysis of soil water retention models using lysimeter data. Water Resources Research, 1999, 35(9): 2401-2414
[49] NMCI. Uncertainty analysis in water quality modeling, a review of methods of model calibration and uncertainty analysis, and their application to the TOPLEM water quality models. USA:TOPLEM, 1999
[50] Beck M B. Water quality modeling: a review of the analysis of uncertainty. Water Resources Research, 1987, 23(5): 1393-1442
[51] Dong-il Seo. 水质模型不确定性的一种新定量方法. 见:给水与废水处理国际会议论文集. 北京:中国建筑工业出版社,1994,833-838
[52] 梁晋文,陈林才,何贡. 误差理论与数据处理. 北京:中国计量出版社,1989,78-81
[53] 熊大国. 随机过程理论与应用. 北京:国防工业出版社,1991,103-105
[54] 汪培庄. 模糊集合论及其应用. 上海:上海科学技术出版社,1983,1-8
[55] 曾光明,杨春平,卓利. 环境系统灰色理论与方法. 北京:中国科学技术出版社,1994
[56] 赵克勤,宣爱理. 集对论——一种新的不确定性理论、方法与应用. 系统工程,1996,14(1):18-23
[57] 王光远. 未确知信息及其数学处理. 哈尔滨建筑工程学院学报,1990,23(4):1-9
[58] 刘开第,吴和琴. 未确知数学. 武汉:华中理工大学出版社,1997,27-35
[59] Pawlak Z. Rough sets. International Journal of Computer and Information Sciences, 1982, 11(2): 341 -356
[60] Pawlak Z. Rough set approach to Knowledge-based decision support. European Journal of Operational Research, 1997, 99(1):48-57
[61] 曾黄麟. 粗集理论及其应用. 重庆:重庆大学出版社,1998,56-59
[62] 卿晓霞,龙腾锐. 污水处理系统中的人工智能技术应用现状与展望. 给水排水,2006,32(8):99-102
[63] Wade M, Katebi R. Data mining and knowledge extraction in wastewater treatment plants. IEE Seminar Developments in Control Systems in the Water Industry, 2002, (120): 1-25
[64] 吕金虎,陆君安,陈士华. 混沌时间序列及其应用. 武汉:武汉大学出版社,2002
[65] Ching-Gung Wen, Chih-Sheng Lee. Development of a cost functions for wastewater treatment systems with fuzzy regression. Fuzzy Sets and Systems, 1999, 106(11): 143-153.
[66] Ho-Wen Chen, Ni-Bin Chang. A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants. Journal of Environmental Management, 2002, 65(4): 383-409.
[67] Bernard C M, Rolf Eliassen. Sensitivity analysis of activated sludge economics. Journal of the Environment Engineering Division, ASCE, 1966, 92 (SA2):147-167
[68] Abasaeed E A. Sensitivity analysis on a sequencing batch reactor model I: effect of kinetic parameters. Journal of Chemical Technology and Biotechnology, 1997, 72(3): 379-383
[69] Abasaeed E A. Sensitivity analysis on a sequencing batch reactor model II: effect of stoichiometric and operating parameters. Journal of Chemical Technology and Biotechnology, 1999, 74(4): 451-455
[70] A Abusam, K J Keesman, G van Straten, et al. Sensitivity analysis in oxidation ditch modeling: the effect of variations in stoichiometric, kinetic and operating parameters on the performance indices. Journal of Chemical Technology and Biotechnology, 2001, 76(4): 430-438
[71] A Abusam, K J Keesman, G van Straten, et al. Estimation of uncertainties in the performance induces of an oxidation ditch benchmark. http://www.aenf.wau.nl/ mrs/staff/abusam/research/thesis/chapter6a.pdf, 2002
[72] Marcos von Sperling. Design of Facultative ponds based on uncertainty analysis. Water Science and Technology, 1996, 33(7): 41-47
[73] Michael S K, Chen Larry E Erickson, Liang-tsing Fan. Consideration of sensitivity and parameter uncertainty in optimal process design. Industrial & Engineering Chemistry Process Design and Development, 1970, 9(4): 514-519
[74] 上海市政工程设计研究院. 给水排水设计手册(第 10 册 技术经济). 北京:中国建筑工业出版社,2000,475-485.
[75] Anita Lee, Chun Hung Cheng, Jaydeep Balakrishnan. Software development cost estimation: Integration neural with cluster analysis. Information & Management, 1998, 34(1): 1-9
[76] Al-Tabtabai H, Alex PA, Tantash M. Preliminary cost estimation of highway construction using neural networks. Cost Engineering, 1999, 41(3): 19-24
[77] H Murat Günaydin, S Zeynep Do?an. A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management, 2004, 22(7): 595-602
[78] Sergio Cavalieri, Paolo Maccarrone, Roberto Pinto. Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics, 2004, 91(2): 165-177
[79] 骆敏. 污水处理成本的费用函数分析. 江苏环境科技,1992,5(3):23-25
[80] 尹军,朱佳. 污水处理厂单元处理构筑物费用函数的研究与探讨.中国给水排水,1990,6(6):16-19
[81] 尹军,李晓君,宫正. 城市污水二级处理系统费用函数研究. 水处理技术, 1988,14(4):226-229.
[82] 袁国文,Johanes Pinnekamp. 降低污水处理厂成本的可能性与措施. 见:21世纪国际城市污水处理及资源化发展战略研讨会与展览会会议论文集. 北京:21 世纪国际城市污水处理及资源化发展战略研讨会与展览会组委会,2001,45-47
[83] 傅国伟,程声通. 水污染控制系统规划. 北京:清华大学出版社,1985,46-47
[84] K P Tsagarakis, D D Mara, A Angelakis. Application of cost criteria for selection of municipal wastewater treatment systems. Water, Air and Soil Pollution, 2003, 142(2): 187-210
[85] David Steer, Todd Aseltyne, Lauchlan Fraser. Life-cycle economic model of small treatment wetlands for domestic wastewater disposal. Ecological Economics, 2003, 44 (2): 359-369
[86] 左玉辉. 环境系统工程导论. 南京:南京大学出版社,1985,209-211
[87] S Gillot, B De Clercq. Optimization of wastewater treatment plant design and operation using simulation and cost analysis. http://citeseer.ist.psu.edu/232972. html, 1999
[88] S Gillot, P Vermeire, P Jacquet, et al. Integration of wastewater treatment plant investment and operation costs for scenario analysis using simulation. http:// biomath.ugent.be/~Peter/ftp/pvr226.pdf, 1999
[89] Li Xianwen.Technical economics analysis of stabilization ponds. Water Science and Technology, 1995, 31(12): 103-110
[90] 蒋惠忠,卢旭阳. 污水处理单元构筑物费用函数研究. 环境经济,2000,(8):41-43
[91] 于九如,杨泽华. 模糊线性回归及其应用实例. 系统工程理论与实践,1995,(4):32-37
[92] 苑希民,李鸿雁,刘树坤等. 神经网络和遗传算法在水科学领域中的应用. 北京:中国水利水电出版社,2002,1-73
[93] Kung S Y. An algebraic projection analysis for optimal hidden units size and learning rates in back propagation learning. International Conference on Neural network, USA,1988, 363-370
[94] 金龙,况雪源,黄海洪等. 人工神经网络预报模型的过拟合研究. 气象学报,2004,62(1):62-69
[95] 袁曾任. 人工神经网络及其应用. 北京:清华大学出版社,1999,118-131
[96] Mirchandani Gagan, Gao Wei. On hidden nodes for neral sets. IEEE Transactionson circuits and systems, 1989, 36(5): 661-664
[97] H White, et al. Artificial Neural Networks: Approximation and Learning Theory. Cambridge, Mass: Blackwell, 1992, 16-18
[98] Martin T Hagan, Mohammad B Menhaj. Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks, 1994,5(6): 989-993
[99] Ahmed Gsmal El-Din, Daniel W. Smith. A neural network model to predict the wastewater inflow incorporating rainfall events. Water Research, 2002, 36(5): 1115-1126
[100] Michael Fernández, Julio Caballero. Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. Bioorganic & Medicinal Chemistry, 2006, 14(1): 280-294
[101] M Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, 1974, 36 (1): 111-147
[102] Francesco Bini Verona, Massimo Ceraolo. Use of neural networks for customer tario exploitation by means of short-term load forecasting. Neurocomputing, 1998, 23: 135-149
[103] Raymond La?, Sovan Lek, Jacques Moreau. Predicting fish yield of African lakes using neural networks. Ecological Modelling, 1999, 120(2-3): 325-335
[104] 阎平凡,张长水. 人工神经网络与模拟进化计算. 北京:清华大学出版社,2003
[105] John Neter, illiam Wasserman, Michael H. Kutner 著,张勇,王国明,赵秀珍译. 应用线性回归模型. 北京:中国统计出版社,1990
[106] Hegazy T Fazio P, Moselhi O. Developing practical neural network applications using back-propagation. Microcompu.Civil Eng., 1994, 9: 145-159.
