Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.     

Optimal design of urban drainage networks

In this paper, a general procedure for the optimal design of urban drainage networks is proposed taking into account realistic technical constraints and the management cost of the automatic flushing devices. The procedure, which is based on the utilisation of a standard genetic algorithm, is first tested with reference to a urban drainage network taken from the literature, while a second test case is considered in order to evaluate the impact of different input parameters and constraints on the optimisation problem. In particular, we evaluate: (i) the effect of considering the crown elevation of the network outlet not fixed a priori but variable in a given range; (ii) the effect of imposing that the size of the conduit downstream a node should be not smaller than the sizes of the conduits upstream; and (iii) the effect of modifying input parameters such as the peak wastewater discharge or the management unit costs.     

Post-Pareto optimality approach to enhance budget allocation process for bridge rehabilitation management

A variety of conflicting criteria in the form of objective functions exist in budget allocation optimisation problem for bridge rehabilitation projects. Budget allocation decision-making for such transportation assets is generally a combinatorial problem. The nature of the problem is a good reason for decision-makers to apply multi-objective optimisation techniques. However, manually choosing an acceptable solution from a set of optimal solutions is a time-consuming task, which would be avoided if the optimisation technique could be followed by a ranking method to obtain unique acceptable solution. To enhance the budget allocation process, this paper develops a posteriori approach to prioritise Pareto-Optimal (PO) solutions generated by genetic algorithm in order to identify a unique package of bridge rehabilitation activities. By identifying the most conventional objective functions for bridge rehabilitation based on technical and managerial criteria, a multi-objective knapsack problem is constructed. PO solutions will then be prioritised applying ‘Technique for Order Preference by Similarity to an Ideal Solution’. The feasibility of the study will be finally demonstrated through an illustrative example. The proposed ranking approach may facilitate the budget allocation optimisation process for bridge rehabilitation where one or a few acceptable solutions are demanded.     

盾构法施工引起邻近地下管线位移分析

采用三维有限元方法,考虑土体、地下管线、盾壳以及衬砌的相互作用,用ANSYS程序软件模拟盾构推进对邻近地下管线位移的影响,探讨盾构刀盘推进力、盾构开挖面到地下管线不同距离、注浆程度等因素对地下管线位移的影响。     

Optimisation of a combined Stirling cycle–organic Rankine cycle using a genetic algorithm

In a Stirling cycle a huge amount of energy is wasted due to the losses. This wasted energy may be utilised as a heat source for the boiler of an organic Rankine cycle (ORC). Combination of these two cycles leads to an increased cycle efficiency compared to a single Stirling cycle and the analysis and optimisation of the integrated system is carried out. Optimisation is performed using the genetic algorithm and considering three decision variables: the temperature of the cold tank of the Stirling cycle, the pressure ratio and the temperature of the ORC condenser. In optimisation, the efficiency is considered as the objective function and the highest value is achieved by adjusting the decision variables. Using this method, the efficiency of the overall combined cycle was improved in which the highest efficiency was obtained to be 41.5%.     

Optimisation of solar and wind energy systems: a survey

In the last decade, the penetration of renewable energy sources (RESs) for energy generation has increased. Because of their several technical and environmental benefits, solar and wind energy systems are the major RESs that are being increasingly employed for different aims. In solar and wind energy systems, there are a variety of optimisation problems which are non-linear and non-convex in nature. Study of the literature shows that among the meta-heuristic techniques, genetic algorithm (GA) and particle swarm optimisation (PSO) are highly used to solve different optimisation problems of solar and wind energy systems. The main goal of this paper is to review different applications of GA and PSO in solar and wind energy systems. This review will enable the researchers to study the present status of different solar and wind optimisation problems and plan for future investigations.     

