Reliability-Constrained Optimization of Water Treatment Plant Design Using Genetic Algorithm

A new approach that links genetic algorithm (GA) as an optimization tool with Monte Carlo simulation (MCS)-based reliability program is presented for reliability-constrained optimal design of water treatment plant (WTP). The reliability of a WTP is defined as the probability that it can achieve the desired effluent water quality standard (WQS). The objective function minimizes the treatment cost, subjected to design and performance constraints, and to achieve desired reliability level for meeting the given effluent WQS. The random variables used to generate the reliability estimates are suspended solids (SS) concentration, flow rate, specific gravity of floc particle, temperature of raw water, sedimentation basin performance index, and model coefficients. The application of GA-MCS approach for design of a WTP is illustrated with a hypothetical case study. The annualized cost of WTP is affected by the number of uncertain parameters included in the analysis, coefficient of variation of uncertain parameters, effluent WQS, and target reliability level. Analysis suggests that higher reliability at lower annual cost of treatment can be achieved by limiting the fluctuation of uncertain parameters. Results show that distribution of effluent SS is also affected by the uncertainty. The suggested GA-MCS approach is efficient to evaluate treatment cost-reliability tradeoff for WTP. Results demonstrate that the combination of GA with MCS is an effective approach to obtain the reliability-constrained optimal/near-optimal solution of WTP design problem consistently.     

Improvement of Grain Size and Dome Height of Microalloyed Steels Using Taguchi Method Based on Grey Relational Grade in Controlled Rolling

An efficient approach was introduced for improving the condition of major controlled rolling process parameters of roughing, finishing and coiling temperatures and optimizing these parameters to obtain minimum grain size and maximum dome height simultaneously. Taguchi method combined with grey relational analysis was applied to achieve optimum grain size and dome height during controlled rolling process. For this purpose, four levels for the above temperatures were chosen and sixteen experiments were conducted based on orthogonal array of Taguchi method. Based on Taguchi approach, signal-to-noise (S/N) ratios were calculated and used in order to obtain the optimum levels for every input parameter. Analysis of variance revealed that finishing and coiling temperatures have the maximum effect on the grain size and dome height of microalloyed steels. The confirmation tests with the optimal levels of parameters indicated that the grain size and dome height of controlled rolled microalloyed steels can be improved effectively through this approach.     

Optimization of Pile Groups Using Hybrid Genetic Algorithms

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.     

Machinability Investigations on High Chrome White Cast Iron Using Multi Coated Hard Carbide Tools

This study investigated the performance of multilayer hard coated carbide tool and multi-response optimization of the turning process for an optimal parametric combination to yield the minimum cutting forces and machining power with a maximum material removal rate (MRR) using Taguchi and artificial neural network (ANN) methods. In recent times, high chrome white cast iron finds increasing applications in aerospace, mining, mineral process industries. Its machinability using carbide insert (TiC/TiCN/Al₂O₃) cutting tool has been studied. The influences of cutting parameters on the cutting forces, MRR and machining power of the process have been analyzed using analysis of variance and the results are correlated using ANN. Linear regression method was used to establish the relation between the cutting parameters and the process responses. The confirmation test reveals that, the accuracy of prediction of ANN is better than that of the regression analysis. In view of the good performance of the carbide tools (at optimum conditions), it can replace the cosly CBN, with improved economic benefits.     

Comparison of Using Mixed-Integer Programming and Genetic Algorithms for Construction Site Facility Layout Planning

The use of modular construction has gained wide acceptance in the industry. For a specific construction facility layout problem such as site precast standardized modular units, it requires the establishment of an on-site precast yard. Arranging the precast facilities within a construction site presents real challenge to site management. This complex task is further augmented with the involvement of several resources and different transport costs. A genetic algorithm (GA) model was developed for the search of a near-optimal layout solution. Another approach using mixed-integer programming (MIP) has been developed to generate optimal facility layout. These two approaches are applied to solve with an example in this paper to demonstrate that the solution quality of MIP outperforms that of GA. Further, another scenario with additional location constraints can also be solved readily by MIP, which, however, if modeled by GA, the solution process would be complicated. The study has highlighted that MIP can perform better than GA in site facility layout problems in which the site facilities and locations can be represented by a set of integer variables.     