[107]闻新,周露,王丹力等. MatLab 神经网络应用设计. 北京:科学出版社,2000,207-243
[108] 辛大欣,王长元,肖峰. BP 神经网络在回归分析中的应用研究. 西安工业学院学报,2002,22(2):129-135
[109] 建设部,国家环境保护总局,科学技术部. 城市污水处理及污染防治技术政策. http://www.jxepb.gov.cn/cyjs/hbcy/content/02.htm, 2000-07-13
[110] 温汝俊. 大城市污水处理工艺的经济选择因素. 重庆环境科学,2000,22(4):22-24
[111] Ellis KV, Tang S L. Wastewater treatment optimization model for developing world. I: Model development. Journal of Environmental Engineering Division, ASCE, 1991,117: 501-581
[112] 刘永淞. 污水处理设计的综合评价与优化分析. 化工给水排水,1996,(1):1-3
[113] Annelies J B, Heinz A P, Ralf O, et al. Indicators for the sustainability assessment of wastewater treatment systems. Urban Water, 2002, 4(2): 153-161
[114] 中国系统工程学会决策科学专业委员会. 决策科学理论与实践. 北京:海洋出版社,2003
[115] 王浙明. 钱塘江流域城市污水处理厂工程费用分析和方案优化:[浙江大学硕士学位论文]. 杭州:浙江大学,2000,13-14
[116] 刘占伟,邓四二,腾弘飞. 复杂工程系统设计方案评价方法综述. 系统工程与电子技术,2003,25(12):1488-1491
[117] 王媛,郭薇,贺业讯等. 价值工程在设计方案选择中的应用. 中国给水排水,2000,16(9):56-57.
[118] 赵峪,刘萍. AHP(层次分析法)对城镇污水处理厂的选型进行决策. 污染防治技术,1995,8(2):98-100
[119] 慕金波,翟素军. 层次分析法在废水治理工程评价中的应用. 苏州城建环保学院学报,1996,9(4):49-58
[120] 汤民淮,蔡俊雄. 利用层次分析计算污染治理设施评价指标权重. 环境科学与技术,1997,(1):27-31
[121] 张建锋,黄廷林. 关中地区污水处理工艺选择的系统分析. 环境工程,1999,17(6):61-64
[122] 吴育华,郑道英,吴庆雄. 海口长流污水厂工艺方案多目标评价与选择. 中国给水排水,1999,15(4):41-43
[123] 高湘,徐良. 层次分析法在西安咸阳国际机场污水处理投标方案选择中的应用. 见:第四届全国给水排水青年学术年会论文集. 2000
[124] 胡天觉,陈维平,曾光明等. 运用层次分析法对株洲霞湾污水处理厂污水处理工艺方案择优. 环境工程,2000,18(1):61-63
[125] 邵海员,刘绮,黎锡流. 污水处理厂规划中 AHP 方法的应用. 环境科学与技术,2006,29(2):97-100
[126] 刘永淞. 用模糊数学方法分析污水处理厂设计. 化工给排水设计,1995,(3):21-22
[127] 向跃霖. 改进的模糊相似选择法在污染源治理方案优选中的应用. 铁道劳动安全卫生与环保,1996,23(3):204-206
[128] 慕金波. 环境工程评标的模糊优选模型及应用. 江苏环境科技,1997,10(1):9-11
[129] 慕金波,酒济明. 废水治理工程的多层次模糊综合评判方法及应用. 山东环境,1997,81(6):9-11
[130] 凌猛,杭世君. 城市污水处理厂工艺方案模糊决策方法的应用. 给水排水,1998,24(3):6-9
[131] 张松滨,李万海,王红. 污水处理设施性能评价中的赋权相关分析. 环境工程,1999,17(5):55-58
[132] 李如忠. 多层次模糊综合评判模型在城市污水处理中的应用. 淮南工业学院学报,2000,20(4):1-4
[133] 黄绍娃,胡志光. 模糊综合评判法确定城市污水处理工艺. 工业用水与废水. 2004,35(4):12-14
[134] 狄军贞,刘书贤. 污水处理厂治理设施的模糊综合评价及优选. 工程设计与建设,2004,36(1):40-42
[135] 王玲玲,曾光明,黄国和等. 小区域污水处理的多目标规划设计探讨. 环境工程,2004,22(6):88-89
[136] 李小荣,张邵怡. 晋城市城区污水厂处理工艺的选择. 水处理技术,2002,28(3):175-176
[137] 王浙明,史惠祥,苏雨生等. 灰色关联模型用于工程方案优化. 中国给水排水,2002,18(1):81-84
[138] 慕金波,杨红红,姜涛. 灰色综合评判用于工厂废水处理方案的优选. 环境工程,1992,10(5):37-41
[139] 慕金波,姜涛. 灰色多层次综合评判模型在废水治理工程综合评价中的应用. 污染防治技术,1995,8(4):260-263
[140] 向跃霖. 治污投资方向选择的灰靶决策法. 化工环保,1996,16(2):102-106
[141] 向跃霖. 环境治理工程方案的灰色优选新模型及其应用. 环境工程,1998,16(3):52-55
[142] 王国平,王洪光. 集对分析用于污水处理厂的综合评价. 江苏环境科技,2002,15(1):16-18
[143] 李凡修,梅 平,陈武. 多元集对模型在污水处理厂改造决策中应用. 环境科学与技术,2004,27(6):92-94
[144] 向跃霖. 物元分析在污染源治理方案优化决策中的应用初探. 污染防治技术,1995,8(4):227-232
[145] 王洪光,王国平. 物元分析在污染源治理项目工程方案决策中的应用. 贵州环保科技,1997,3(3):6-9
[146] 慕金波,酒济明. 物元分析在环境工程方案评标中的应用. 重庆环境科学,1997,19(3):28-32
[147] 慕金波. 灰色物元分析及其在环境治理工程方案评标中的应用. 污染防治技术,1997,10(1):5-10
[148] 李秀玲. 处理污水的优化方案. 数理统计与管理,1998,17(4):16-18
[149] 杨健,陆雍森,施鼎方. 运用生命周期分析(LCA)评估和选择废水处理工艺. 工业用水与废水,2000,31(3):4-6
[150] Julia C C. Tsai. Stochastic dynamic programming formulation for a wastewater treatment decision-making framework. Annals of Operations Research, 2004, 132(1-4): 207-221
[151] 黄明,陆燕勤,许立巍等. 基于奖优惩劣的密切值法在污水处理工艺优选中的应用. 环境污染与防治,2006,28(7):512-514
[152] 张文泉,张世英,江立勤. 基于熵的决策评价模型及应用. 系统工程学报,1995,10(3):69-75
[153] 王 靖,张金锁. 综合评价中确定权重向量的几种方法比较. 河北工业大学学报,2001,30(2):52-58
[154] 张 斌. 多目标系统决策的模糊集对分析方法. 系统工程理论与实践, 1997,17(12):108-114
[155] 顾夏声. 废水生物处理数学模式. 北京:清华大学出版社,1993,43-44
[156] 张自杰. 排水工程(下册). 北京:中国建筑工业出版社,2000,118-122
[157] 周雹. 活性污泥工艺简明原理和设计计算. 北京:中国建筑工业出版社,2005,12-13
[158] Lauria D T, Uunk J B, Schaefer J K. Activated sludge process design. Journal of the Environmental Engineering Division, ASCE, 1977, 103(4): 625-645
[159] Bernard C M, Rolf Eliassen. Sensitivity analysis of activated sludge economics. Journal of the Environment Engineering Division, ASCE, 1966, 92 (SA2): 147-167
[160] 屈济宁,高廷耀,周曾炎等. 关于活性污泥法若干参数的讨论. 同济大学学报,1998,26(6):721-724
[161]庞煜,龙腾锐,张承刚等. 非线性规划在活性污泥系统方案优化设计中的应用. 给水排水,2004,30(3):32-35
[162] Evenson D E, Orlob G T, Monser K R. Preliminary selection of wastewater treatment system. Journal of Water Pollution Control Federation, 1969, 41(10): 1845-1858
[163] Chia S S, Defilippi J A. System optimization of wastewater treatment plant process design. Journal of the Environmental Engineering Division, ASCE, 1970, 96(SA2, 4): 409-421
[164] Erickson L E, Chen G K C, Fan L T. Modeling and optimization of biologicalwaste treatment systems. Chem. Eng. Prog. Symp. Ser., 1968,64: 97-110
