新型喷雾推进通风冷却塔的应用研究

喷雾推进通风冷却塔是具有多项专利技术的新型冷却塔，应用于轧钢循环水的处理还是首次。通过试验研究，为全面替代清水型逆流式玻璃钢冷却塔在冶金行业的使用提供了依据。     

Aesthetic Appraisal of Antenna Towers

The very rapid increases in telecommunication and advancements in the field of communications technology have resulted in many different structures to support antennae. Exponential growth in cellular telephone networks has given rise to numerous new masts and towers spread all over the landscape, especially in large cities. Although everybody wants a mobile phone, nobody wants an antenna tower in their neighborhood. Recently, local authorities, nature conservancy organizations, etc., in Europe have set forward requirements to reduce the impact of new antenna masts and towers on the landscape. As a result, telecommunication companies are paying more attention and concern toward the aesthetic layout and design of new antenna supporting structures. In the future, we may have antenna supporting structures that do not look like masts and towers that are used today. This paper gives a brief history of antenna towers, an introduction to the problem, and a discussion on a new approach to design based on aesthetics. Examples and illustrations are presented showing new designs and innovative ideas.     

Object-Oriented Framework for Genetic Algorithms with Application to Space Truss Optimization

Genetic algorithms have been shown to be very effective optimization tools for a number of engineering problems. Since the genetic processes typically operate independent of the actual problem, a core genetic algorithm library consisting of all the genetic operators having an interface to a generic objective function can serve as a very useful tool for learning as well as for solving a number of practical optimization problems. This paper discusses the object-oriented design and implementation of such a core library. Object-oriented design, apart from giving a more natural representation of information, also facilitates better memory management and code reusability. Next, it is shown how classes derived from the implemented libraries can be used for the practical size optimization of large space trusses, where several constructibility aspects have been incorporated to simulate real-world design constraints. Strategies are discussed to model the chromosome and to code genetic operators to handle such constraints. Strategies are also suggested for member grouping for reducing the problem size and implementing move-limit concepts for reducing the search space adaptively in a phased manner. The implemented libraries are tested on a number of large previously fabricated space trusses, and the results are compared with previously reported values. It is concluded that genetic algorithms implemented using efficient and flexible data structures can serve as a very useful tool in engineering design and optimization.     

Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.     

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints.     

Minimizing Cutting Wastes of Reinforcement Steel Bars Using Genetic Algorithms and Integer Programming Models

Materials that are in the form of one-dimensional stocks such as steel rebars, structural steel sections, and dimensional lumber generate a major fraction of the generated construction waste. Cutting one-dimensional stocks to suit the construction project requirements result in trim or cutting losses, which is the major cause of the one-dimensional construction waste. The optimization problem of minimizing the trim losses is known as the cutting stock problem (CSP). In this paper, three approaches for solving the one-dimensional cutting stock problem are presented. A genetic algorithm (GA) model, a linear programming (LP) model, and an integer programming (IP) model were developed to solve the one-dimensional CSP. Three real life case studies from a steel workshop have been studied. The generated cutting schedules using the GA, LP, and IP approaches are presented and compared to the actual workshop’s cutting schedules. The comparison shows a high potential of savings that could be achieved using such techniques. Additionally, a user friendly Visual Basic computer program that utilizes genetic algorithms for solving the one-dimensional CSP is presented.     

Resource Optimization Using Combined Simulation and Genetic Algorithms

This paper presents a new approach for resource optimization by combining a flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly, and most productive, amount of resources that achieve the highest benefit/cost ratio in individual construction operations. To further incorporate resource optimization into construction planning, various genetic algorithms (GA)-optimized simulation models are integrated with commonly used project management software. Accordingly, these models are activated from within the scheduling software to optimize the plan. The result is a hierarchical work-breakdown-structure tied to GA-optimized simulation models. Various optimization experiments with a prototype system on two case studies revealed its ability to optimize resources within the real-life constraints set in the simulation models. The prototype is easy to use and can be used on large size projects. Based on this research, computer simulation and genetic algorithms can be an effective combination with great potential for improving productivity and saving construction time and cost.     

Concurrent Genetic Algorithms for Optimization of Large Structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high‐rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high‐performance computers such as the Cray Y‐MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high‐rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35‐story tower (with 1,262 elements and 936 degrees of freedom), using eight processors.     

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.     

