模拟植物生长算法在水库群优化调度中的应用

介绍了模拟植物生长算法的基本原理,针对梯级水电站优化调度问题的特点,建立了该问题的模拟植物生长算法求解模型.对基本模拟植物生长算法进行了改进,并设计了相应的算法步骤.在此基础上,将基本模拟植物生长算法及其改进算法应用于水库群优化调度中.通过对雅砻江梯级优化调度的应用分析,表明由形态素浓度决定其方向性和随机性搜索机制的模拟植物生长算法具有较强的全局搜索能力,为解决水库群优化调度问题提供了新的途径.     

Optimal planning of water supply system for long-term sustainability

Developing a long-term system plan for sustainable water supply is a challenging task due to system complexity and future uncertainties in water demands and source availability. Here a coupled optimization model is proposed for water supply system design and long-term operations by deciding system component sizes and water flow allocations simultaneously. The objective is to minimize overall system costs (i.e., sum of capital and operation costs) while meeting water demands and operational constraints. The economic costs include initial component construction costs and operation expenditure over pre-defined operation years. The proposed model integrates a genetic algorithm with a linear programming model to optimize water infrastructure investments and annual water transfers satisfying flow constraints. The coupled model was applied to a simplified water supply network composed of multiple water sources and users. For the application network, various qualities of water from different sources could be supplied to different users. Plausible future scenarios with time varying water demands were simulated representing potential future conditions. Application results show that the proposed coupled model is beneficial in decision making process to design structural components in near future and prepare long-term policies for water shortage and water right issues in upcoming years. The model can be tailored to a specific system and various regulations and conditions can be incorporated within the model without adding complexity to the optimization framework.     

Simulation and optimization of an experimental membrane wastewater treatment plant using computational intelligence methods

The optimization of relaxation and filtration times of submerged microfiltration flat modules in membrane bioreactors used for municipal wastewater treatment is essential for efficient plant operation. However, the optimization and control of such plants and their filtration processes is a challenging problem due to the underlying highly nonlinear and complex processes. This paper presents the use of genetic algorithms for this optimization problem in conjunction with a fully calibrated simulation model, as computational intelligence methods are perfectly suited to the nonconvex multi-objective nature of the optimization problems posed by these complex systems. The simulation model is developed and calibrated using membrane modules from the wastewater simulation software GPS-X based on the Activated Sludge Model No.1 (ASM1). Simulation results have been validated at a technical reference plant. They clearly show that filtration process costs for cleaning and energy can be reduced significantly by intelligent process optimization.     

Artificial Life Algorithm for Management of Multi-reservoir River Systems

The design and operation of civil engineering systems, particularly water resources systems, has been pursued from the perspective of minimizing costs and related negative impacts, maximizing benefits, or a combination thereof. Due to the complex, nonlinear nature of the majority of systems, together with an increase in digital computing capabilities, global search algorithms are becoming a common means of meeting these objectives. This paper employs an artificial life algorithm, derived from the artificial life paradigm. The algorithm is evaluated using standard optimization test functions and is subsequently applied to determine optimal dam operations in multi-reservoir river systems. The optimal dam operation scheme is that which indirectly minimizes environmental impacts caused by short-term water level fluctuations. Optimal releases are sought by coupling an artificial life algorithm with FLDWAV, a one-dimensional, steady flow simulation model. The resulting multi-reservoir management model is successfully applied to a portion of the Illinois River Waterway.     

