Input–Output Linearizing Control Strategy for an Ethylene Dichloride Cracking Furnace Using a Coupled PDE-ODE Model

An input–output (I/O) linearizing control scheme for a gas-fired thermal cracking furnace is developed for a tubular reactor coil, which is a type of tubular reactor surrounded by gas-fired burners in the furnace. Due to the simultaneous interaction between the spatially distributed dynamics of the reactor coil and the lumped radiating wall, the typical proportional-integral-derivative control widely used in industry may have insufficient performance to handle the complexity. In this work, a feedback I/O linearizing controller is applied to control a cracking furnace system that is described by a coupled PDE-ODE model: ethylene dichloride cracking. The cracked gas temperature is manipulated through the fuel gas flow to achieve the desired trajectories. Control performances of the developed controller are illustrated through simulation results for servo and regulatory problems. The proposed method provides more robustness to handle control problems without offset

Control of Ethylene Dichloride Cracking Furnace Using an Analytical Model Predictive Control Strategy for a Coupled Partial Differential Equation/Ordinary Differential Equation System

A nonlinear optimization-based control system with analytical model predictive control (AMPC) structure is formulated in cascade with an off-line pseudo-steady-state calculator for an ethylene dichloride (EDC) cracking furnace process described by a coupled partial differential equation/ordinary differential equation model. The objective of the proposed control system is to control the EDC cracking rate at the desired set points by manipulating the fuel gas flow rate with constraints to avoid extensive coke formation. To handle the complex behaviors that are affected by radiating walls interacting with spatial dynamics of the reactor coil, the set point calculator is employed to provide an optimal target for the constrained optimization-based controller in calculating the control actions. Simulation results show that the proposed control system is successful to regulate the controlled output at the desired set points. Control performance tests with servo and regulatory problems demonstrate that the developed control system is capable of providing excellent responses to achieve the desired set point and reject process disturbance

Optimization-Based Control Strategy with Wavelet Network Input–Output Linearizing Constraint for an Ill-Conditioned High-Purity Distillation Column

A new nonlinear optimization control strategy is developed for multivariable control of an ill-conditioned, high-purity distillation column. A high-gain directional effect resulting from the ill-conditioned nature of the system causes difficulty in controllability and requires a higher performance control system. The developed optimal controller applies a minimization of energy consumption as the optimal objective function to treat the ill-conditioning effect, while wavelet neural network input/output linearizing constraints force the outputs to reach the desired set points. In this paper, ethylene dichloride purification is used as a case study. The process dynamics are evaluated based on relevant thermodynamic properties in Aspen Plus Dynamics and are controlled by the proposed controller in the MATLAB/Simulink platform. Control performances are investigated in this cosimulation environment for set point tracking and regulatory problems. The simulation results demonstrate that robust tracking is attained, while compensation of the input disturbances is effectively improved compared with a model predictive controller