Optimization of Fine Hydro‐Blanking

Optimization of the process of the fine hydro‐blanking is proposed. In this approach, the V‐ring indenter carved on the work piece substitutes for the guide plate that is used by the conventional fine blanking and is pressurized by hydraulic pressure. In addition, the counter force is also replaced by hydraulic pressure to produce a uniform back‐pressure by acting as the ejector. To find the final optimal solution, the Taguchi‐FE method is adopted to simulate and find the optimal factors according to the Taguchi technology. Once the optimal factors were chosen, a hybrid system that combined the artificial neural network (ANN) with the genetic algorithm (GA) is used to search and find the final optimal solution. It was observed that the V‐ring cavity on the work‐piece has the same efficacy as conventional fine blanking. In addition, the results of the experiment and finite element simulation using the optimal factors were compared. It was found that the study could achieve its stated objectives.     

Integrated Optimization of Control System for Smart Cylindrical Shells Using Modified GA

In this paper, modeling of the vibration of cylindrical shell components of space structures incorporating piezoelectric sensor/actuators (S/As) for optimal vibration control is proposed and formulated. The parameters of the control system, which include the placement and sizing of the piezoelectric S/As and the feedback control gains, were considered as design variables and optimized simultaneously. The effect of the amount of piezoelectric patches was investigated as well. The criterion based on the maximization of energy dissipation was employed for the optimization of the control system. A modified real-encoded genetic algorithm (GA) dealing with various constraints has been developed and applied to search for the optimal placement and size of the piezoelectric patches as well as the optimal feedback control gains. The results of three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped-simply supported plate, demonstrated significant vibration suppression based on the optimal design of the control system. It was also found that for specific controlled vibration modes, the optimal distribution of the piezoelectric S/As should be located at the areas separated by the nodal lines to achieve the optimal control effect. This finding would be useful for the practical design of smart structures.     

Parametric Optimization of Pulsed Current Gas Arc Welding of Dissimilar Welding Between UNS32750 and AISI 321 Based on Taguchi Method

In this research, Taguchi method (DOE technique) was used to optimize welding parameters in a dissimilar joint between the duplex stainless steel UNS32750 and the austenite stainless steel AISI 321 for the corrosion resistance. For joining, pulsed current gas arc welding with the ER2594 electrode was used. Pulse current, background current, % on time and current frequency were considered in three levels. Welding conditions were based on the L₉ Taguchi’s orthogonal array design of experiment. Signal-to-noise ratio was used to estimate the optimal conditions. The parameters and levels were considered as the optimal conditions in which the higher pitting potential could be obtained. Analysis of variance was carried out to determine the influential effect of each parameter. The pitting potential was evaluated by the potentiodynamic polarization test in the 3.5% NaCl solution at room temperature. Microstructural characterization and mechanical properties of the optimal joint were determined by optical microscopy and Vickers microhardness, respectively. According to the results of Taguchi method, the pulse current of 130 A, the Background current of 60 A, the % on time of 90% and the current frequency of 3 Hz were obtained as the optimal conditions. Pitting potential under optimal conditions (1.03 V) was close to Taguchi prediction (0.99 V). Analysis of Variance also indicated that the most effective parameter on the pitting corrosion was Background current. The percentage contributions of pulse current, background current, current frequency and % on time were 38.01, 32.48, 22.85 and 6.6%, respectively. The results showed that adjusting heat input led to the equal austenite-ferrite proportion.     

遗传算法在矿用车辆转向优化设计中的应用

本课题以本溪北方机械重型汽车制造厂生产的25 t矿用汽车为研究背景,运用遗传算法(geneticalgorithm,GA)解决25 t重型矿用汽车的转向优化设计问题。该矿用车辆采用六杆转向梯形结构。程序使用4个参量控制梯形,对4个参量分别编码,采用精英保留模型,经三代遗传操作后得到了优化结果。解决了转向误差过大的问题。结果表明遗传算法适用于矿用车辆六杆转向机构优化设计,与其他传统算法相比自适应性强、参量约束控制简单、搜索效率高。     

Metallurgical Approach Towards Explaining Optimized EDM Process Parameters for Better Surface Integrity of AISI D2 Tool Steel

Many attempts have been made to model and optimize performance parameters of Electrical Discharge Machining (EDM) process. In this investigation, the experimental design includes L9 orthogonal array based on the Taguchi methodology. The corresponding effect of input current, duty cycle and spark on time, on surface integrity, has been studied on AISI D2 tool steel. The residual stress measurement is done using X-ray diffraction method. The obtained output results regarding material removal rate, surface roughness, crack density, and residual stress have been modeled and optimized by regression equation and Genetic algorithm respectively. In this investigation, a metallurgical approach has been introduced towards the explanation of the resulted optimized response parameters.     

Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique

Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity (T), hydraulic conductivity (K), storage coefficient (S), and leakance (L), for which the graphical method is widely used. In the present study, the efficacy of the genetic algorithm (GA) optimization technique is assessed in estimating aquifer parameters from the time-drawdown pumping test data. Computer codes were developed to optimize various aquifer parameters under different hydrogeologic conditions by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using 12 sets of the published and unpublished aquifer test data. The aquifer parameters were also estimated by the graphical method using AquiferTest software, and were compared with those obtained by the GA technique. The GA technique yielded significantly low values of the sum of square errors (SSE) for almost all the datasets under study. The results revealed that the GA technique is an efficient and reliable method for estimating various aquifer parameters, especially in the situation when the graphical matching is poor. Also, it was found that because of its inherent characteristics, GA avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. Furthermore, the performance evaluation of the developed GA-based computer codes showed that the fitness value (SSE) of the best point in a population reduces with increasing generation number and population size. The analysis of the sensitivity of the parameters during the performance of GA indicated that a unique set of aquifer parameters was obtained for all three aquifer systems. The GA-based computer programs with interactive windows developed in this study are user-friendly and can serve as a teaching and research tool, which could also be useful for practicing hydrologists and hydrogeologists.     

Analysis of Significant Parameters Influencing Formability of Titanium Alloy by Using Over all Evaluation Criteria and New Matrix Model Based on Taguchi Method

In this study indigenously developed Titan 31, has been hot rolled under alpha–beta process at elevated temperatures with heavy reduction and heat treated at different temperatures with different heat treatment processes to obtain microstructure which is conducive for formability. The uniformly distributed, globular and very fine micro structure has been obtained at finally rolled, and annealed at 800 °C. ANOVA analysis has been carried out to analyze parameters influencing formability on the basis of results obtained from tensile tests in plane strain at different elevated temperatures, at different cross head speeds and different angles to rolling as per Taguchi design of experiment, for anisotropy value, exponent of hardening and percentage elongation. A new matrix model has been developed to optimize formability parameters. The results obtained from new matrix model have been analyzed with results of Taguchi overall evaluation criteria considering combined effect of anisotropy value, exponent of hardening and percentage elongation. The optimized value from new matrix model are temperature 718 °C, crosshead speed 1.0 mm per min (0.00066 per second), angle to rolling to which specimen tested is 44.9° and that of Taguchi overall evaluation criteria temperature 725 °C, crosshead speed (0.0005 per second), angle to rolling to which specimen tested is 45° among all factors selected in the study.     

Optimal Sampling Design Methodologies for Water Distribution Model Calibration

Sampling design (SD) for water distribution systems (WDS) is an important issue, previously addressed by various researchers and practitioners. Generally, SD has one of several purposes. The aim of the methodologies developed and presented here is to find the optimal set of network locations for pressure loggers, which will be used to collect data for the calibration of a WDS model. First, existing SD approaches for WDS are reviewed. Then SD is formulated as a multiobjective optimization problem. Two SD models are developed to solve this problem, both using genetic algorithms (GA) as search engines. The first model is based on a single-objective GA (SOGA) approach in which two objectives are combined into one using appropriate weights. The second model uses a multiobjective GA (MOGA) approach based on Pareto ranking. Both SD models are applied to two case studies (literature and real-life problems). The results show several advantages and one disadvantage of the MOGA model when compared to SOGA. A comparison of the MOGA SD model solution to the results of several published SD models shows that the Pareto optimal front obtained using MOGA acts as an envelope to the Pareto fronts obtained using previously published SD models.     

Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques

A cost effective channel section for a specified flow rate, roughness coefficients, longitudinal slope, and various cost parameters can be determined using an optimization technique. However, the derived optimal channel section may not be feasible for construction because of in situ conditions. The local soil conditions may not support the optimal side slope of the channel and if constructed, the slope may fail. It is therefore necessary to also incorporate the criteria for side slope stability in designing an optimal open channel section. In this paper, a new methodology has been developed to design a stable and optimal channel section using hybrid optimization techniques. A genetic algorithm based optimization model is developed initially to determine the factor of safety of a channel slope for given soil parameters. This optimization model is then externally linked with a separate sequential quadratic programming based optimization model to evaluate the parameters of the stable and optimal channel section. Solution for various example problems incorporating different soil parameters are illustrated to demonstrate the applicability of the developed methodology.     