Optimization on the VFDs’ Operation for Pump Units

The third Huaiyin pumping station in the South-to-North Water Diversion Project aims to solve the problem of lack of water. In order to save on electricity while satisfying the required flow demand, the operation optimization problem of the third Huaiyin pumping station is investigated, with a mathematical model set up to simulate the optimal daily operation, in which the pump units can have variable speed. After analyzing the characteristics of the mathematical model, an improved dynamic programming algorithm is presented to decrease the dimensions of the operation optimization problem and save electricity cost, which enables us to perform a practical engineering application. After discretization of the optimization problem has been achieved, the number of operational schedule sequences could be reduced and optimal scheduling could be achieved to save electricity costs by power constraint, classified enumeration constraint, feasible combination constraint and flow demand constraint. Through research and analysis of the working of the third Huaiyin pumping station, optimal operational scheduling of multiple pump units with variable speed operation using variable frequency drive (VFD) can lessen the electricity tariff significantly compared with dynamic programming with the successive approximation method and decomposition/aggregation-dynamic programming method. The operational electricity tariff is reduced by 7.71% by the improved dynamic programming algorithm in comparison with the benchmark scheduling. Simulation results demonstrate that the cost efficiency comes from variable speed operation with VFD and flow demand transfer from the time periods when a high electricity tariff applies to time periods of a low electricity tariff based on the time-of-use electricity tariff.

     

Model for Optimal Operation of Water Distribution Pumps with Uncertain Demand Patterns

An optimization model is presented for pump operation based upon minimizing operation costs and indirectly the maintenance costs of pumps considering uncertainty of specified demand (load) curves. The purpose of this model is to determine pump operation to meet the uncertain demands as well as to satisfy the pressure requirements in the water distribution system. In addition, constraints on the number of pump (‘on-off’) switches are included as a surrogate to indirectly minimizing the maintenance costs. This model is a mixed integer nonlinear programming (MINLP) problem using a chance constraint formulation of the uncertain demand constraint. The optimization model was solved using the LocalSolver option in A Mathematical Programming Language (AMPL). The model was first applied to the operation of an example pumping system for an urban water distribution system (WDS) illustrating a reduction in operation costs using the optimization model. The optimization model with the chance-constraint for demand was applied for a range of demand satisfaction uncertainties. A decrease in the operation costs was observed with an increased uncertainty in demand satisfaction, which shows that the model further optimizes the operations considering the relaxed constraints. Model application could be extended to operations of pumping systems during emergencies and contingencies such as droughts, component failures etc.     

Assessment of Penalty-Free Multi-Objective Evolutionary Optimization Approach for the Design and Rehabilitation of Water Distribution Systems

This paper describes a penalty-free multi-objective evolutionary optimization approach for the phased whole-life design and rehabilitation of water distribution systems. The optimization model considers the initial construction, rehabilitation and upgrading costs. Repairs and pipe failure costs are included. The model also takes into consideration the deterioration over time of both the structural integrity and hydraulic capacity of every pipe. The fitness of each solution is determined from the trade-off between its lifetime costs and its actual hydraulic properties. The hydraulic analysis approach used, known as pressure-dependent modelling, considers explicitly the pressure dependency of the water supply consumers receive. Results for two sample networks in the literature are included that show the algorithm is stable and finds optimal and near-optimal solutions reliably and efficiently. The results also suggest that the evolutionary sampling efficiency is very high. In other words, the number of solutions evolved and analysed on average before finding a near-optimal solution is small in comparison to the total number of feasible and infeasible solutions. We found better solutions than those reported previously in the literature for the two networks considered. For the Kadu network, for example, the new best solution costs Rs125,460,980—a significant improvement. Additional statistics that are based on extensive testing are included.     

改进的遗传算法在渡槽结构优化设计中的应用

传统的遗传算法存在寻优效率较低、精度不高等缺点。为了解决这些问题,对传统遗传算法进行了改进——采用Fortran语言编制结构优化程序,并将改进后的遗传算法应用于大型预应力矩形渡槽优化设计中。通过对比原设计方案与优化方案的结构安全性及经济性,验证了优化设计方案具有显著的经济效益。结果表明,改进后的遗传算法提高了计算性能和优化效果。     