[165] Erickson L E, Fan L T. Optimization of the hydraulic regime of activated sludge systems. Journal of Water Pollution Control Federal, 1968, 40: 345-362
[166] Shih C S, De Filippi J. A system optimization of waste treatment plant process design. Journal of Sanitary Engineering Division, ASCE, 1970, 96: 409-421
[167] Schuhe D D, Loehr R C. Analysis of duck farm waste treatment systems. In: Proc of Int Symp On livestock Wastes. American Soc. of Agri. Eng., St. Joseph, Michigan, 1971, 73-80
[168] Ecker J G, McNamara J R. Geometric programming and the preliminary design of industrial wastewater treatment plants. Water Resource Research, 1971, 7: 18-22
[169] Marsden J R, Pingry D E, Whinston A. Production function theory and the optimal design of waste treatment facilities. Applied Economics, 1972, 4: 279-290
[170] Sterling R A. Computerized algorithm simplify meeting federal effluent standads. Water Sewage Works, 1976, 123(10): 68-72
[171] Middleton A C, Lawrence A W. Least cost design of activated sludge systems. Journal of Water Pollution Control Federation, 1976, 48(5): 889-905
[172] Adams B J, Panagiotakopoulos D. Network approach to optimal wastewater treatment system design. Journal of Water Pollution Control Federal, 1977, 49: 623-632
[173] Tarrer A R, Grady C P L, Lim H C, et al. Optimal activated sludge design under uncertainty. Journal of the Environmental Engineering Division, ASCE, 1976, 102(EE3): 657-673
[174] Bowden K, Gale R S, Wreight D E. Evaluation of the CIRIA prototype model for the design of sewage treatment works. Journal of Water Pollution Control Federation, 1976, 48(1): 192-205
[175] Grady C P L jr. Simplified optimization of activated sludge process. Journal of the Environmental Engineering Division, ASCE, 1977, 103(EE3): 413-429
[176] Lauria D T, Uunk J B, Schaefer J K. Activated sludge process design. Journal of the Environmental Engineering Division, ASCE, 1977, 103(4): 625-645
[177] Craig E E, Meredith D D, Middleton A C. Algorithm for optimal activated sludge design. Journal of the Environmental Engineering Division, ASCE, 1978, 104(EE6): 1101-1117
[178] Takama N, Loucks D P. A multilevel model and algorithm for somemulti-objective problems. Water Resour. Bull., 1981, 17: 448-453
[179] Dick R I. Intergration of sludge management processes. In: Proc Int Symp On Wastewater Engrg and Mgmt, Society of Environmental Science of Guangdong, People’s Republic of China, Mar., 1984
[180] Kao J J, Brill E D jr., Pfetter J T, et al. Computer-based environment for wastewater treatment plant design. Journal of the Environmental Engineering Division, ASCE, 1993, 119(5): 931-945
[181] Alderman B J, Theis T L, Collins A G. Optimal design for anaerobic pretreatment of municipal wastewater. Journal of the Environmental Engineering Division, ASCE, 1998, 124(1): 4-10
[182] 庞煜,龙腾锐. 污水处理工艺系统优化设计理论的研究与发展. 环境污染治理技术与设备,2003,4(3):42-49
[183] 彭永臻,于颂明. 活性污泥法污水处理厂最优化设计的研究. 哈尔滨建筑工程学院学报,1989,22(4):68-75
[184] 蒋惠忠. 污水处理流程优化动态规划. 环境保护,1998,(12):15-17
[185] 郑传宁等. 系统论在污水厂设计中的应用. 合肥工业大学学报(自然科学版),1999,22(1):48-52
[186] 邹锐,郭怀成,刘磊. 洱海流域环境经济相协调的农林土地利用不确定性系统规划. 环境科学学报,1999,19(2):186-193
[187] Huang G H, Beatz B W, Patry G G. Grey quadratic programming: application to waste management planning under uncertainty. Engineering Optimization, 1995, 27(1): 201-223
[188] Huang G H, Moore. Grey linear programming: its solving approach, and its application. International Journal of Systems Sciences, 1993, 24(1): 159-172
[189] Voshel D, Sak J G. Effect of primary effluent suspended solids and BOD on activated sludge production. Journal of Water Pollution Control Federation, 1968, 40(5): 203-212
[190] Chapman D T. The influence of process variables on secondary clarification. Journal of Water Pollution Control Federation, 1983, 55(12): 1425-1434
[191] Pfeffer J T. Temperature effects on anaerobic fermentation of domestic refuse. Biotechnology and Bioengineering, 1974, 16: 771-787
[192] 秦肖生,曾光明. 遗传算法在水环境灰色非线性规划中的应用. 水科学进展,2002,13(1):31-36
[193] 徐丽娜,李琳琳. 遗传算法在非线性系统辨识中的应用研究. 哈尔滨工业大学学报,1999,31(2):39-42
[194] 高尚. 基于 MATLAB 遗传算法优化工具箱的优化计算. 微型电脑应用,2002,18(8):52-54
[195] 张德跃,黄礼共,罗昊进等. 温州城市人均综合供水、排污的预测指标. 中国给水排水,2003,19(6):20-22
[196] 张雅君,刘全胜. 蓄水量预测方法的评析与择优. 中国给水排水,2001,17(7):27-29
[197] 王国平. 城市污水及主要污染物排放量的灰色预测模型. 环境工程,12(1):47-51
[198] 林锰杰,高京广. 广东省废水排放量预测及宏观控制研究. 广东工业大学学报(社会科学版),2003,3(6):165-167
[199] 刘海洋,戴志军,桓曼曼. 广州市污水排放分析及预测. 重庆环境科学,2002,24(1):62-64
[200] 赵晶,孟凡玲,徐建新,陈南祥. 基于灰色 Verhulst 的城市污水排放量预测模型. 华北水利水电学院学报,2006,27(2):94-95
