Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

Abstract

BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase-based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale-up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The resultsshow that our previously developed two-phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro-fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS-proportional-integral-differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers.

Modeling and Flowsheet Simulation of Vibrated Fluidized Bed Dryers

“…Davidson and Harrison's two-phase model [13] was further improved and refined by Hilligardt and Werther [15], who introduced semi-empirical correlations to describe the hydrodynamics of fluidized beds of Geldart A, B and D particles. Their model has been successfully applied in the modeling of several fluidized bed processes [16][17][18][19] and has been confirmed to be accurate [20][21][22][23]. Recently, the Hilligardt and Werther model was expanded for modeling the hydrodynamic of cohesive Geldart A and C particles with and without vibration of the fluidized bed [6].…”

A Non-Convex Robust Simulation Optimization Model for Inventory Management Problem by System Dynamics

“…Abbasi et al [50] have modelled the hybrid intelligent control in fluidized bed reactors by dynamic process modelling. Their work presents an excellent application of mathematical dynamic modelling which can be presented by simulation as well.…”

Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm

“…ANFIS comprises five different consecutive layers: the first layer is the if-part (fuzzification); the second layer is the rules (production) part; the third layer is norm part; the fourth layer is the then-part (defuzzification); and the fifth layer is the output part [34]. The main ANFIS structure with two inputs (i.e., x and y) as independent variables and one output (i.e., f out ) as the dependent variable is illustrated by Figure 1.…”

“…The structures of enhanced mutation and crossover in BSA are extremely different, with the mutation and crossover operators specified in former optimization algorithms, such as differential evolution (DE) and GA. Those two enhanced operators are applied in this algorithm to provide a balance between exploitation of optimal result and exploration of the optimization search space. BSA has efficient exploitation and exploration capabilities because of several effective mechanisms defined in this optimization algorithm to generate a trial population, control the boundaries of optimization search space and adapt the magnitude of the search direction [34].…”