Methodology for the optimal component selection of electronic devices under reliability and cost constraints

The objective of this paper is to present an efficient computational methodology for the reliability optimization of electronic devices under cost constraints. The system modeling for calculating the reliability indices of the electronic devices is based on Bayesian networks using the fault tree approach, in order to overcome the limitations of the series–parallel topology of the reliability block diagrams. Furthermore, the Bayesian network modeling for the reliability analysis provides greater flexibility for representing multiple failure modes and dependent failure events, and simplifies fault diagnosis and reliability allocation. The optimal selection of components is obtained using the simulated annealing algorithm, which has proved to be highly efficient in complex optimization problems where gradient‐based methods can not be applied. The reliability modeling and optimization methodology was implemented into a computer program in Matlab using a Bayesian network toolbox. The methodology was applied for the optimal selection of components for an electrical switch of power installations under reliability and cost constraints. The full enumeration of the solution space was calculated in order to demonstrate the efficiency of the proposed optimization algorithm. The results obtained are excellent since a near optimum solution was found in a small fraction of the time needed for the complete enumeration (3%). All the optimum solutions found during consecutive runs of the optimization algorithm lay in the top 0.3% of the solutions that satisfy the reliability and cost constraints. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimizing cutting parameters in process planning of prismatic parts by using genetic algorithms

The determination of optimal cutting parameters, such as the number of passes, depth of cut for each pass, cutting speed and feed, which are applicable for assigned cutting tools, is one of the vital modules in process planning of metal parts, since the economy of machining operations plays an important role in increasing productivity and competitiveness. The present paper introduces a 'system software' developed to optimize the cutting parameters for prismatic parts. The system is mainly based on a powerful artificial intelligence (AI) tool, called genetic algorithms (GAs). It is implemented using C programming language and on a PC. It can be used as standalone system or as the integrated module of a process planning system called OPPS-PRI (Optimized Process Planning System for PRIsmatic parts) that was also developed for prismatic parts and implemented on a vertical machining centre (VMC). With the use of GAs, the impact and power of AI techniques have been reflected on the performance of the optimization system. The methodology of the developed optimization system is illustrated with practical examples throughout the paper.     

Advanced tertiary treatment of municipal wastewater using raw and modified diatomite

Advanced technology for more efficient and effective wastewater treatment is always timely needed. The feasibility of using raw and modified diatomite for advanced treatment of secondary sewage effluents (SSE) was investigated in this study. Raw diatomite at a dosing rate of 300 mg/l showed a similar potential as activated carbon for removing most organic pollutants and toxic metals from SSE. Its performance was found poor in removal of arsenic and crop nutrient constituents (e.g. ammoniacal nitrogen and phosphate) and remained unsatisfactory even when the dosing rate increased up to 500 mg/l. Where modified diatomite was in lieu of raw diatomite, the removal efficiency for all target constituents was improved by 20-50%. At the dosing rate of 150 mg/l, modified diatomite enabled the post-treated effluents to satisfy the discharge consents, with the levels of all target constituents below the regulatory limits. Modified diatomite has advantages over raw diatomite in improving removal efficiency and reducing the dosing rate required for satisfactory treatment of SSE. It is concluded that modified diatomite is much more effective and efficient than raw diatomite, as an alternative to activated carbon, for economic treatment of SSE.     

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

This article presents a framework and an illustrative example for identifying the optimal pavement maintenance and rehabilitation (M&R) strategy using a mixed-integer nonlinear programming model. The objective function is to maximize the cost-effectiveness expressed as the ratio of the effectiveness to the cost. The constraints for the optimization problem are related to performance, budget, and choice. Two different formulations of effectiveness are derived using treatment-specific performance models for each constituent treatment of the strategy; and cost is expressed in terms of the agency and user costs over the life cycle. The proposed methodology is demonstrated using a case study. Probability distributions are established for the optimization input variables and Monte Carlo simulations are carried out to yield optimal solutions. Using the results of these simulations, M&R strategy contours are developed as a novel tool that can help pavement managers quickly identify the optimal M&R strategy for a given pavement section.     