基于GA和FCM的岩体结构面的混合聚类方法

提出了一种基于遗传算法(GA)和模糊C均值(FCM)算法的岩体结构面混合聚类方法.利用GA的全局搜索性能,求得初始聚类中心;在此基础上利用FCM算法,根据精度要求再作进一步求解.该方法避免了人为划定分类界限的主观性,消除了FCM聚类算法的局部最优的弱点,解决了采用普通遗传算法聚类时搜索速度和聚类精度的矛盾.结合实测数据,对应用该方法进行结构面组识别的步骤、参数选取、分组有效性、优势方位的判定进行了分析和讨论.     

Conjoining MMAS to GA to Solve Construction Site Layout Planning Problem

An optimal construction site layout planning (CSLP) is vital for project management. It can reduce the transportation flows and thus the costs of a project. Genetic algorithm (GA) is the most used algorithm to solve site layout problems, but randomly generated initial population in GA will decrease solution quality. Max-min ant system (MMAS) can offer a better initial population than the randomly generated initial population at the beginning of GA. In this study, a modified GA (MMAS-GA) formed by conjoining MMAS to the step of initialization of GA is proposed to solve CSLP problems. In order to reveal the computational capability of MMAS-GA to solve CSLP problems, the results of MMAS-GA and traditional GA are compared by solving an equal-area CSLP problem. The results showed that the proposed MMAS-GA algorithm provided a better optimal solution under the objective function of minimizing the transportation flows between the site facilities. The proposed MMAS-GA algorithm could assist project managers and planners to design optimal construction site layout, and thus to reduce construction costs.     

连铸二冷参数的优化和控制研究

近些年连铸过程模拟成为研究的重点,模拟的根本目的在于优化连铸过程.为在保证连铸坯质量的前提下进一步提高连铸生产率,基于数学上的子问题接近优化技术建立了连铸过程二冷参数优化系统,可对不同钢种、不同拉速、不同冶金限制准则下的二冷各段配水进行最优化.运用优化后的这些过程参数,将使铸机以最大生产率、最小消耗运行,同时又保证了生产无缺陷铸坯.整个优化系统包括两个模块,一个是热过程模拟模块,另一个是设计优化模块,系统靠在两个模块之间反复自动循环调用而使参数得到优化.     

Multi-objective Optimization of Cold Upsetting Parameters for Aluminium Metal Matrix Composites

Hybrid aluminium metal matrix composites reinforced with 20 wt% of silicon carbide and 3 wt% of yttria at three different aspect ratios of 0.75, 1 and 1.25 was successfully fabricated through powder metallurgy. The state of reinforcements was examined through the X-ray diffraction analysis and scanning electron microscopy analysis. The cold upset experimentation was performed in the developed composites using the Taguchi’s L₉ orthogonal design array by considering the aspect ratio, load and friction factor as the process parameters. After the experimentation, the responses (barrel radius and the workability) were calculated. A multi objective optimization was performed with the objective of maximum barrel radius and maximum workability with the help of grey relational analysis combined with Taguchi technique. The optimal process parameters were identified through the main effect plot and the significant parameters on the responses were identified with the analysis of variance.     

Optimisation of process conditions in powder injection moulding of microsystem components using robust design method Part 2 – Secondary design parameters

Abstract

Powder injection moulding (PIM) is a proper fabrication method for microsystem technology components. This paper studies the process control of PIM to create thin walled, high aspect ratio geometries, which can be easily found in microtechnology based electro chemical, mechanical and biological systems (MECS). The powder used in this study is gas atomised 316L stainless steel with a median particle size of 10 μm. The effects of reducing the thickness of high aspect ratio geometries on the secondary design parameters including the maximum wall shear stress, cooling time and standard deviations of the melt front velocity and areas are studied. The study shows process parameters including fill time, feedstock injection temperature, mould wall temperature and switchover position can be optimised using the Taguchi robust design method.     

Taguchi Optimisation of Friction Stir Processing Parameters to Achieve Maximum Tensile Strength of Mg AZ31B Alloy

In this investigation, the effect of friction stir processing process variables such as rotational speed, traverse speed and tool tilt angle on the tensile strength of magnesium alloy AZ31B was studied. The experiments were carried out according to the Taguchi parametric design L9 at various combinations of process parameters and statistical optimization technique ANOVA was used to determine the optimum levels and to find the percentage of contribution of the process parameters. The results indicate that the rotational speed is the most significant factor followed by the traverse speed and tool tilt angle for maximising the tensile strength of the friction stir processed magnesium alloy.