[201] 张宏伟,岳琳,王亮. 城市污水排放量预测模型研究. 中国给水排水,2005,21(9):40-42
[202] 雷绍兰. 基于电力负荷时间序列混沌特性的短期负荷预测方法研究:[重庆大学博士学位论文]. 重庆:重庆大学,2005,13-17
[203] 林淑真,刘长龄,游保杉. 日径流时间序列之混沌动力探求.台湾水利,1996,44(2):13-23
[204] 王文均,叶敏,陈显维. 长江径流时间序列混沌特性的定量分析. 水科学进展,1994,5(2):87-94
[205] I Rodriguez-Iturbe, B F de Power. Chaos in rainfall. Water Resources Research, 1989, 25(7): 1667-1675
[206] 陈士华,陆君安. 混沌动力学初步. 武汉:武汉水利电力大学出版社,1998
[207] 刘树勇,朱石坚,俞翔. 改进相空间重构方法在混沌识别中的应用研究. 海军工程大学学报,2006,18(2):69-73
[208] Kugiumtzis D. State space reconstruction parameters in the analysis of chaotic time series——the role of the time window length. Physica D: Nonlinear Phenomena, 1996, 95(1): 13-28
[209] Alexei Potapov, Jürgen Kurths. Correlation integral as a tool for distinguishing between dynamics and statistics in time series data. Physica D: Nonlinear Phenomena, 1998, 120(3-4): 369-385
[210] Kim H S, Eykholt R, Salas J D. Nonlinear dynamics, delay times, and embedding windows. Physica D: Nonlinear Phenomena, 1999, 127(1-2): 48-60
[211] Alan Wolf, Jack B Swift, Harry L. Swinney, et al. Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 1986, 16(3): 217-236
[212] Rosenstein M T, Collins J J, Deluca C J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 1993, 65(1-2): 117-134
[2] 国家环境保护总局.中国城市环境保护. http://www.zhb.gov.cn/cont/mhcity/ mfcsxx/gldt/200506/t20050602_67180_7.htm,2005-06-02
[3] 建设部. 建设部通报全国城市污水处理情况. 中国环保产业,2005,11(10):27
[4] 国家环境保护总局. 2005 年中国环境统计公报. 中国环保产业,2006,12(6):4-5
[5] 羊寿生,张辰. 城市污水处理厂设计中热点问题剖析. 给水排水,1999,25(9):1-3
[6] 柯崇宜,石淑倩,潘宁等. 现有污水处理厂存在的若干问题探讨. 环境保护,2000,27(2):21-22
[7] 周雹,谭振江. 中、小型城市污水处理厂的优选工艺. 中国给水排水,2000,16(10):21-24
[8] 邵林广. 南方城市污水处理工艺的选择. 给水排水,2000,26(6):32-34
[9] 沈光范. 关于城市污水处理厂设计的若干问题. 中国给水排水,2000,16(3):20-23
[10] 许劲. 关于城市污水处理厂设计的若干问题讨论. 给水排水,2001,27(7):15-18
[11] 尹 文 选 , 尹 卫 红 . 中 小 城 市 污 水 处 理 厂 设 计 的 几 个 问 题 . http://www.gpszx.com/disquisition/detail.php?id=1640,2003-06-07
[12] 林晓明. 论城市污水处理厂建设规模与处理标准的确定. 给水排水,1997,23(9):20-23
[13] 邵林广. 南方城市污水处理厂实际运行水质远小于设计值的原因及其对策. 给水排水,1999,25(2):11-13
[14] 庞子山. 活性污泥法工艺系统优化设计模型及应用研究:[重庆大学博士学位论文]. 重庆:重庆大学,2004,6-7
[15] 孙振世,陆芳. 我国城市污水处理厂运行状况及加强监管对策. 中国环境管理,2003,22(5):1-2
[16] Lynn W R, Logan J A, Charnes A. System analysis for planning wastewater treatment plants. Journal of Water Pollution Control Federation, 1962, 34: 565-581
[17] Chia S S, Krishman P. Dynamic optimization for industrial wastewater treatmentdesign. Journal of Water Pollution Control Federation, 1969, 41(10): 1787-1802
[18] Paul M Berthouex, Lawrence B Polkowski. Optimum waste treatment plant design under uncertainty. Journal of Water Pollution Control Federation, 1970, 42(9): 1589-1613
[19] Rossman L A. Synthesis of waste treatment systems by implicit enumeration. Journal of Water Pollution Control Federation, 1980, 52(1): 148-160
[20] Tyteca D. Nonlinear programming model of wastewater treatment plant. Journal of the Environmental Engineering Division, ASCE, 1981, 107(4): 747-766
[21] Tyteca D, Smeer Y. Nonlinear programming design of wastewater treatment plant. Journal of the environmental Engineering Division, ASCE, 1981, 107(4): 767-779
[22] Tang C C, Brill E D jr, Pfetter J T. Optimization techniques for secondary wastewater treatment system. Journal of the Environmental Engineering Division, ASCE, 1987, 113(5):935-951
[23] Tang C C, Brill E D jr, Pfetter J T. Comprehensive model of an activated sludge wastewater treatment system. Journal of the Environmental Engineering Division, ASCE, 1987, 113(5):952-969
[24] Uber J G, Pfetter J T, Brill E D jr. Robust optimal design of wastewater treatment I: General approach. Journal of the Environmental Engineering Division, ASCE, 1991, 117(4):425-437
[25] Uber J G, Pfetter J T, Brill E D jr. Robust optimal design of wastewater treatment II: Application. Journal of the Environmental Engineering Division, ASCE, 1991, 117(4):438-456
[26] 林玉鹏. 考虑不确定性因素影响的城市污水处理厂优化设计的理论和方法研究:[湖南大学硕士学位论文]. 长沙:湖南大学,2000,42-49
[27] 邢可霞,郭怀诚. 环境模型不确定性分析方法综述. 环境科学与技术,2006,29(5):112-114
[28] 刘毅,陈吉宁,杜鹏飞. 环境模型参数识别与不确定性分析. 环境科学,2002,23(6):6-10
[29] 张贤根. 论认识系统中的不确定性. 湖北民族学院学报(社会科学版), 1994,4(12):16-18
[30] 万咸涛. 江河水质监测中不确定性的应用研究. 江苏环境科技,1999(2):1-4
[31] 曾光明,黄国和. 环境科学与工程中的不确定性理论与方法. 长沙:湖南大学出版社,2002
[32] 梅亚东,吴贻名. 城市污水处理厂群动态投入问题研究. 武汉水利电力学院学报,1990,23(2-3):66-73
[33] 梅亚东. 城市污水处理厂系统最优规划研究. 见:中国青年科学技术论文精选. 北京:中国科学技术出版社,1994,786-791
[34] 徐祖信. 河流污染治理规划理论与实践. 北京:中国环境科学出版社,2003