Stochastic quasi-gradient based optimization algorithms for dynamic reliability applications

On one hand, PSA results are increasingly used in decision making, system management and optimization of system design. On the other hand, when severe accidental transients are considered, dynamic reliability appears appropriate to account for the complex interaction between the transitions between hardware configurations, the operator behavior and the dynamic evolution of the system. This paper presents an exploratory work in which the estimation of the system unreliability in a dynamic context is coupled with an optimization algorithm to determine the “best” safety policy. Because some reliability parameters are likely to be distributed, the cost function to be minimized turns out to be a random variable. Stochastic programming techniques are therefore envisioned to determine an optimal strategy. Monte Carlo simulation is used at all stages of the computations, from the estimation of the system unreliability to that of the stochastic quasi-gradient. The optimization algorithm is illustrated on a HNO₃ supply system.     

An augmented Lagrange programming optimization neural network for short-term hydroelectric generation scheduling

An approach based on augmented Lagrange programming neural networks is proposed for determining the optimal hourly amounts of generated power for the hydro-units in an electric power system. This methodology is based on the Lagrange multiplier theory in optimization and searches for solutions satisfying the necessary conditions of optimality in the state space. The equilibrium point of the network satisfies the Kuhn–Tucker condition for the problem. The equilibrium point of the network corresponds to the Lagrange solution of the problem. The proposed technique has been applied to a multi-reservoir cascaded hydro-electric system with a non-linear power generation function of water discharge rate and storage volume. The water transportation delay between connected reservoirs is also taken into account. Results obtained from this approach are compared with those obtained from the two phase optimization neural network and the conventional augmented Lagrange multiplier method. It is concluded from the results that the proposed method provides better results with respect to constraint satisfaction and is very effective in yielding optimal hydro-generation schedules.     

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.     

Optimal tolerance re-allocation for the generative process sequence

In generative process planning, the sequence of machining processes is decided according to the specifications of parts, such as tolerance values. However, in order to obtain the minimal manufacturing cost, the machining process sequence needs to be considered before tolerances are assigned. It is therefore difficult to assign optimal tolerances so that a minimum manufacturing cost is achieved. This paper presents an iterative approach for reallocation of tolerance within the given functional constraints to minimize the manufacturing cost. With the given values of tolerance and corresponding process sequences, which are derived from a handbook or a designer's experience as initial inputs, each iteration of tolerance re-allocation tries to improve the total cost by shifting tolerances along the different processes in the current sequence. The re-allocation problem is formulated as a mixed integer nonlinear programming problem. The Lagrange Multiplier method has been used to solve nonlinear programming, and an exhaustive search method has been adopted to guarantee the global optimum in solving the zero-one algorithm. A prototype system has been implemented in an object-oriented programming environment and a case study is presented to demonstrate the capability of the system.     

Process integration techniques for optimizing seawater cooling systems and biocide discharge

This work addresses the problem of using seawater for cooling and the associated environmental problems caused by the usage and discharge of biocides. The discharged biocide and its byproducts are toxic to aquatic lives and must be decreased below certain discharge limits on load prior to discharge. The conventional approach has been to add biocide removal units as an end-of-pipe treatment. This work introduces an integrated approach to reducing biocide discharge though a set of coordinated strategies for in-plant modifications and biocide removal. In particular, process integration tools are used to reduce heating and cooling requirements through the synthesis of a heat-exchange network. Heat integration among process hot and cold streams is pursued economically by reconciling cost reduction in utilities versus any additional capital investment of the heat exchangers. Other strategies include maximization of the temperature range for seawater through the process and optimization of biocide dosage. This new approach has the advantage of providing cost savings while reducing the usage and discharge of biocides. A case study is used to illustrate the usefulness of this new approach and the accompanying design techniques.     

Reliability and cost optimization of electronic devices considering the component failure rate uncertainty

The objective of this paper is to present an efficient computational methodology to obtain the optimal system structure of electronic devices by using either a single or a multiobjective optimization approach, while considering the constraints on reliability and cost. The component failure rate uncertainty is taken under consideration and it is modeled with two alternative probability distribution functions. The Latin hypercube sampling method is used to simulate the probability distributions. An optimization approach was developed using the simulated annealing algorithm because of its flexibility to be applied in various system types with several constraints and its efficiency in computational time. This optimization approach can handle efficiently either the single or the multiobjective optimization modeling of the system design. The developed methodology was applied to a power electronic device and the results were compared with the results of the complete enumeration of the solution space. The stochastic nature of the best solutions for the single objective optimization modeling of the system design was sampled extensively and the robustness of the developed optimization approach was demonstrated.     

