Dynamic two phase modelling and anfis-based control of ethylene copolymerization in catalytic Fluidized Bed Reactor / Mohammad Reza Abbasi

Mohammad Reza , Abbasi (2019) Dynamic two phase modelling and anfis-based control of ethylene copolymerization in catalytic Fluidized Bed Reactor / Mohammad Reza Abbasi. PhD thesis, Universiti Malaya.

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Abstract

The worldwide demand for polyolefins has reached more than 140 million tons per year and low-pressure processes using different catalytic systems are utilized to produce more than 80 wt% of the total production. Polyethylene (PE) is the most extensively consumed plastic in the world and gas-phase processes are widely used for its production due to their flexibility. The sole type of reactor that can produce PE in gas phase is Fluidized Bed Reactor (FBR). This is due to their ability to dissipate heat of reaction in quantities that make reliable production rates possible. Given the scale of production of polyolefins produced via catalytic polymerization, the development of reliable process models to simulate the behavior of the related process units in general and the commercial reactor units in particular is becoming more and more important. In this study, a modified dynamic model for ethylene co-polymerization in an industrial fluidized-bed reactor (FBR) is developed to describe its behavior and calculate the polymer properties. The model considers particle entrainment and polymerization reaction in two phases. Two-site kinetics and hydrodynamics in combination, provide a comprehensive model for the gas phase fluidized-bed polyethylene production reactor. The governing moment and mass and energy balance differential equations have been solved simultaneously and the results were compared with literature as well as industrial data. Nonetheless, since the model is dynamic, it can be used in control studies as well and it was utilized as a base to control two of the most important process variables namely polymer Melt Flow Index (MFI) and the temperature. The results showed that the dynamic model predicts more accurate results for Polydispersity Index (PDI), Molecular Weight Distribution (MWD), reactor temperature and polymer production rate. The open loop simulation analysis revealed that the behavior of the polyethylene fluidized bed reactor is strongly dependent on the superficial gas velocity and feed concentrations and that the process is highly sensitive and nonlinear, thus justifying the use of an advanced control algorithm for efficient control of the process variables. To test the model and control the polymer MFI and temperature, inlet hydrogen concentration and cool water flow rate have been manipulated respectively. Firstly, conventional Proportional-Integral-Differential (PID) controller was used to control the variables in both servo and regulatory scenarios. The results confirm the disadvantages of the conventional controllers for applications to this nonlinear system. Intelligent and expert system-based controllers have proven to have the capability to control these systems. As a result, Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in this study. The findings have shown the superiority of this Artificial Intelligence (AI) based controller in set-point tracking and disturbance rejection profiles; solely or in hybrid architectures with conventional controllers. This process model can equip the user-engineer with the essential tools required for the process design, optimization, and control which could ultimately lead to significant savings in time and cost during the process development and operation.

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