Design of Sewer Line

Sewerage involves the major portion of the cost of a wastewater system. In the design of a sewerage system the sewer line is the basic unit occurring repeatedly in the design process. Any savings during the design of this unit will affect the overall cost of the sewerage system. A survey of the literature showed that the present status of sewer line design algorithms use linear programming and dynamic programming. The linear programming, using piecewise linearization of the objective function and constraints in every cycle, requires substantial computer time. On the other hand, dynamic programming algorithms are subjected to the “curse of dimensionality,” thus requiring large amounts of computer memory. In this paper, using a dimensionally consistent resistance equation, the sewer line design problem is formulated as a minimization of the cost function subjected to tight and loose constraints. The problem is solved by iterative application of the Lagrange-multiplier method.     

GA-QP Model to Optimize Sewer System Design

Sanitary sewer systems are fundamental and expensive facilities for controlling water pollution. Optimizing sewer design is a difficult task due to its associated hydraulic and mathematical complexities. Therefore, a genetic algorithm (GA) based approach has been developed. A set of diameters for all pipe segments in a sewer system is regarded as a chromosome for the proposed GA model. Hydraulic and topographical constraints are adopted in order to eliminate inappropriate chromosomes, thereby improving computational efficiency. To improve the solvability of the proposed model, the nonlinear cost optimization model is approximated and transformed into a quadratic programming (QP) model. The system cost, pipe slopes, and pipe buried depths of each generated chromosome are determined using the QP model. A sewer design problem cited in literature has been solved using the GA-QP model. The solution obtained from the GA model is comparable to that produced by the discrete differential dynamic programming approach. Finally, several near-optimum designs produced using the modeling to generate alternative approach are discussed and compared for improving the final design decision.     

Integrated Decision Support System for Optimal Renewal Planning of Sewer Networks

Municipalities are under increasing pressure to adopt proactive and optimized renewal strategies to reduce the risks, life-cycle costs, and resources needed to maintain acceptable performance and service levels of their infrastructure assets. A new integrated approach for optimal renewal planning of municipal infrastructure systems has been developed. This paper discusses the application of the proposed approach to implement a GIS-based decision support system (DSS) to support the renewal planning of sewer networks. Condition rating, risk assessment, and prioritization techniques are described. A procedure for identifying and selecting the most suitable renewal technologies is also presented. A genetic algorithm-based multiobjective optimization technique is used to find a Pareto front of feasible solutions, each comprising a set of sewers to be renewed each year, along with the associated costs and expected benefits in terms of condition improvement and risk reduction. The paper also presents an example application of the prototype DSS on the sewer network in Regina, Canada.     

Simultaneous Design and Control of Stiffened Laminated Composite Structures

The problem investigated here concerns the simultaneous design and control of structures. The structure considered is a laminated stiffened composite plate for which an optimum control system is designed by the minimization of an appropriate performance index with respect to both the control forces and structural design variables consisting of stiffener areas and the number of plies of a given orientation. To ensure a physically realistic structure appropriate constraints on the stiffener sizes, total weight, and structural frequencies are imposed. Nonlinear mixed‐integer programming is used to force the number of plies to take on integral values. The entire problem is posed as a three‐level optimization problem. Using the well‐known independent modal space control method, effects of plate geometry, initial disturbance conditions, and control effort penalty parameters on the optimal design are considered. The minimization process requires derivatives of eigenvalues and eigenvectors with respect to the design variables. These derivatives can be computed by an involved analytical procedure or a relatively simple finite‐difference procedure. This paper also examines the computer cost‐effectiveness of these two procedures for the sensitivity‐derivative calculations.     

Improved Damage Localization and Quantification Using Subset Selection

Because a structure’s modal parameters (natural frequencies and mode shapes) are affected by structural damage, finite- element model updating techniques are often applied to locate and quantify structural damage. However, the dynamic behavior of a structure can only be observed in a narrow knowledge space, which usually causes nonuniqueness and ill-posedness in the damage detection problem formulation. Thus, advanced optimization techniques are a necessary tool for solving such a complex inverse problem. Furthermore, a preselection process of the most significant damage parameters is helpful to improve the efficiency of the damage detection procedure. A new approach, which combines a parameter subset selection process with the application of damage functions is proposed herein to accomplish this task. Starting with a simple 1D beam, this paper first demonstrates several essential concepts related to the proposed model updating approach. A more advanced example considering a 2D model is then considered. To determine the capabilities of this approach for more complex structures, a trust region-based optimization method is adopted to solve the corresponding nonlinear minimization problem. The objective is to provide an improved robust solution to this challenging problem.     

Model Predictive Control Synthesis Approach of Electrode Regulator System for Electric Arc Furnace

 In electric arc furnace smelting, electrode regulator system is a key link. A good electrode control algorithm will reduce energy consumption effectively and shorten smelting time greatly. The offline design online synthesis model predictive control algorithm is proposed for electrode regulator system with input and output constraints. On the offline computation, the continuum of terminal constraint sets will be constructed. On the online synthesis, the time-varying terminal constraint sets will be adopted and at least one free control variable will be introduced to solve the min-max optimization control problem. Then Lyapunov method will be adopted to analyze closed-loop system stability. Simulation and field trial results show that the proposed offline design online synthesis model predictive control method is effective.     

Optimization Modeling for Sewer Network Management

Due to their low visibility, sanitary sewers' condition assessment and rehabilitation are frequently neglected until a catastrophic failure occurs. Neglecting regular maintenance of these underground utilities adds to life-cycle costs and liabilities, and in extreme cases causes stoppage or reduction of vital services. A systematic approach for the determination of deterioration of sewer systems and an integrated management system are necessary to fully understand the complete status of this underground infrastructure system. This paper discusses the major aspects of integrated management for sewer systems, namely, the development of network identification, sewer classification and sewer condition rating systems, sewer deterioration mechanisms, prediction modeling, and the use of optimization techniques for maximizing benefit∕cost ratios over a planning horizon. A case study, based on large combined sewers from the city of Indianapolis, has been used to demonstrate the use of the framework of this integrated life-cycle based sewer management system. Deterministic dynamic programming is employed to identify appropriate sewer rehabilitation techniques at different stages during the planning horizon adopted for the sewer systems.     

Multiobjective Optimal Fuzzy Logic Control System for Response Control of Wind-Excited Tall Buildings

Performance of the structure includes both the safety as well as comfort level for the user. The safety of the structure mainly depends on the displacement response, while the comfort level of occupants depends on the acceleration response. In this paper, an approach for multiobjective optimal design of a fuzzy logic controller (FLC)-driven active tuned mass damper (ATMD) has been proposed. The evaluation criteria for both the acceleration and displacement responses have been used as the two objective functions for this multiobjective optimization problem. As a multiobjective optimization approach provides a set of Pareto-optimal solutions, the user is allowed to select an appropriate design for the specific performance requirement. The effectiveness and performance of the proposed FLC-driven ATMD has been investigated for the third-generation benchmark problem for the response control of wind-excited tall buildings. A multiobjective optimization version of the genetic algorithm has been used for obtaining the FLC and ATMD design parameters, as this approach is more effective in handling a discontinuous and nonconvex domain. Performance of the proposed control system has been found to be better than the sample controller given in the benchmark problem. The proposed controller is less sensitive than the sample controller for the variation in the stiffness of the structure.     

Robust Optimal Design of Activated Sludge Bioreactors

This paper proposes an algorithm for a robust optimal design of the biological reactor and secondary settling facilities in suspended growth nitrogen and phosphorus removal systems. Robust optimization includes uncertainty in the decision-making procedure and seeks a solution that remains “close” to optimal for all potential operation scenarios. It thus differs fundamentally from the deterministic and stochastic approaches, where uncertainty is ignored or a solution based on either the most likely scenario or the average performance over all potential scenarios is produced. The robust optimization of a suspended growth system is a multiobjective optimization problem concerned with minimization of the global costs and variability of the system’s performance around the optimal. The proposed robust optimization approach uses the ASM3 model, making use of its performance prediction capabilities to produce a powerful tool for designing activated sludge systems. The algorithm was applied to the design of the biological reactor and secondary settling facilities for the Vila Real municipal wastewater treatment plant (Portugal).     