Evaluation of anode materials for the electro-oxidation of ammonia and ammonium ions

The electrochemical treatment of effluents has been used as an alternative to conventional treatments because of advantages such as environmental compatibility, process versatility and high efficiency. In addition, electrochemical treatment methods have been demonstrated to be very useful in the treatment of poorly biodegradable effluents. Studies have demonstrated that the electrochemical treatment efficiency is directly related to the electrode material. These materials should have electro-activity for reactions with the targeted pollutants, have high activation energy for undesired reactions and be corrosion resistant. Platinum and DSA^® are known to be effective for the electro-oxidation of pollutants such as phenol, ammonia and ammonium ions. This work presents the results of a comparative evaluation of some materials that can be used as anodes for the electro-oxidation of ammonia and ammonium ions in petroleum refinery effluents. The selected materials were Pt, DSA^®, Ni, anodized Al and graphite. Cyclic voltammograms confirmed the electro-activity of the Pt and DSA^® electrodes and demonstrated the electro-activity of Ni. Anodic polarization revealed the electro-activity of graphite. Anodized Al electrodes showed no activity for the electro-oxidation of the analyzed compounds. The results for the Ni and graphite electrodes are promising.     

Maximization of System Reliability with a Choice of Redundancy Strategies

Optimal solutions to the redundancy allocation problem are determined when either active or cold-standby redundancy can be selectively chosen for individual subsystems. This problem involves the selection of components and redundancy levels to maximize system reliability. Previously, solutions to the problem could only be found if analysts were restricted to a predetermined redundancy strategy for the complete system. Generally, it had been assumed that active redundancy was to be used. However, in practice both active and cold-standby redundancy may be used within a particular system design and the choice of redundancy strategy becomes an additional decision variable. Available optimization algorithms are inadequate for these design problems and better alternatives are required. The methodology presented here is specifically developed to accommodate the case where there is a choice of redundancy strategy. The problem is formulated with imperfect sensing and switching of cold-standby redundant components and k -Erlang distributed time-to-failure. Optimal solutions to the problem are found by an equivalent problem formulation and integer programming. The methodology is demonstrated on a well-known test problem with interesting results. The optimal system design is distinctly different from the corresponding design obtained with only active redundancy. The availability of this tool can result in more reliable and cost-effective engineering designs.     

Optimal urban sewer layout design using Steiner tree problems

This article introduces a novel method using mixed-integer linear programming for optimizing sewer layouts. The research concept was obtained from the obstacle-avoiding rectilinear Steiner minimal tree technique in integrated circuit routing design. The Steiner nodes serve as optional nodes to link each sewer discharge node and flow to sinks on an optimal sewer layout. Those nodes are utilized for minimizing the total cost required for constructing pipes. All the nonlinear constraints and the objective function are transformed into a simple linear format. The optimization model analysed three types of objective function conditions—solely considering the pipe cost, solely considering the ground-cutting cost, and combining both considerations—to illustrate the importance of considering all conditions to obtain an optimal sewer layout design. The proposed optimization model can save more than 38.83% of the cost required to realize the expert’s manual sewer layout design.     

Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks

An efficient methodology is proposed to find the optimum shape of arch dams considering fluid-structure interaction subject to earthquake loading. The earthquake load is considered by time variant ground acceleration applied in the upstream–downstream direction of the arch dam. The optimization is carried out by particle swarm optimization, employing real values of design variables. To reduce the computational cost of the optimization process, two strategies are adopted. In the first strategy, the most influential design variables on arch-dam response from original variables are selected using an adaptive neuro-fuzzy inference system. In the second, arch-dam response is predicted by a properly trained wavelet radial basis function neural network employing the influential design variables as the inputs. In order to assess the effectiveness of the suggested methodology, a real arch dam is considered as a test example. The numerical results demonstrate the computational advantages of the proposed methodology for the optimal design of arch dams.     

