Purpose
As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform.
Design/methodology/approach
First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced.
Findings
Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO.
Originality/value
The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.
This study proposes a new selection method called trisection population for genetic algorithm selection operations. In this new algorithm, the highest fitness of 2N/3 parent individuals is genetically manipulated to reproduce offspring. This selection method ensures a high rate of effective population evolution and overcomes the tendency of population to fall into local optimal solutions. Rastrigin’s test function was selected to verify the superiority of the method. Based on characteristics of arc tangent function, a genetic algorithm crossover and mutation probability adaptive methods were proposed. This allows individuals close to the average fitness to be operated with a greater probability of crossover and mutation, while individuals close to the maximum fitness are not easily destroyed. This study also analyzed the equipment layout constraints and objective functions of deep-water semisubmersible drilling platforms. The improved genetic algorithm was used to solve the layout plan. Optimization results demonstrate the effectiveness of the improved algorithm and the fit of layout plans.
A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization (IPSO-SVM). EMD is a highly adaptive decomposition and can decompose any complicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but also can reduce the dimension of the model input data space. we can extracts IMF energy feature as the input feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters. A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.
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