Optimal Distribution and Control of Storage Tank to Mitigate the Impact of New Developments on Receiving Water Quality

Storage tanks are commonly installed in a combined sewer system to control the discharge of combined sewer overflows that have been identified as a leading source for receiving water pollution. The traditional approach to determine the distribution of storage tank volume in the sewer system is confined to the use of objectives within the system itself due to the limits of separate modeling of urban wastewater systems, consisting of the sewer system, wastewater-treatment plant, and receiving water. The aim of this study is to investigate the optimal distribution and control of storage tanks with an objective to mitigate the impact of new residential development on receiving water quality from an integrated modeling perspective. An integrated urban wastewater model has been used to test three optimization scenarios: optimal flow rate control, storage distribution, and a combination of these two. In addition to the cost of storage tank construction, two receiving water quality indicators, dissolved oxygen and ammonium concentration, are used as optimization objectives. Results show the benefits of direct evaluation of receiving water quality impact in the context of storage distribution optimization. Results indicate that storage allocation should be considered in conjunction with optimal flow rate control to achieve the maximum effectiveness in water pollution mitigation.     

Competent Genetic-Evolutionary Optimization of Water Distribution Systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms—a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms.     

Design∕Control Optimization of Cross‐Ply Laminates Under Buckling and Vibration

The optimal layer thicknesses and optimal feedback control function are determined for a symmetric, cross‐ply laminate. The objectives of the optimization are to maximize the biaxial buckling load (design objective) and to minimize the dynamic response to external disturbances (control objective) subject to a constraint on the expenditure of control energy. The design∕control problem is formulated as a multiobjective optimization problem by employing a performance index that combines the design and control objectives in a weighted sum. Numerical results are given for a laminate made of an advanced composite material. Comparisons of controlled and uncontrolled laminates as well as optimally designed and nonoptimal laminates indicate the benefits of treating the design and control problems in unified formulation. The implications of solving these two problems are discussed. The values of optimal design and control variables are given for a number of problem parameters.     

运用智能控制技术改进交流电弧炉电极调节器的研究

介绍了几种将智能控制技术(模糊控制器FC,人工神经网络ANN,遗传算法GA)与传统的PID(比例积分微分)控制方式相结合,并将其运用于交流电弧炉的电极控制器上以实现电极控制参数优化和电流解耦。总结了交流电弧炉智能PID电极调节器的应用前景和发展趋势。     

Iterative Heuristic Response Surface Method for Transmission Expansion Planning

Transmission expansion planning has become a central challenge in the design of competitive electricity markets. The underlying optimization/decision problem exhibits a high level of complexity, being nonconvex, dynamic, and nonlinear, with multiple objective functions and a wide range of uncertainties. In this paper we propose a methodology for handling the expansion problem that involves a novel adaptation of an iterative optimization method based on response surface models already used successfully in the design and manufacture of integrated circuits. It is implemented in a distributed environment using Web services, and is validated and compared with a genetic algorithm based methodology. An application of the proposed methodology to the Chilean Central Interconnected System is then presented and analyzed. The results demonstrate its potential in the fields of risk analysis and decision support systems in power markets.     

Global Predictive Real-Time Control of Sewers Allowing Surcharged Flows

A global predictive real-time control strategy minimizing overflow volumes from combined sewers during rainfalls is presented. For an optimal use of controlled sewer transport and storage capacities, the proposed strategy allows surcharged flows. Flows and piezometric heads in the sewer are computed according to flow inputs by a hydraulic simulation model. The optimal operation of the regulators controlling these flow inputs is determined on a finite control horizon using the generalized reduced gradient optimization algorithm. The control strategy was applied to the 23 rain events that occurred during the summer of 1989 on the urban area drained by the Marigot interceptor in Laval, Canada. In this application, the admitted intensity of surcharges was varied to assess this parameter impact on total overflow volumes. A comparison between performances of the proposed strategy and a local reactive control was also carried out. Results obtained indicate that the global predictive control can reduce overflow volumes during rainstorms and that this reduction may be improved by allowing surcharged flows.     

