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Improvement of multicomponent batch reactive distillation under parameter uncertainty by inferential state with model predictive control
yesBatch reactive distillation is aimed at achieving a
high purity product, therefore, there is a great deal to find an
optimal operating condition and effective control strategy to
obtain maximum of the high purity product. An off-line
dynamic optimization is first performed with an objective
function to provide optimal product composition for the batch
reactive distillation: maximum productivity. An inferential
state estimator (an extended Kalman filter, EKF) based on
simplified mathematical models and on-line temperature
measurements, is incorporated to estimate the compositions in
the reflux drum and the reboiler. Model Predictive Control
(MPC) has been implemented to provide tracking of the
desired product compositions subject to simplified model
equations. Simulation results demonstrate that the inferential
state estimation can provide good estimates of compositions.
Therefore, the control performance of the MPC with the
inferential state is better than that of PID. In addition, in the
presence of unknown/uncertain parameters (forward reaction
rate constant), the estimator is still able to provide accurate
concentrations. As a result, the MPC with the inferential state
is still robust and applicable in real plants
Implementation Of Internal Model Control (IMC) In Continuous Distillation Column.
Distillation columns have been widely used in chemical plants for separation process. The high nonlinearity and dynamic behavior of the column make them hard to control
A Review on AI Control of Reactive Distillation for Various Applications
In this chapter, previous studies on reactive distillation process control including control using conventional as well as soft sensor control, membrane assisted reactive distillation design and simulation, estimation and control are discussed. The review of literature in different dimensions is carried out to explore the opportunities in the field of research work. The chapter is focused on dynamics and control of Reactive distillation, its control using Conventional Techniques, Model Predictive Control MPC), Reactive Distillation using Soft Sensors/Soft Controllers, Membrane assisted reactive distillation, Biodiesel in Reactive Divided Wall Column: Design and Control and Membrane reactive divided wall column. These control techniques are proposed and analyzed by many researchers. These techniques have potential use in process industries to have better soft sensor control of nonlinear processes
Methodologies for the optimisation, control and consideration of uncertainty of reactive distillation
The work presented in this thesis is motivated by the current obstacles hindering the implementation of reactive distillation in industry, mainly related to the complexities of its design and control, as well as the impact of uncertainties thereupon. This work presents a rigorous methodology for the optimal design and control under uncertainty of reactive distillation. The methodology can also be used to identify and investigate mitigation strategies for process failures arising due to design and/or operation deficiencies under changed processing conditions, based on the evaluation of different design and/or control alternatives. The first step of the methodology is the simultaneous (MINLP) optimisation of the design and operation of a reactive distillation process superstructure, used to explore the possible steady-state design alternatives available, including ancillary equipment such as pre- and side-reactors, side-strippers and additional distillation columns, based on product-related constraints and a detailed objective cost function. The next step is the investigation of the dynamic control performance of this optimal system, where conventional and advanced process control strategies are considered in order to investigate how robust the system is towards operational disturbances, or whether revising the optimal steady-state design is required. As the optimisation depends heavily on accurate data for reaction kinetics and separation performance, the final step of the methodology is the evaluation of the impact of parameter uncertainty on the performance of the optimal controlled system, including redesigning the controlled system if required. The methodology is demonstrated using a number of industrially relevant case studies with different reaction and separation characteristics in order to investigate how these determine the design and control of an economically attractive and rigorous reactive distillation process. It is demonstrated that the process characteristics have a significant impact on the design of the system, and that auxiliary equipment may be required to meet production specifications and/or to ensure robust controlled behaviour. It is also shown that, under parameter uncertainty, an optimal controlled system may nevertheless face performance issues, and revising the design and/or operation of the process may be required in order to mitigate such situations
MODEL-BASED CONTROL DEVELOPMENT FOR BINARY PILOT PLANT DISTILLATION COLUMN
The increasingly popular Model Predictive Control (MPC) strategy has been used in many process units either to improve the performance, save utility costs, or create a robust process able to cater to multiple variables. This project focuses on the development of model-based control for a distillation column in the Process Control laboratory at Universiti Teknologi PETRONAS (UTP) separating an ethanol-water and IPA-acetone mixtures. Specifically, the controller inputs are the reflux flow and the reboiler steam flow, while the outputs are distillate and bottom compositions respectively. Previous works have attempted to determine the dynamics of said column, therefore the MPC to be developed in this project is based on two of the derived models, one is a 2 X 2 Wood and Berry model and the other an inferential model. A comparison between the developed MPC controllers with standard PID controller is done to demonstrate the effectiveness and reliability of the MPC controller
Feed Forward Neural Network Model for Isopropyl Myristate Production in Industrial-scale Semi-batch Reactive Distillation Columns
The application of the artificial neural network (ANN) model in chemical
industries has grown due to its ability to solve complex model and online application
problems. Typically, the ANN model is good at predicting data within the training range
but is limited when predicting extrapolated data. Thus, in this paper, selected optimum
multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) models
are used to predict the bottom (xb) compositions of extrapolated data. The MIMO and
MISO models both managed to predict the extrapolated data with MSE values of 0.0078
and 0.0063 and with R2 values of 0.9986 and 0.9975, respectively
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