电力电量平衡算法及其应用研究

电力电量平衡法原是水电站规划设计中确定装机容量的基本方法，通过对该方法的研究，总结出其应用于水电系统短期调度的突出特点是原理简单、可较好地处理机组连续开停机问题，并可有效地减少优化计算工作量；同时还提出了该原理的数学计算方法及其在水电站群短期优化调度数学模型求解中的应用方法，为计算机软件系统的开发奠定了基础。实际应用表明，该方法合理、实用。     

改进蛙跳算法在水电站经济调度中的应用

为寻求更为方便有效的水电站经济调度算法,以提高水电站运行效率、节约宝贵的水资源。根据水电站厂内经济运行模型,合理处理约束条件,提出了一种用改进蛙跳算法求解水电站厂内经济运行问题的数学模型。实例分析证明,此算法比传统的等耗量微增率法寻优效果更好,且运算量较小,便于灵活操作和改进,是一种非常实用的求解水电站厂内经济运行问题的优化算法。     

Benchmarking of municipal waste water treatment plants (an Austrian project).

An Austrian research project focused on the development of process indicators for treatment plants with different process and operation modes. The whole treatment scheme was subdivided into four processes, i.e. mechanical pretreatment (Process 1), mechanical-biological waste water treatment (Process 2), sludge thickening and stabilisation (Process 3) and further sludge treatment and disposal (Process 4). In order to get comparable process indicators it was necessary to subdivide the sample of 76 individual treatment plants all over Austria into five groups according to their mean organic load (COD) in the influent. The specific total yearly costs, the yearly operating costs and the yearly capital costs of the four processes have been related to the yearly average of the measured organic load expressed in COD (110 g COD/pe/d). The specific investment costs for the whole treatment plant and for Process 2 have been related to a calculated standard design capacity of the mechanical-biological part of the treatment plant expressed in COD. The capital costs of processes 1, 3 and 4 have been related to the design capacity of the treatment plant. For each group (related to the size of the plant) a benchmark band has been defined for the total yearly costs, the total yearly operational costs and the total yearly capital costs. For the operational costs of the Processes 1 to 4 one benchmark ([see symbol in text] per pe/year) has been defined for each group. In addition a theoretical cost reduction potential has been calculated. The cost efficiency in regard to water protection and some special sub-processes such as aeration and sludge dewatering has been analysed.     

An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

A hybrid evolutionary search algorithm is developed to optimize the classical single-criterion operation of multi-reservoir systems. The proposed improved genetic algorithm-simulated annealing (IGA-SA) which combines genetic algorithms (GAs) and the simulated annealing (SA) is a new global optimization algorithm. The algorithm is capable of overcoming the premature convergence of GAs and escaping from local optimal solutions. In addition, it is faster than a traditional unimproved GA-SA algorithm. A case study of optimization operation on generation electricity of a 3-reservoir system in series over 41-year (from May 1940 to April 1981) time periods in Wujiang River, one branch of Yangtze River in China, was performed. The objective is to maximize generation output from the system over each 12-month operating periods. Trade-off analyses on binary coding representation and real-value coding representation of GAs are performed. Sensitivity to some parameters of the GA, the SA and the IGA-SA is analyzed, respectively, and the appropriate values of parameters are suggested. The performance of the proposed algorithm is compared with that of the existing genetic algorithm, the simulated annealing and the dynamic programming (DP). Results demonstrate that the GA is better than the DP, the SA performs better than the GA and the IGA-SA is more efficient than SA. The IGA-SA produces higher quality solutions and costs less computation time compared with the traditional GA-SA. The results obtained from these applications have proved that the IGA-SA has the ability of addressing large and complex problems and is a new promising search algorithm for multi-reservoir optimization problems.     

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.     

