MODELING OF A NEURO-LOGIC SOLVER FOR IMPROVED RISK REDUCTION FACTOR IN PETRO-CHEMICAL INDUSTRY

Subject Area: CONTROL SYSTEM ENGINEERING

This paper presents improving the reliability of critical safety instrument system using mathematical method and machine learning technique. The aim of the research is to improve the reliability of critical safety instrument system using machine learning technique and the main objective to develop a neuro logic solver and polynomial estimation model which monitoring the behaviour and distillation plant and control against system failure. To address this problem, methods such as risk assessment test, data collection, neurologic solver algorithm and error estimation algorithm and guided by the International Electrochemical Commission (IEC) 61508 and 61511 methodologies for the design and implementation of Safety Instrument System (SIS). The risk analysis was done using inductive and deductive techniques which employed both fault tree analysis and self-defining equations to determine the probability of failure on demand (PFD) of the SIS components. The neurologic solver algorithm was developed using artificial neural network, tansigactivation function and gradient descent back-propagation algorithm, while the error estimation algorithm was developed with recursive polynomial functions. These algorithms were implemented with Simulink, evaluated and cross validated considering Mean Square Error (MSE), regression, PFD, Risk Reduction Factor (RRF) and Safety Integrity Level (SIL). The result of the neurologic solver MSE is 2.98E-09, Regression is 0.9978 and PFD is 9.00E-04.