雾辅助物联网中公平节能的计算迁移

为了构建绿色且长生命周期的物联网,本文提出了一种雾辅助的公平节能物联网计算迁移方案。首先,基于雾节点计算能力、带宽资源以及融合雾节点能耗公平性的迁移决策的联合考量,构建了一个最小化所有任务完成总能耗的优化问题。其次,提出了基于动量梯度和坐标协同下降的公平性能耗最小化算法用于解决上述混合整数非线性规划问题。该算法基于雾节点的历史平均能耗、距离、计算能力以及剩余能量值设计了公平性指标以获得对于雾节点能耗公平性最优的迁移决策;通过提出的动量梯度与坐标协同下降法,联合优化雾节点分配给各个任务的计算及带宽资源占比,达到最小化任务处理总能耗。最后,仿真结果表明本文方案能够取得较快的收敛速度,且与随机选择和贪婪任务迁移方案两种基准方案相比,本文方案的总能耗最低,雾节点的能耗公平性最高,且网络寿命分别平均提高了23.6%和31.2%。进一步地,该方案在不同雾节点数量以及不同任务大小的环境下仍然能够保持性能优势,体现了方案鲁棒性高的特点。      

Comprehensive Design of Minimum Cost IrrigationCanal Sections

Design of a minimum cost canal section involves minimization of the sum of costs per unit length of the canal, subject to uniform flow condition in the canal. Essentially it is a problem of minimization of a nonlinear objective function subject to a nonlinear equality constraint. In this investigation, the objective function has been expressed as the cost per unit length of the canal for lining, the depth-dependent unit volume earthwork cost, and the cost of water lost as seepage and evaporation losses. A general resistance equation has been used as an equality constraint. Using a nonlinear optimization technique on an augmented function, generalized empirical equations and section shape coefficients have been obtained for the design of minimum cost irrigation canals of triangular, rectangular, and trapezoidal shapes. The optimal dimensions for any shape can be obtained from the proposed equations along with tabulated section shape coefficients. The equation for optimal cost along with the corresponding section shape coefficients is useful during the planning of a canal project. A design example with sensitivity analysis has been included to demonstrate the simplicity of the present method.     

Transit Route Network Design Using Parallel Genetic Algorithm

A transit route network design (TRND) problem for urban bus operation involves the determination of a set of transit routes and the associated frequencies that achieve the desired objective. This can be formulated as an optimization problem of minimizing the total system cost, which is the sum of the operating cost and the generalized travel cost. A review of previous approaches to solve this problem reveals the deficiency of conventional optimization techniques and the suitability of genetic algorithm (GA) based models to handle such combinatorial optimization problems. Since GAs are computationally intensive optimization techniques, their application to large and complex problems is limited. The computational performance of a GA model can be improved by exploiting its inherent parallel nature. Accordingly, two parallel genetic algorithm (PGA) models are proposed in this study. The first is a global parallel virtual machine (PVM) parallel GA model where the fitness evaluation is done concurrently in a parallel processing environment using PVM libraries. The second is a global message passing interface (MPI) parallel GA model where an MPI environment substitutes for the PVM libraries. An existing GA model for TRND for a large city is used as a case study. These models are tested for computation time, speedup, and efficiency. From the study, it is observed that the global PVM model performed better than the other model.     

Potential Computational Benefits of Geometric, Kinematic, and Behavioral Downsampling of Microscopic Traffic Network Simulation

This paper introduces the concept of microscopic simulation system scalability for the purpose of reducing the computational requirements of microsimulation modeling of large-scale traffic networks. This exploratory stage of research investigates scalability of both lane-changing and car-following behavior. The main objective of the proposed methodology is to create a reduced-scale network (microcosm) that retains most of the significant characteristics of the full-scale network (prototype). To achieve this objective a systematic downsampling procedure has been applied to a case study of a one-lane homogeneous freeway corridor in order to create a geometrically, kinematically, and behaviorally equivalent microcosm environment. This paper examines the scalability of lane-changing behavior, assuming a shifted negative exponential headway distribution, and investigates the scalability of car-following behavior under various operating conditions and downsampling ratios. The paper focuses on the tradeoff between performance and scalability of microscopic simulation systems. For each of the 48 cases considered, optimal behavioral parameters were determined based on two optimization methods: (1) Microscopic based on minimization of trajectory errors in both environments and (2) macroscopic derived from minimization of density errors in both environments throughout the simulation period. The results show that both optimization solutions were consistent in determining the optimal behavioral parameters.