[35] L Akca, C Kinaci, M Karpuzcu. A model for optimum design of activated sludge plants. Water Research, 1993, 27(9):1461-1468
[36] Beck M B. Systems analysis in management. The Netherlands: Pergamon Press, 1987
[37] 刘新春,任光耀,潘晓玲等. 资源利用中的不确定性、不可逆性分析. 新疆环境保护,2003,25(2):40-42
[38] 刘国东,丁晶. 水环境中不确定性方法的研究现状与展望. 环境科学进展,1996,4(4):46-53
[39] 徐宗学. 洪水风险率模型:[武汉水利电力大学博士论文]. 武汉: 武汉水利电力大学,1988,10-12
[40] Yacov Y Haimes. Integrated risk and uncertainty assessment in water resources within a multi-objective. Journal of Hydroology, 1984, 68(2): 222-234
[41] Benjamin J R, Cornell C A. Probability: statistics and decision for civil engineers. New York:Mograw-Hill, 1970, 70-92
[42] 王清印,倪天智,刘开第等. 不确定性数学及其研究方向. 见:灰色系统学术论文集. 开封:河南大学出版社,1993,151-154
[43] 王清印. 不确定性系统科学概要. 河北经贸大学学报,1997,18(1):48-50
[44] 龙腾锐,冯裕钊,郭劲松. 考虑不确定因素的污水厂日进水量预测法. 中国给水排水,2001,17(5):1-5
[45] 陈吉宁.环境系统不确定性分析的理论与发展. http://www.h2o-china.com/ paper/viewpaper.asp?id=1503,2001-11-08
[46] Beck M B. Principles of modeling. Water Science and Technology, 1991, 24(1):1-8
[47] Charles S Melching, Ben Chie Yen, Harry G Wenzel jr. A reliability estimation in modeling watershed runoff with uncertainties. Water Resources Research, 1990, 26: 2275-2286
[48] J Chen, H S Wheater. Identification and uncertainty analysis of soil water retention models using lysimeter data. Water Resources Research, 1999, 35(9): 2401-2414
[49] NMCI. Uncertainty analysis in water quality modeling, a review of methods of model calibration and uncertainty analysis, and their application to the TOPLEM water quality models. USA:TOPLEM, 1999
[50] Beck M B. Water quality modeling: a review of the analysis of uncertainty. Water Resources Research, 1987, 23(5): 1393-1442
[51] Dong-il Seo. 水质模型不确定性的一种新定量方法. 见:给水与废水处理国际会议论文集. 北京:中国建筑工业出版社,1994,833-838
[52] 梁晋文,陈林才,何贡. 误差理论与数据处理. 北京:中国计量出版社,1989,78-81
[53] 熊大国. 随机过程理论与应用. 北京:国防工业出版社,1991,103-105
[54] 汪培庄. 模糊集合论及其应用. 上海:上海科学技术出版社,1983,1-8
[55] 曾光明,杨春平,卓利. 环境系统灰色理论与方法. 北京:中国科学技术出版社,1994
[56] 赵克勤,宣爱理. 集对论——一种新的不确定性理论、方法与应用. 系统工程,1996,14(1):18-23
[57] 王光远. 未确知信息及其数学处理. 哈尔滨建筑工程学院学报,1990,23(4):1-9
[58] 刘开第,吴和琴. 未确知数学. 武汉:华中理工大学出版社,1997,27-35
[59] Pawlak Z. Rough sets. International Journal of Computer and Information Sciences, 1982, 11(2): 341 -356
[60] Pawlak Z. Rough set approach to Knowledge-based decision support. European Journal of Operational Research, 1997, 99(1):48-57
[61] 曾黄麟. 粗集理论及其应用. 重庆:重庆大学出版社,1998,56-59
[62] 卿晓霞,龙腾锐. 污水处理系统中的人工智能技术应用现状与展望. 给水排水,2006,32(8):99-102
[63] Wade M, Katebi R. Data mining and knowledge extraction in wastewater treatment plants. IEE Seminar Developments in Control Systems in the Water Industry, 2002, (120): 1-25
[64] 吕金虎,陆君安,陈士华. 混沌时间序列及其应用. 武汉:武汉大学出版社,2002
[65] Ching-Gung Wen, Chih-Sheng Lee. Development of a cost functions for wastewater treatment systems with fuzzy regression. Fuzzy Sets and Systems, 1999, 106(11): 143-153.
[66] Ho-Wen Chen, Ni-Bin Chang. A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants. Journal of Environmental Management, 2002, 65(4): 383-409.
[67] Bernard C M, Rolf Eliassen. Sensitivity analysis of activated sludge economics. Journal of the Environment Engineering Division, ASCE, 1966, 92 (SA2):147-167
[68] Abasaeed E A. Sensitivity analysis on a sequencing batch reactor model I: effect of kinetic parameters. Journal of Chemical Technology and Biotechnology, 1997, 72(3): 379-383
[69] Abasaeed E A. Sensitivity analysis on a sequencing batch reactor model II: effect of stoichiometric and operating parameters. Journal of Chemical Technology and Biotechnology, 1999, 74(4): 451-455
[70] A Abusam, K J Keesman, G van Straten, et al. Sensitivity analysis in oxidation ditch modeling: the effect of variations in stoichiometric, kinetic and operating parameters on the performance indices. Journal of Chemical Technology and Biotechnology, 2001, 76(4): 430-438
[71] A Abusam, K J Keesman, G van Straten, et al. Estimation of uncertainties in the performance induces of an oxidation ditch benchmark. http://www.aenf.wau.nl/ mrs/staff/abusam/research/thesis/chapter6a.pdf, 2002
[72] Marcos von Sperling. Design of Facultative ponds based on uncertainty analysis. Water Science and Technology, 1996, 33(7): 41-47
[73] Michael S K, Chen Larry E Erickson, Liang-tsing Fan. Consideration of sensitivity and parameter uncertainty in optimal process design. Industrial & Engineering Chemistry Process Design and Development, 1970, 9(4): 514-519
[74] 上海市政工程设计研究院. 给水排水设计手册(第 10 册 技术经济). 北京:中国建筑工业出版社,2000,475-485.
