The efficiency of soft sensors modelling in advanced control systems in oil refinery through the application of hybrid intelligent data mining techniques

Quality variables cannot be automatically measured to all nor be measured at a high cost, infrequently, nor with high delays, such as laboratory analysis and online analyser. Therefore, data-driven soft sensors are inferential models which use online available sensors, such as temperature, pressure, and flow rate among others, to predict the quality variables. Soft sensors which are built using historical data of the processes are normally developed from the supervisory control and data acquisition (SCADA) systems connected with PLC or DCS (distribution control systems) as the daily reports on the oil refinery processes. These systems are then obtained from laboratory observation/measurements. Notably, the main issue in the development of the soft sensor is the treatment of missing data, outlier detection, selection of input variables, model training, validation, and soft sensor maintenance to adopt the heavy-duty oil refineries to improve the products of the crude oil and increase yield. In this article, the improvement in the virtual sensor based on hybrid soft computing methods (FLS and NN), which are combined into ANFIS, will be employed to construct the soft sensor model. Moreover, RST will be used to reduce the fuzzy rules and discretisation method to optimise and deal with the large continuous data. It was found from the implementation of rough set theory and discretisation methods that these two methods solved the complexity and nonlinearity of the soft sensor model. This model was employed for the refining process measurements data of the oil refinery from two different crude oil sources, in which the database of the measurements and processes was combined to improve the quality of data and discover the knowledge stored in the data pattern. It was indicated from this study result that the ANFIS model is able to manage the complex data to predict two important parameters of light naphtha (API and RVP) compared to the simple regression model. Additionally, controlling and monitoring the process are crucial actions performed to achieve the 4th industrial revolution and IoT. This study has contributed to the assistance in breaking the barriers of privacy between oil industries and the applicability of soft sensors modelling in the changes of data sources to achieve remarkable data analysis. The analyses result of RVP show the efficiency of ANFIS compare with linear regression regarding the generalization and overfitting.

[1]  Geert Gins,et al.  Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis , 2017 .

[2]  António E. Ruano,et al.  Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network , 2016, Expert Syst. Appl..

[3]  Iman Morsi,et al.  SCADA system for oil refinery control , 2014 .

[4]  Nenad Bolf,et al.  Soft sensor for continuous product quality estimation (in crude distillation unit) , 2011 .

[5]  Teh Ying Wah,et al.  Data mining and data gathering in a refinery , 2010 .

[6]  Luigi Fortuna,et al.  Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets , 2009, IEEE Transactions on Instrumentation and Measurement.

[7]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[8]  Bogdan Gabrys,et al.  Soft sensors: Where are we and what are the current and future challenges? , 2009, ICONS.

[9]  Gabor Nagy,et al.  Integrated Process and Control System Model for Product Quality Control - Application to a Polypropylene Plant , 2008 .

[10]  Plamen Angelov,et al.  Soft sensor for predicting crude oil distillation side streams using Takagi Sugeno evolving fuzzy models , 2007 .

[11]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[12]  Xiaogang Wang,et al.  Soft Sensing Modeling Based on Stacked Least Square-Support Vector Machine and Its Application , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[13]  Armen Zakarian,et al.  Data mining algorithm for manufacturing process control , 2006 .

[14]  Hui Shao,et al.  Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms , 2006, Soft Comput..

[15]  Marcel Rijckaert,et al.  Application of feedforward neural networks for soft sensors in the sugar industry , 2002, VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings..