Abstract
The acidic compounds such as Mercaptans, H2S and COS are commonly present in the liquid LPG streams in the south Pars gas processing plant. Sulfur contaminants not only lead to odor problems but can form objectionable oxides on combustion and cause environmental pollution. In present study, Support Vector Machine (SVM) is employed to develop an intelligent model to predict the sulfur content of propane and butane products of Liquefied Petroleum Gas (LPG) treatment unit of south Pars gas processing plant of Assaluyeh/Iran. A set of seven input/output plant data each consisting of 365 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 70%, 15%, and 15% of available data respectively. Test results from the SVM developed model showed good compliance with operating plant data. Squared correlation coefficients for developed models are 0.97 and 0.99 for propane and butane sulfur content, respectively. According to the results of the present case study, SVM could be regarded as a reliable accurate approach for modeling the sulfur content of LPG treatment unit of a natural gas processing plant.
Highlights
Liquefied Petroleum Gas (LPG) referred to predominately propane or butane, either separately or in mixtures, which is maintained in a liquid state under specific pressure/temperature within the confining vessel (Santos et al, 2016)
Sulfur contaminants lead to odor problems but can form objectionable oxides on combustion and cause environmental pollution (Safadoost et al, 2014; Mahdipoor and Ashkezari, 2016)
A data set of seven series of input/output data is collected from the LPG treatment unit
Summary
Liquefied Petroleum Gas (LPG) referred to predominately propane or butane, either separately or in mixtures, which is maintained in a liquid state under specific pressure/temperature within the confining vessel (Santos et al, 2016). The main advantage of such models over existing approaches is the capability of learning and generalizing data, fault tolerance and inherent contextual information processing in addition to fast computation potential (Raynal et al, 2016) Such characteristics make them perfect candidates for applications where the complexity of the data or task demands high computational costs (Haghbakhsh et al, 2012; Adib et al, 2013, 2015; Moradi et al, 2016). Since the purpose of the process is to reduce the sulfur content of propane and butane product of LPG treatment unit of south Pars gas processing plant of Assaluyeh, the input parameters are amine, caustic and feed flowrate of this unit and the output variables are total sulfur of propane and butane of this unit. The models are compared to actual plant data and with each other and the accuracy of the models is assessed through calculation of Average Absolute Deviation Percent (ADD%)
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