遗传算法与自适应粒子群算法耦合的大坝安全预警评价模型

改变应用最小二乘法求解大坝统计预警模型的传统方式,利用粒子群算法随机搜索的优化能力确定统计模型的回归系数。针对粒子群算法收敛速度较慢等问题,提出一种新的自适应策略,能够依据粒子个体和种群的优化信息,调整学习因子,并将该策略与遗传算法的交叉、变异算子相结合。通过工程算例表明,该方法具备较好的搜索多样解能力,自适应地调整粒子飞行的步长,提高了粒子群算法的收敛速度;基于该方法的大坝预警评价模型与最小二乘法、基本粒子群算法相比,数据挖掘能力强,预警评价结果与大坝的实际运行状态更加吻合,有效地提高了统计模型的预测精度。     

Optimum design of steel frames using a multiple-deme GA with improved reproduction operators

In this paper, the performance of the genetic algorithm is improved by introducing some new crossover and mutation operators. The new operators are incorporated into a multiple-deme genetic algorithm in which population is divided into subpopulations and communication between different demes is established through migration of individuals, enhancing diversity and resulting in better solutions. This algorithm is applied to the minimum weight design of steel frames subjected to actual strength and ductility constraints of AISC-ASD specifications as well as other serviceability and constructability constraints. The efficiency of the proposed method is demonstrated through optimising two benchmark problems including a three-bay, three-storey steel frame and a five-bay, 22-storey special steel frame. Significant improvements in the optimum solutions are obtained with reduced number of finite element analyses, resulting in less computational effort.     

Enhancing and multi-objective optimising of the performance of Stirling engine using third-order thermodynamic analysis

Stirling engine is an external combustion engine which uses eternal heat sources like solar radiation for heating a compressible fluid inside cylinders. In the recent years, significant attention is drawn to Stirling engines due to the clear advantages, high efficiency potential, flexible fuel, lower nitrogen oxides, quiet and minimal vibration, high reliability and highest specific output work for any closed regenerative cycle. The third order thermal analysis is one of the analyses which has been applied in several studies which have been carried out on Stirling engines. NSGA-II algorithm is applied to optimise the differential regenerator pressure (bar) and the power output (kW) for a Stirling engine system. In this study, three decision-making techniques are utilised to optimise the solutions, obtained of the results. At last, the employed techniques are compared with the data of an experimental research work.     

引入新算子采用隔代映射遗传算法对钢框架进行优化设计

通过引入新的交叉和变异算子,改进遗传算法。隔代映射遗传算法将种群分为多个子群,通过个体迁移、加强多元性及求较优解确定不同种群之间的联系。将新算子引入隔代映射遗传算法中,并采用此算法,按AISC-ASD规范规定,对钢框架进行最小重量优化设计。通过对某3跨3层钢框架和5跨22层特殊钢框架的优化设计,验证了本方法的有效性。对优化求解方法做了重要改进,减少了有限元分析计算量。     

A multi-objective approach for detecting and responding to accidental and intentional contamination events in water distribution systems

The protection against contamination events in water distribution systems involves two distinct phases: detection of the presence of a contaminant and implementation of actions to isolate and/or expel it rapidly. The problem of detection is confronted by installing a series of monitoring stations, strategically placed across the distribution system and consisting of sensors to detect the presence of contaminants. The actions to be implemented may include operations on distribution system devices (valves and hydrants) or injection of reagents to eliminate the contaminant, or simply alert users. The procedure proposed here attempts to address the problems related to the two phases by means of two consecutive optimisation processes, both of them performed off-line and assuming a specific 24-hour water demand sequence in each network node, whereas the accidental/intentional injection of contaminant can occur in any node and at any hour of the day. With reference to this vast range of possible injection scenarios, the first multi-objective optimisation process defines the position of a pre-selected number n_s of sensors across the distribution system in order to minimise the expected percentage of undetected contamination events and the expected volume of contaminated water consumed up to the beginning of the response operations following detection. A single configuration of stations is then selected from the Pareto front produced by this optimisation process (‘knee point’ of the Pareto front). At the end of this first optimisation process and with reference to the selected set of sensors, a potentially contaminated area in the network is associated to each sensor for each sub-period of the day. The second multi-objective optimisation process is then aimed to identify, with reference to each station and sub-period, and thus inside the corresponding potentially contaminated area, the hydrant-opening and valve-closing operations to be carried out in order to minimise both the number of operations and the expected volume of contaminated water consumed between the beginning of the response operations and the disappearance of the contaminant, assuming the availability of an unlimited number of response teams. Once these devices have been identified (‘knee point’ of the Pareto front relevant to the second optimisation process), an a posteriori analysis is performed to determine the sequence in which they should be activated based on the number of response teams actually available. In these optimisation processes, a hydraulic and quality simulator (EPANET) is linked to a multi-objective genetic algorithm (NSGA-II) in order to compute the value of the objective functions of the problem across different contamination scenarios. The results obtained applying the procedure to a real and complex water distribution system have shown it to be a robust and effective method for reducing the impact on the population.     