Organics removal of combined wastewater through shallow soil infiltration treatment: a field and laboratory study

Soil infiltration treatment (SIT) was proved to be an effective and low-cost treatment technique for decentralized effluents in the areas without perfect sewage systems. Field-scale experiments were conducted under several conditions to assess organics removals through a shallow soil infiltration treatment (SSIT, with effective depth 0.3m) of combined wastewater (discharge from toilets, restaurants and a gas station), while bench-scale soil column experiments were performed in laboratory in parallel to investigate biological and abiological effects of this kind of system. From the start-up to the 10th month, the field SSIT trenches experienced the lowest and highest temperatures of the operation period in Shanghai and exhibited effective organics removals after maturation, with the highest removal rate 75.8% of chemical oxygen demand (COD), highest ultraviolet absorption at 254 nm (UV(254)) decrease by 67.2% and 35.2-100% removals of phenolic and phthalate pollutants. The laboratory results indicated that more organics could be removed in room-temperatured (25+/-2 degrees C) SSIT systems under different influent COD concentrations from 45 mg/l to 406 mg/l, and the highest total COD removal rate could reach 94.0%, in which biological effect accounted for 57.7-71.9%. The results showed that temperature and hydraulic loading rate were the most important factors influencing the removals of COD and organic pollutants in SSIT.     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Investigation of the use of a central unique renewable energy system versus distributed units for crop irrigation

This paper discusses a comparison study of the use of 100% renewable energy systems for desert and remote areas, investigating both a central unique unit and distributed units. An initial HRES consisting of a photovoltaic (PV) array and wind generator is used to power an agricultural area of 4 ha once with a central unit and then with four distributed units. The selection of the optimal size is accomplished through linear programming based on the simplex algorithm to minimize the total life cycle cost. The results show for the first time that the use of a distributed renewable energy system containing a full PV array is cost-effective compared to a central unit.     

In situ electrocatalytic oxidation of acid violet 12 dye effluent

Electrochemical treatment of organic pollutants is a promising treatment technique for substances which are recalcitrant to biodegradation. Experiments were carried out to treat acid violet 12 dye house effluent using electrochemical technique for removal color and COD reduction covering wide range in operating conditions. Ruthenium/lead/tin oxide coated titanium and stainless steel were used as anode and cathode, respectively. The influence of effluent initial concentration, pH, supporting electrolyte and the electrode material on rate of degradation has been critically examined. The results indicate that the electrochemical method can be used to treat dye house effluents.     

Optimal Allocation of Multichannel Measurement Devices for Distribution State Estimation

This paper proposes an optimization algorithm that is suitable for choosing the optimal number and position of the measurement devices in distribution state estimation (DSE) procedures used in modern electric distribution networks. The algorithm is based on the techniques of dynamic programming, and its goal is to guarantee both the minimum cost and the accuracy required for the measured data needed to operate management and control issues, such as energy dispatch and protection coordination. Both the uncertainty introduced by the measurement devices and the tolerance in the knowledge of the network parameters (line impedances) are taken into account in the proposed approach. The aggregation of the quantities to be measured in a few measurement points has been favored to reduce the overall cost of the measurement system. Random changes in the loads are considered to establish adequate reference conditions for the tests. Tests relevant to real distribution networks are presented to show the validity of the proposed approach. The results emphasize how both the influence of the tolerance on the network parameters and the cost of the measurement system can dramatically be minimized by suitably choosing the algorithm to be implemented to solve the DSE problem.      

Generalized Timoshenko modelling of composite beam structures: sensitivity analysis and optimal design

This article describes a new approach to design the cross-section layer orientations of composite laminated beam structures. The beams are modelled with realistic cross-sectional geometry and material properties instead of a simplified model. The VABS (the variational asymptotic beam section analysis) methodology is used to compute the cross-sectional model for a generalized Timoshenko model, which was embedded in the finite element solver FEAP. Optimal design is performed with respect to the layers’ orientation. The design sensitivity analysis is analytically formulated and implemented. The direct differentiation method is used to evaluate the response sensitivities with respect to the design variables. Thus, the design sensitivities of the Timoshenko stiffness computed by VABS methodology are imbedded into the modified VABS program and linked to the beam finite element solver. The modified method of feasible directions and sequential quadratic programming algorithms are used to seek the optimal continuous solution of a set of numerical examples. The buckling load associated with the twist–bend instability of cantilever composite beams, which may have several cross-section geometries, is improved in the optimization procedure.