The error prediction of inventory reconciliation during storage and transportation process based on PLS and MFOA-LSSVM

Abstract

Process optimization and fault diagnosis technology, which is represented by production process monitoring, design and production condition adjustment, play an important role in the modern petroleum industry. Accurate inventory reconciliation model is the basis of process optimization and fault diagnosis. To eliminate the impact of the inventory reconciliation error caused by different metering systems, the error prediction method of inventory reconciliation during storage and transportation process based on partial least squares (PLS) and least squares support vector machine optimized by modified fruit fly optimization algorithm (MFOA-LSSVM) is proposed. The general error prediction method flow of inventory reconciliation is provided. The principles of PLS and MFOA-LSSVM are elaborated in detail. Firstly, the algorithm of PLS is used to exclude the interference of unrelated factors and extract the most relevant factors that influence the error of inventory reconciliation. Then the modified three-dimensional fruit fly optimization algorithm with diminishing steps as well as a good global search capability is adopted to select the LSSVM model parameters and build the error prediction model. Finally, the sample data were revised by using the predictive value to verify the validity of the proposed method. The experimental modeling was carried out by PLS and MFOA-LSSVM. Compared with other forecasting methods, this method not only has the advantage of faster calculations, but also can well predict the error of reconciliation.