[75] Anita Lee, Chun Hung Cheng, Jaydeep Balakrishnan. Software development cost estimation: Integration neural with cluster analysis. Information & Management, 1998, 34(1): 1-9
[76] Al-Tabtabai H, Alex PA, Tantash M. Preliminary cost estimation of highway construction using neural networks. Cost Engineering, 1999, 41(3): 19-24
[77] H Murat Günaydin, S Zeynep Do?an. A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management, 2004, 22(7): 595-602
[78] Sergio Cavalieri, Paolo Maccarrone, Roberto Pinto. Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics, 2004, 91(2): 165-177
[79] 骆敏. 污水处理成本的费用函数分析. 江苏环境科技,1992,5(3):23-25
[80] 尹军,朱佳. 污水处理厂单元处理构筑物费用函数的研究与探讨.中国给水排水,1990,6(6):16-19
[81] 尹军,李晓君,宫正. 城市污水二级处理系统费用函数研究. 水处理技术, 1988,14(4):226-229.
[82] 袁国文,Johanes Pinnekamp. 降低污水处理厂成本的可能性与措施. 见:21世纪国际城市污水处理及资源化发展战略研讨会与展览会会议论文集. 北京:21 世纪国际城市污水处理及资源化发展战略研讨会与展览会组委会,2001,45-47
[83] 傅国伟,程声通. 水污染控制系统规划. 北京:清华大学出版社,1985,46-47
[84] K P Tsagarakis, D D Mara, A Angelakis. Application of cost criteria for selection of municipal wastewater treatment systems. Water, Air and Soil Pollution, 2003, 142(2): 187-210
[85] David Steer, Todd Aseltyne, Lauchlan Fraser. Life-cycle economic model of small treatment wetlands for domestic wastewater disposal. Ecological Economics, 2003, 44 (2): 359-369
[86] 左玉辉. 环境系统工程导论. 南京:南京大学出版社,1985,209-211
[87] S Gillot, B De Clercq. Optimization of wastewater treatment plant design and operation using simulation and cost analysis. http://citeseer.ist.psu.edu/232972. html, 1999
[88] S Gillot, P Vermeire, P Jacquet, et al. Integration of wastewater treatment plant investment and operation costs for scenario analysis using simulation. http:// biomath.ugent.be/~Peter/ftp/pvr226.pdf, 1999
[89] Li Xianwen.Technical economics analysis of stabilization ponds. Water Science and Technology, 1995, 31(12): 103-110
[90] 蒋惠忠,卢旭阳. 污水处理单元构筑物费用函数研究. 环境经济,2000,(8):41-43
[91] 于九如,杨泽华. 模糊线性回归及其应用实例. 系统工程理论与实践,1995,(4):32-37
[92] 苑希民,李鸿雁,刘树坤等. 神经网络和遗传算法在水科学领域中的应用. 北京:中国水利水电出版社,2002,1-73
[93] Kung S Y. An algebraic projection analysis for optimal hidden units size and learning rates in back propagation learning. International Conference on Neural network, USA,1988, 363-370
[94] 金龙,况雪源,黄海洪等. 人工神经网络预报模型的过拟合研究. 气象学报,2004,62(1):62-69
[95] 袁曾任. 人工神经网络及其应用. 北京:清华大学出版社,1999,118-131
[96] Mirchandani Gagan, Gao Wei. On hidden nodes for neral sets. IEEE Transactionson circuits and systems, 1989, 36(5): 661-664
[97] H White, et al. Artificial Neural Networks: Approximation and Learning Theory. Cambridge, Mass: Blackwell, 1992, 16-18
[98] Martin T Hagan, Mohammad B Menhaj. Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks, 1994,5(6): 989-993
[99] Ahmed Gsmal El-Din, Daniel W. Smith. A neural network model to predict the wastewater inflow incorporating rainfall events. Water Research, 2002, 36(5): 1115-1126
[100] Michael Fernández, Julio Caballero. Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. Bioorganic & Medicinal Chemistry, 2006, 14(1): 280-294
[101] M Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, 1974, 36 (1): 111-147
[102] Francesco Bini Verona, Massimo Ceraolo. Use of neural networks for customer tario exploitation by means of short-term load forecasting. Neurocomputing, 1998, 23: 135-149
[103] Raymond La?, Sovan Lek, Jacques Moreau. Predicting fish yield of African lakes using neural networks. Ecological Modelling, 1999, 120(2-3): 325-335
[104] 阎平凡,张长水. 人工神经网络与模拟进化计算. 北京:清华大学出版社,2003
[105] John Neter, illiam Wasserman, Michael H. Kutner 著,张勇,王国明,赵秀珍译. 应用线性回归模型. 北京:中国统计出版社,1990
[106] Hegazy T Fazio P, Moselhi O. Developing practical neural network applications using back-propagation. Microcompu.Civil Eng., 1994, 9: 145-159.
[107]闻新,周露,王丹力等. MatLab 神经网络应用设计. 北京:科学出版社,2000,207-243
[108] 辛大欣,王长元,肖峰. BP 神经网络在回归分析中的应用研究. 西安工业学院学报,2002,22(2):129-135
[109] 建设部,国家环境保护总局,科学技术部. 城市污水处理及污染防治技术政策. http://www.jxepb.gov.cn/cyjs/hbcy/content/02.htm, 2000-07-13
[110] 温汝俊. 大城市污水处理工艺的经济选择因素. 重庆环境科学,2000,22(4):22-24
[111] Ellis KV, Tang S L. Wastewater treatment optimization model for developing world. I: Model development. Journal of Environmental Engineering Division, ASCE, 1991,117: 501-581
[112] 刘永淞. 污水处理设计的综合评价与优化分析. 化工给水排水,1996,(1):1-3
[113] Annelies J B, Heinz A P, Ralf O, et al. Indicators for the sustainability assessment of wastewater treatment systems. Urban Water, 2002, 4(2): 153-161
[114] 中国系统工程学会决策科学专业委员会. 决策科学理论与实践. 北京:海洋出版社,2003
[115] 王浙明. 钱塘江流域城市污水处理厂工程费用分析和方案优化:[浙江大学硕士学位论文]. 杭州:浙江大学,2000,13-14
[116] 刘占伟,邓四二,腾弘飞. 复杂工程系统设计方案评价方法综述. 系统工程与电子技术,2003,25(12):1488-1491
[117] 王媛,郭薇,贺业讯等. 价值工程在设计方案选择中的应用. 中国给水排水,2000,16(9):56-57.