Optimal design of buried pipelines considering failure by mechanical damage

Mechanical damage by external interference can represent a serious threat to the structural integrity of onshore pipelines. International databases indicate that, nowadays, this failure mode is the main cause of spillages in pipelines. The present paper addresses the optimal design of buried pipelines considering failure by mechanical damage. Historical incident data relating failure rates with problem parameters are used to guide the optimisation process. The design objective is to minimise total costs over the lifecycle, which include costs of construction, inspection, and expected costs of failure and repair. The optimal wall thickness, depth of cover and surveillance interval are considered as design variables. Results of numerical examples are presented and discussed. Results show that operators should favour deeper depths-of-cover. In some cases, it is shown that the higher initial costs associated to larger wall thicknesses are compensated by reduction in expected costs of failure. Results also show that, for higher costs of failure, the optimal surveillance interval is significantly smaller than the commonly-practiced 15 days.     

A TIME-COST TRADE-OFF MODEL WITH RESOURCE CONSIDERATION USING GENETIC ALGORITHM

A genetic algorithm (GA)-based model to deal with time-cost trade-off problems is presented. The traditional algorithms assume the unlimited availability of resources. Instead, the proposed model allows for resource constraints. Accordingly, the trade-off is considered in terms of the level of resources to be deployed for each activity. At the same time the model schedules the starts of the activities in order to optimize the objective function. The activity starts is a significant factor in the case of non-uniform available profile. The GA searches both spaces of resource utilization and activity starts to determine the optimal schedule that conforms to the resource available profile. Although, in principle, this has the potential of a combinatorial explosion, earlier work suggests that the GA-based model can be applied to larger networks without appearing to suffer from this problem. Allowing the project manager to think in terms of resource utilization makes this approach consistent with resource allocation problems, and more pragmatic and appealing. The model can also be used to solve conventional time-cost trade-off problems by a simple modification of the objective function. Numerical examples are used to illustrate the working of the model. The examples include a case of varying resource available profile.     

Multi-objective optimisation of bridge retrofit and post-event repair selection to enhance sustainability

The decaying state of our infrastructure paired with rising emphasis on sustainable engineering suggests the need for upgrades which are selected based on their ability to provide adequate performance under natural hazards and minimise negative impacts on the three pillars of sustainability: society, environment and economy. This paper poses a framework based on a multi-objective genetic algorithm to help identify optimal retrofit and repair combinations which ensure public safety while minimising lifetime environmental, economic and social performance measures of sustainability for infrastructure exposed to natural hazards. Assessment of the case-study application results reveals the relationship between life-cycle environmental, economic and social indicators of sustainability for a bridge subject to earthquakes. The framework is anticipated to help guide the selection of retrofit and repair combinations by providing a set of ‘near-optimal’ non-dominated solutions, which enhance sustainability while ensuring public safety and mitigating or repairing damage from natural disasters.     