A Simulation-Based Optimization Model for Flood Management on a Watershed Scale

Using structural and nonstructural measures for flood damage reduction is a long-standing problem in water resources planning and management. In the present study, an algorithm is presented for the optimal design of structural and nonstructural flood mitigation measures based on simulation-based optimization approach. For this purpose, the MIKE-11 simulation model, a one-dimensional hydrodynamic model, was used to calculate the potential damages of different flood scenarios under the various combinations of structural and nonstructural measures and this model was coupled with the NSGA-II multi-objective optimization model to provide the optimal Pareto solutions between two conflict objectives of minimizing the investment costs of flood mitigation measures and the potential damages of the floodplain. The proposed model was then applied to a small watershed in central part of Iran as a case study and the optimal trade-off solutions were calculated for different flood scenarios. Using these trade-offs, for each level of funding, decision makers can assign the optimal design of flood mitigation measures considering decision criteria.     

基于模拟退火遗传算法的自压树状管网优化

将遗传算法全局优化和模拟退火的良好局部搜索能力有机结合,构造出一种退火遗传算法用于自压树状管网的优化设计方法。假定管网中每一管段最多只能由两种管径的管道组成,建立了以管网造价为目标函数,以管长、标准管径为决策变量的自压树状管网优化数学模型。采用基于不可行度的退火算法处理约束条件,应用遗传算法进行优化计算。仿真实例结果表明,该模型与算法在求解自压树状管网优化问题上,具有良好的优化性能和求解效率。     

水工高性能混凝土配合比多目标智能优化设计与分析方法

高性能混凝土(HPC)在水利工程中的多元化应用对其配合比设计提出了更高要求.为实现高效、准确的配合比设计,本文结合人工智能算法和元启发式搜索技术,开发了一种综合考虑多目标需求(力学性能、经济性和环保性)的HPC配合比优化设计新方法.其主要包括:(1)建立了以配合比参数为设计变量,以抗压强度、单方生产成本和碳排放量为优化...     

Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm

Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and Jaya Algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multi-reservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.     

改进的粒子群算法在水库优化调度中应用

针对水库优化调度中存在的规模庞大、结构复杂,涉及大量的决策变量和复杂的约束条件,呈现出高维度、非线性、强约束特性,传统的优化方法难以直接求解或者计算效率低,存在早熟等问题。为了提高粒子群算法全局搜索能力和收敛性能,把下山搜索策略引入到粒子群智能算法中,提出了改进的粒子群算法。函数测试证明该方法改进了算法的鲁棒性,提高了算法求解效率。上述优化算法应用于水库优化调度模型求解中,计算结果表明:该方法易于实现,求解效率高,为水库优化调度模型求解提供了新的途径。     

Improved PSO algorithm based on chaos theory and its application to design flood hydrograph

The deficiencies of basic particle swarm optimization (bPSO) are its ubiquitous prematurity and its inability to seek the global optimal solution when optimizing complex high-dimensional functions.To overcome such deficiencies,the chaos-PSO (COSPSO) algorithm was established by introducing the chaos optimization mechanism and a global particle stagnation-disturbance strategy into bPSO.In the improved algorithm,chaotic movement was adopted for the particles'initial movement trajectories to replace the former stochastic movement,and the chaos factor was used to guide the particles' path.When the global particles were stagnant,the disturbance strategy was used to keep the particles in motion.Five benchmark optimizations were introduced to test COSPSO,and they proved that COSPSO can remarkably improve efficiency in optimizing complex functions.Finally,a case study of COSPSO in calculating design flood hydrographs demonstrated the applicability of the improved algorithm.     

Calibration and simulation of two large wastewater treatment plants operated for nutrient removal.

Control and optimisation of plant processes has become a priority for WWTP managers. The calibration and verification of a mathematical model provides an important tool for the investigation of advanced control strategies that may assist in the design or optimization of WWTPs. This paper describes the calibration of the ASM2d model for two full scale biological nitrogen and phosphorus removal plants in order to characterize the biological process and to upgrade the plants' performance. Results from simulation showed a good correspondence with experimental data demonstrating that the model and the calibrated parameters were able to predict the behaviour of both WWTPs. Once the calibration and simulation process was finished, a study for each WWTP was done with the aim of improving its performance. Modifications focused on reactor configuration and operation strategies were proposed.