Multireservoir Systems Optimization Using Genetic Algorithms: Case Study

A genetic algorithm approach is presented for the optimization of multireservoir systems. The approach is demonstrated through application to a reservoir system in Indonesia by considering the existing development situation in the basin and two future water resource development scenarios. A generic genetic algorithm model for the optimization of reservoir systems has been developed that is easily transportable to any reservoir system. This generality is a distinct practical advantage of the genetic algorithm approach. A comparison of the genetic algorithm results with those produced by discrete differential dynamic programming is also presented. For each case considered in this study, the genetic algorithm results are very close to the optimum, and the technique appears to be robust. Contrary to methods based on dynamic programming, discretization of state variables is not required. Further, there is no requirement for trial state trajectories to initiate the search using a genetic algorithm. Model sensitivity and generalizations that can be drawn from this and earlier work by Wardlaw and Sharif are also considered.     

Simplified Spreadsheet Solutions.?II: Overall Schedule Optimization

Overall schedule optimization, considering time, cost, and resource constraints is a difficult task due to the inherent complexity of projects, the difficulties associated with modeling all aspects combined, and the inability of traditional optimization tools to solve this large-size problem. In this paper, a practical approach is presented for the modeling and optimization of overall construction schedules. To simplify modeling, a spreadsheet-based model is developed to be easily usable by practitioners. The spreadsheet model integrates critical-path network scheduling with time-cost trade-off analysis, resource allocation, resource leveling, and cash flow management. The model uses the total project cost as the objective function to be minimized. To facilitate this large-size optimization, a nontraditional optimization technique, genetic algorithms, is used to locate the globally optimal solution, considering all aspects simultaneously. Details of the proposed model are described, and a hypothetical case study was used to experiment with it. Integration of the model with a simple information system is described to automate the development of optimal construction schedules.     

Geysering Generated by Large Air Pockets Released through Water-Filled Ventilation Shafts

One potential problem affecting below-grade storm-water storage tunnels is the occurrence of geysering, which is defined as the return of conveyed water to grade. Most investigations to date have linked this occurrence with inertial oscillation of the water within vertical shafts. Another mechanism that can lead to geysering is the release of air and water through ventilation towers. This study presents a systematic investigation on geysering caused by the release of large air pockets through partially water-filled ventilation towers. Parameters considered in the study included the water level in the ventilation tower, air-phase pressure head, and ventilation tower diameter. An important parameter in geysering was the diameter of the ventilation tower. A simplified numerical model was developed to simulate the experiments; it was able to reproduce the essential features of the experiments.     

Design and Construction of High Multitier Shoring Towers: Case Study

Shoring towers are the common formwork solution for high-clearance construction, but there are not many documented cases of extremely high towers. This paper reports on a project in which 60-m-(200-ft-) high shoring towers were used for slab formwork. The paper describes the design and construction of the formwork, with a focus on aspects unique to high multitier towers. The various considerations made are presented, and data and information are provided that may assist practitioners facing similar engineering undertakings. Special attention is given to the organization of the work, in light of the scarcity of data pertaining to shoring towers of such heights. Measured assembly and disassembly work inputs are presented and analyzed, and the validity of a model to predict work inputs in multitier tower erection is examined.     

Irrigation Scheduling with Genetic Algorithms

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets that may be serviced simultaneously. This problem has an analogy with the classical multimachine earliness/tardiness scheduling problem in operations research (OR). In previously published work, integer programming was used to solve irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding. This is widely reported in OR literature. Hence integer programs (IPs) can be used only to solve relatively small problems typically in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However, these need to be formulated carefully and tested thoroughly. The current research explores the potential of genetic algorithms to solve the simultaneous irrigation scheduling problem. For this purpose, two models are presented: the stream tube model and the time block model. These are formulated as genetic algorithms, which are then tested extensively, and the solution quality is compared with solutions from an IP. The suitability of these models for the simultaneous irrigation scheduling problem is reported.     