[118] 赵峪,刘萍. AHP(层次分析法)对城镇污水处理厂的选型进行决策. 污染防治技术,1995,8(2):98-100
[119] 慕金波,翟素军. 层次分析法在废水治理工程评价中的应用. 苏州城建环保学院学报,1996,9(4):49-58
[120] 汤民淮,蔡俊雄. 利用层次分析计算污染治理设施评价指标权重. 环境科学与技术,1997,(1):27-31
[121] 张建锋,黄廷林. 关中地区污水处理工艺选择的系统分析. 环境工程,1999,17(6):61-64
[122] 吴育华,郑道英,吴庆雄. 海口长流污水厂工艺方案多目标评价与选择. 中国给水排水,1999,15(4):41-43
[123] 高湘,徐良. 层次分析法在西安咸阳国际机场污水处理投标方案选择中的应用. 见:第四届全国给水排水青年学术年会论文集. 2000
[124] 胡天觉,陈维平,曾光明等. 运用层次分析法对株洲霞湾污水处理厂污水处理工艺方案择优. 环境工程,2000,18(1):61-63
[125] 邵海员,刘绮,黎锡流. 污水处理厂规划中 AHP 方法的应用. 环境科学与技术,2006,29(2):97-100
[126] 刘永淞. 用模糊数学方法分析污水处理厂设计. 化工给排水设计,1995,(3):21-22
[127] 向跃霖. 改进的模糊相似选择法在污染源治理方案优选中的应用. 铁道劳动安全卫生与环保,1996,23(3):204-206
[128] 慕金波. 环境工程评标的模糊优选模型及应用. 江苏环境科技,1997,10(1):9-11
[129] 慕金波,酒济明. 废水治理工程的多层次模糊综合评判方法及应用. 山东环境,1997,81(6):9-11
[130] 凌猛,杭世君. 城市污水处理厂工艺方案模糊决策方法的应用. 给水排水,1998,24(3):6-9
[131] 张松滨,李万海,王红. 污水处理设施性能评价中的赋权相关分析. 环境工程,1999,17(5):55-58
[132] 李如忠. 多层次模糊综合评判模型在城市污水处理中的应用. 淮南工业学院学报,2000,20(4):1-4
[133] 黄绍娃,胡志光. 模糊综合评判法确定城市污水处理工艺. 工业用水与废水. 2004,35(4):12-14
[134] 狄军贞,刘书贤. 污水处理厂治理设施的模糊综合评价及优选. 工程设计与建设,2004,36(1):40-42
[135] 王玲玲,曾光明,黄国和等. 小区域污水处理的多目标规划设计探讨. 环境工程,2004,22(6):88-89
[136] 李小荣,张邵怡. 晋城市城区污水厂处理工艺的选择. 水处理技术,2002,28(3):175-176
[137] 王浙明,史惠祥,苏雨生等. 灰色关联模型用于工程方案优化. 中国给水排水,2002,18(1):81-84
[138] 慕金波,杨红红,姜涛. 灰色综合评判用于工厂废水处理方案的优选. 环境工程,1992,10(5):37-41
[139] 慕金波,姜涛. 灰色多层次综合评判模型在废水治理工程综合评价中的应用. 污染防治技术,1995,8(4):260-263
[140] 向跃霖. 治污投资方向选择的灰靶决策法. 化工环保,1996,16(2):102-106
[141] 向跃霖. 环境治理工程方案的灰色优选新模型及其应用. 环境工程,1998,16(3):52-55
[142] 王国平,王洪光. 集对分析用于污水处理厂的综合评价. 江苏环境科技,2002,15(1):16-18
[143] 李凡修,梅 平,陈武. 多元集对模型在污水处理厂改造决策中应用. 环境科学与技术,2004,27(6):92-94
[144] 向跃霖. 物元分析在污染源治理方案优化决策中的应用初探. 污染防治技术,1995,8(4):227-232
[145] 王洪光,王国平. 物元分析在污染源治理项目工程方案决策中的应用. 贵州环保科技,1997,3(3):6-9
[146] 慕金波,酒济明. 物元分析在环境工程方案评标中的应用. 重庆环境科学,1997,19(3):28-32
[147] 慕金波. 灰色物元分析及其在环境治理工程方案评标中的应用. 污染防治技术,1997,10(1):5-10
[148] 李秀玲. 处理污水的优化方案. 数理统计与管理,1998,17(4):16-18
[149] 杨健,陆雍森,施鼎方. 运用生命周期分析(LCA)评估和选择废水处理工艺. 工业用水与废水,2000,31(3):4-6
[150] Julia C C. Tsai. Stochastic dynamic programming formulation for a wastewater treatment decision-making framework. Annals of Operations Research, 2004, 132(1-4): 207-221
[151] 黄明,陆燕勤,许立巍等. 基于奖优惩劣的密切值法在污水处理工艺优选中的应用. 环境污染与防治,2006,28(7):512-514
[152] 张文泉,张世英,江立勤. 基于熵的决策评价模型及应用. 系统工程学报,1995,10(3):69-75
[153] 王 靖,张金锁. 综合评价中确定权重向量的几种方法比较. 河北工业大学学报,2001,30(2):52-58
[154] 张 斌. 多目标系统决策的模糊集对分析方法. 系统工程理论与实践, 1997,17(12):108-114
[155] 顾夏声. 废水生物处理数学模式. 北京:清华大学出版社,1993,43-44
[156] 张自杰. 排水工程(下册). 北京:中国建筑工业出版社,2000,118-122
[157] 周雹. 活性污泥工艺简明原理和设计计算. 北京:中国建筑工业出版社,2005,12-13
[158] Lauria D T, Uunk J B, Schaefer J K. Activated sludge process design. Journal of the Environmental Engineering Division, ASCE, 1977, 103(4): 625-645
[159] Bernard C M, Rolf Eliassen. Sensitivity analysis of activated sludge economics. Journal of the Environment Engineering Division, ASCE, 1966, 92 (SA2): 147-167
[160] 屈济宁,高廷耀,周曾炎等. 关于活性污泥法若干参数的讨论. 同济大学学报,1998,26(6):721-724
[161]庞煜,龙腾锐,张承刚等. 非线性规划在活性污泥系统方案优化设计中的应用. 给水排水,2004,30(3):32-35
[162] Evenson D E, Orlob G T, Monser K R. Preliminary selection of wastewater treatment system. Journal of Water Pollution Control Federation, 1969, 41(10): 1845-1858
[163] Chia S S, Defilippi J A. System optimization of wastewater treatment plant process design. Journal of the Environmental Engineering Division, ASCE, 1970, 96(SA2, 4): 409-421
[164] Erickson L E, Chen G K C, Fan L T. Modeling and optimization of biologicalwaste treatment systems. Chem. Eng. Prog. Symp. Ser., 1968,64: 97-110
[165] Erickson L E, Fan L T. Optimization of the hydraulic regime of activated sludge systems. Journal of Water Pollution Control Federal, 1968, 40: 345-362
[166] Shih C S, De Filippi J. A system optimization of waste treatment plant process design. Journal of Sanitary Engineering Division, ASCE, 1970, 96: 409-421
[167] Schuhe D D, Loehr R C. Analysis of duck farm waste treatment systems. In: Proc of Int Symp On livestock Wastes. American Soc. of Agri. Eng., St. Joseph, Michigan, 1971, 73-80
[168] Ecker J G, McNamara J R. Geometric programming and the preliminary design of industrial wastewater treatment plants. Water Resource Research, 1971, 7: 18-22
[169] Marsden J R, Pingry D E, Whinston A. Production function theory and the optimal design of waste treatment facilities. Applied Economics, 1972, 4: 279-290
[170] Sterling R A. Computerized algorithm simplify meeting federal effluent standads. Water Sewage Works, 1976, 123(10): 68-72
[171] Middleton A C, Lawrence A W. Least cost design of activated sludge systems. Journal of Water Pollution Control Federation, 1976, 48(5): 889-905
