Precipitation and deposition of asphaltene cause number of severe problems in downstream and upstream of oil industry. Hence, it is essential to present a dependable model for quantitative estimation of asphaltene precipitation. This paper aims to introduce a hybrid support vector regression (SVR) with harmony search (HS) as an intelligence approach to create quantitative formulation between amount of asphaltene precipitation and titration data. Harmony search is combined with SVR for determining the optimal value of its user-defined parameters. The optimization implementation by HS significantly improves the generalization capability of SVR. A dataset that includes 176 data points was employed in the current study, while 141 data points were utilized for constructing the model and the remainder data points (35 data points) were used for assessment of degree of accuracy and robustness. Evaluating the performance of constructed model based on statistical criteria indicates that the hybrid model has acceptable accuracy to estimate the amount of asphaltene precipitation from titration data. This study concludes that optimization of SVR with HS produces a smart hybrid model, which is an appropriate solution for modeling of the asphaltene precipitation.

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One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (a), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

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