混合遗传算法在工程结构优化设计中的应用

根据工程实际 ,充分考虑规范规定的约束条件和各项技术标准要求 ,建立了建筑结构优化模型。并提出一种离散变量结构优化设计的斐波那契算法与标准遗传算法结合成混合遗传算法。优化设计结果表明 ,这种混合遗传算法既发挥了斐波那契算法省时、高效、局部搜索能力强的特点 ,又发挥了遗传算法全局性好的特点 ,是兼二者之长 ,弃二者之短的高效的理想的工程结构优化设计方法。     

基于混合遗传算法的结构优化设计

针对遗传算法在迭代过程中经常出现早熟、收敛速度慢、局部搜索能力差等缺点,对遗传算法中的遗传算子进行了改进,提出梯度算法与遗传算法相结合的混合遗传算法。分析表明,这种混合遗传算法既发挥了梯度算法局部搜索能力强的特点,又结合了遗传算法全局搜索能力强的优点,避免出现早熟现象,使收敛速度大大改善,具体算例表明该混合遗传算法是一种有效的工程结构优化方法。     

层状地层粘弹性优化反分析与混合遗传算法

本文针对基坑开挖过程所表现出来的时间效应 ,采用Voigt粘弹性模型进行模拟 ,并结合具体的施工过程 ,进行粘弹性位移优化反分析。由于遗传算法具有早熟收敛 ,并且在进化后期有搜索能力较低等特点 ,把阻尼最小二乘法和遗传算法结合起来 ;并结合具体的工程实例 ,对遗传算法以及混合遗传算法进行了对比分析。结果发现 ,混合遗传算法在保证搜索精度的基础上加快了搜索速度。     

Reducing CO2 emission from exhaust gases using molten carbonate fuel cells: a new approach

The aim of this study is to couple molten carbonate fuel cell (MCFC) stack with integrated gasification combined cycle fed by refinery residues, to remove CO₂ from gas turbine exhaust gases that have CO₂ emission rate of 14,200 ton/year. By applying multi-objective optimisation (MOO) using genetic algorithm, the optimal values of operating load and the corresponding values of objective functions are obtained. The MOO of the MCFC system regarding two scenarios is performed. The first scenario is minimisation of cost of electricity (COE) and CO₂ emission rate. Objective functions of the second scenario are the same as in the first scenario while CO₂ tax is taken into account. Results show that the second scenario has 29.5% lower average optimal COE and 2.5% lower average emission rate in comparison with the first scenario. A sensitivity analysis is also performed to study the effect of fuel price and CO₂ tax variations on optimal solutions.     

A simulation optimisation tool for planning of low-income housing projects

Construction of low-income housing projects is a recurring process and is associated with uncertainties that arise from the unavailability of resources. This paper presents a case study that discusses how computer simulation and optimisation are used to aid government agencies and/or contractors in planning of such projects. It illustrates the optimisation of project objectives, taking into consideration the interaction amongst involved resources. As such, total duration and the associated total costs, including direct and indirect costs, can be estimated and optimised. One Youth Habitation project that is executed in 6th of October City in Egypt is analysed in a step-by-step procedure to demonstrate the capability of proposed computer simulation and optimisation prototype (named LIHouse{_}Sim) in the modelling construction of low-income housing projects using bearing block walls with hollow core technique. The presented tool proves its practicality to contractors in estimating the time and costs of the recurring process of low-income housing construction, considering complex interdependencies between construction resources and the uncertainties associated with construction activities. The LIHouse{_}Sim prototype is used to perform a wide analysis for the alternative of the effective optimisation criteria in the bearing block walls/hollow core technique and for the genetic algorithm optimisation approach elements.     

Improving the productivity of drainage operations activities through schedule optimisation

Operational maintenance of a wastewater collection system involves various activities such as visual inspection, low-pressure flushing, high-pressure flushing, catch basin cleaning, CCTV inspection, and hydromechanized cleaning. These activities are performed at regular intervals at prescheduled locations across the city, which results in a large amount of travel time for the crews performing these activities. Moreover, the stochastic nature of the on-site activity durations leads to unused time at the end of work shifts. A review of the literature and current industry practice reveals the need for schedule optimisation models that specifically address these issues. This research thus formulates the scheduling of drainage operations activities as a special class of combinatorial optimisation problem, and proposes an efficient algorithm to reduce the aforementioned travel and unused times. The model is applied to a monthly high pressure flushing schedule at the City of Edmonton, Canada, showing significant improvement in productivity.