[172] Adams B J, Panagiotakopoulos D. Network approach to optimal wastewater treatment system design. Journal of Water Pollution Control Federal, 1977, 49: 623-632
[173] Tarrer A R, Grady C P L, Lim H C, et al. Optimal activated sludge design under uncertainty. Journal of the Environmental Engineering Division, ASCE, 1976, 102(EE3): 657-673
[174] Bowden K, Gale R S, Wreight D E. Evaluation of the CIRIA prototype model for the design of sewage treatment works. Journal of Water Pollution Control Federation, 1976, 48(1): 192-205
[175] Grady C P L jr. Simplified optimization of activated sludge process. Journal of the Environmental Engineering Division, ASCE, 1977, 103(EE3): 413-429
[176] Lauria D T, Uunk J B, Schaefer J K. Activated sludge process design. Journal of the Environmental Engineering Division, ASCE, 1977, 103(4): 625-645
[177] Craig E E, Meredith D D, Middleton A C. Algorithm for optimal activated sludge design. Journal of the Environmental Engineering Division, ASCE, 1978, 104(EE6): 1101-1117
[178] Takama N, Loucks D P. A multilevel model and algorithm for somemulti-objective problems. Water Resour. Bull., 1981, 17: 448-453
[179] Dick R I. Intergration of sludge management processes. In: Proc Int Symp On Wastewater Engrg and Mgmt, Society of Environmental Science of Guangdong, People’s Republic of China, Mar., 1984
[180] Kao J J, Brill E D jr., Pfetter J T, et al. Computer-based environment for wastewater treatment plant design. Journal of the Environmental Engineering Division, ASCE, 1993, 119(5): 931-945
[181] Alderman B J, Theis T L, Collins A G. Optimal design for anaerobic pretreatment of municipal wastewater. Journal of the Environmental Engineering Division, ASCE, 1998, 124(1): 4-10
[182] 庞煜,龙腾锐. 污水处理工艺系统优化设计理论的研究与发展. 环境污染治理技术与设备,2003,4(3):42-49
[183] 彭永臻,于颂明. 活性污泥法污水处理厂最优化设计的研究. 哈尔滨建筑工程学院学报,1989,22(4):68-75
[184] 蒋惠忠. 污水处理流程优化动态规划. 环境保护,1998,(12):15-17
[185] 郑传宁等. 系统论在污水厂设计中的应用. 合肥工业大学学报(自然科学版),1999,22(1):48-52
[186] 邹锐,郭怀成,刘磊. 洱海流域环境经济相协调的农林土地利用不确定性系统规划. 环境科学学报,1999,19(2):186-193
[187] Huang G H, Beatz B W, Patry G G. Grey quadratic programming: application to waste management planning under uncertainty. Engineering Optimization, 1995, 27(1): 201-223
[188] Huang G H, Moore. Grey linear programming: its solving approach, and its application. International Journal of Systems Sciences, 1993, 24(1): 159-172
[189] Voshel D, Sak J G. Effect of primary effluent suspended solids and BOD on activated sludge production. Journal of Water Pollution Control Federation, 1968, 40(5): 203-212
[190] Chapman D T. The influence of process variables on secondary clarification. Journal of Water Pollution Control Federation, 1983, 55(12): 1425-1434
[191] Pfeffer J T. Temperature effects on anaerobic fermentation of domestic refuse. Biotechnology and Bioengineering, 1974, 16: 771-787
[192] 秦肖生,曾光明. 遗传算法在水环境灰色非线性规划中的应用. 水科学进展,2002,13(1):31-36
[193] 徐丽娜,李琳琳. 遗传算法在非线性系统辨识中的应用研究. 哈尔滨工业大学学报,1999,31(2):39-42
[194] 高尚. 基于 MATLAB 遗传算法优化工具箱的优化计算. 微型电脑应用,2002,18(8):52-54
[195] 张德跃,黄礼共,罗昊进等. 温州城市人均综合供水、排污的预测指标. 中国给水排水,2003,19(6):20-22
[196] 张雅君,刘全胜. 蓄水量预测方法的评析与择优. 中国给水排水,2001,17(7):27-29
[197] 王国平. 城市污水及主要污染物排放量的灰色预测模型. 环境工程,12(1):47-51
[198] 林锰杰,高京广. 广东省废水排放量预测及宏观控制研究. 广东工业大学学报(社会科学版),2003,3(6):165-167
[199] 刘海洋,戴志军,桓曼曼. 广州市污水排放分析及预测. 重庆环境科学,2002,24(1):62-64
[200] 赵晶,孟凡玲,徐建新,陈南祥. 基于灰色 Verhulst 的城市污水排放量预测模型. 华北水利水电学院学报,2006,27(2):94-95
[201] 张宏伟,岳琳,王亮. 城市污水排放量预测模型研究. 中国给水排水,2005,21(9):40-42
[202] 雷绍兰. 基于电力负荷时间序列混沌特性的短期负荷预测方法研究:[重庆大学博士学位论文]. 重庆:重庆大学,2005,13-17
[203] 林淑真,刘长龄,游保杉. 日径流时间序列之混沌动力探求.台湾水利,1996,44(2):13-23
[204] 王文均,叶敏,陈显维. 长江径流时间序列混沌特性的定量分析. 水科学进展,1994,5(2):87-94
[205] I Rodriguez-Iturbe, B F de Power. Chaos in rainfall. Water Resources Research, 1989, 25(7): 1667-1675
[206] 陈士华,陆君安. 混沌动力学初步. 武汉:武汉水利电力大学出版社,1998
[207] 刘树勇,朱石坚,俞翔. 改进相空间重构方法在混沌识别中的应用研究. 海军工程大学学报,2006,18(2):69-73
[208] Kugiumtzis D. State space reconstruction parameters in the analysis of chaotic time series——the role of the time window length. Physica D: Nonlinear Phenomena, 1996, 95(1): 13-28
[209] Alexei Potapov, Jürgen Kurths. Correlation integral as a tool for distinguishing between dynamics and statistics in time series data. Physica D: Nonlinear Phenomena, 1998, 120(3-4): 369-385
[210] Kim H S, Eykholt R, Salas J D. Nonlinear dynamics, delay times, and embedding windows. Physica D: Nonlinear Phenomena, 1999, 127(1-2): 48-60
[211] Alan Wolf, Jack B Swift, Harry L. Swinney, et al. Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 1986, 16(3): 217-236
[212] Rosenstein M T, Collins J J, Deluca C J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 1993, 65(1-2): 117-134