New Mathematical Optimization Model for Construction Site Layout

Layout of temporary construction facilities (objects) is an important activity during the planning process of construction projects. The construction area layout is a complex problem whose solution requires the use of analytical models. Existing popular models employ genetic algorithms that have proven to be useful tools in generating near optimal site layouts. This paper presents an alternative approach based on mathematical optimization that offers several important features and generates a global optimal solution. The construction area consists of an unavailable area that includes existing facilities (sites) and available area in which the objects can be located. The available area is divided into regions that are formulated using binary variables. The locations of the objects are determined by optimizing an objective function subject to a variety of physical and functional constraints. The objective function minimizes the total weighted distance between the objects and the sites as well as among the objects (if desired). The distance can be expressed as Euclidean or Manhattan distance. Constraints that ensure objects do not overlap are developed. The new approach, which considers a continuous space in locating the objects simultaneously, offers such capabilities as accommodating object adjacency constraints, facility proximity constraints, object–region constraints, flexible orientation of objects, visibility constraints, and nonrectangular objects, regions, and construction areas. Application of the model is illustrated using two examples involving single and multiple objects. The proposed model is efficient and easy to apply, and as such should be of interest to construction engineers and practitioners.     

碟式分酸器在烟气制酸装置中的应用

对低气速填料塔,分酸均匀尤其重要。碟式分酸器的显著优点是分酸均匀、调节范围宽。以400kt/a烟气制酸装置为例,介绍了冷却塔和干吸塔碟式分酸器的设计参数和调节性能,冷却塔在于稀酸雾粒的均匀长大,减少喷淋密度对干燥和吸收效率均有利,细小雾粒的蒸发是干燥效率的控制步骤。减小塔底气液温差可相应减少吸收塔内化学酸雾。     

Stochastic Construction Time-Cost Trade-Off Analysis

Traditional time-cost trade-off analysis assumes that the time and cost of an option within an activity are deterministic. However, in reality the time and cost are uncertain. Therefore, in analyzing the time-cost trade-off problem, uncertainties should be considered when minimizing project duration or cost. Simulation techniques are useful for analyzing stochastic effects, but a general strategy∕algorithm is needed to guide the analysis to obtain optimal solutions. This paper presents a hybrid approach that combines simulation techniques and genetic algorithms to solve the time-cost trade-off problem under uncertainty. The results show that genetic algorithms can be integrated with simulation techniques to provide an efficient and practical means of obtaining optimal project schedules while assessing the associated risks in terms of time and cost of a construction project. This new approach provides construction engineers with a new way of analyzing construction time∕cost decisions in a more realistic manner. Historical time∕cost data and available options to complete a project can be modeled, so that construction engineers can identify the best strategies to take to complete the project at minimum time and cost. Also, what-if scenarios can be explored to decide the desired∕optimal time and∕or cost in planning and executing project activities.     

Genetic Algorithm for Optimizing Supply Locations around Tower Crane

Site layout planning is a complicated issue because of the vast number of trades and interrelated planning constraints. To unfold its complexity, this paper aims to confine the study to a particularly defined area of construction: the structural concrete-frame construction stage of public housing projects. In this study, optimization of the tower crane and supply locations is targeted, as they are the major site facilities for high-rise building construction. A site layout genetic algorithm model is developed and a practical example is presented. The optimization results of the example are very promising and demonstrate the application value of the model.     

Multicriterial optimization of the batch composition for steel-smelting arc furnaces

A strategy for optimization of the batch composition in steel-smelting arc furnaces is considered. In this approach, the optimization problem is divided into two parts: the first step is to optimize the ratio of metal scrap and hot metal; the second is to optimize the chemical composition of the scrap in terms of the chromium, nickel, and copper content. As an example, this approach is applied to OAO Magnitogorskii Metallurgicheskii Kombinat (MMK), in terms of the requirements for each grade of steel. A generalized protocol for application of the optimization strategy is presented.     

Optimal Layout Design of a Satellite Module Using a Coevolutionary Method with Heuristic Rules

The layout design of a satellite module belongs to a three-dimensional (3D) packing problem with mutual-conflicting performance constraints. Taking the layout design of a simplified commercial communication satellite as a background, based on the cooperative coevolutionary framework, this paper presents a coevolutionary method with heuristic rules for the optimal layout design of a satellite module. First, a whole satellite module layout problem is decomposed into several sublayout problems according to the multisubphysical structure of a satellite module. Second, a relaxation model is adopted to distribute all objects among subspaces. Third, a coevolutionary genetic algorithm is adopted to solve the detailed layout design within the subspaces. Finally, a heuristic combination-rotation (CR) method is adopted to adjust the constraints to obtain the final whole layout scheme. Compared with the coevolutionary approach and the all-at-once optimization approaches, computational results show that the CR method can improve the computational accuracy of solutions and the proposed heuristic coevolutionary method can produces better solutions within short running times.