Modeling and Control of Distillation Column in a Petroleum Process

This paper introduces a calculation procedure for modeling and control simulation of a condensate distillation column based on the energy balance (-) structure. In this control, the reflux rate and the boilup rate are used as the inputs to control the outputs of the purity of the distillate overhead and the impurity of the bottom products. The modeling simulation is important for process dynamic analysis and the plant initial design. In this paper, the modeling and simulation are accomplished over three phases: the basic nonlinear model of the plant, the full-order linearised model, and the reduced-order linear model. The reduced-order linear model is then used as the reference model for a model-reference adaptive control (MRAC) system to verify the applicable ability of a conventional adaptive controller for a distillation column dealing with the disturbance and the model-plant mismatch as the influence of the plant feed disturbances

Modeling and Control of Multivariable Distillation Column Using Model Predictive Control Using Unisim

Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s), prediction horizon (P = 1-400), and the control horizon (M=1-400). The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE) was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller

Offset-free model predictive control using Koopman-Wiener models

Abstract. This master’s thesis was built on the previously developed Koopman-Wiener nonlinear model predictive controller, and the goal of this thesis was to find a suitable strategy for rejecting steady-state offset, caused by plant model mismatch. This thesis also aimed to enable the controller to perform in applications where the full state is not measured and the available measurements are corrupted with noise. The work in this thesis considered multiple strategies for handling plant model mismatch, but disturbance rejection was selected as the main approach. It is proposed in this thesis that the disturbance model for disturbance rejection can be chosen by calculating empirical observability Gramian at a single initial point for every considered augmented model option and then picking the model which is interpreted as the most observable. The proposed observability analysis provides information about weak observability of the disturbance augmented model only at the single initial point. Nevertheless, it was argued in this thesis that the results can be assumed to represent the relevant operation region, and thus the method is applicable for choosing a disturbance model. As an alternative to compare against disturbance rejection, this thesis also investigated recursive least squares method that adapts the Koopman-Wiener model within the controller online. For state estimation, this thesis utilized unscented Kalman filter. This thesis demonstrated performance of the chosen methods with two nonlinear system case studies commonly studied in the literature: a simulated continuous stirred tank reactor and a simulated distillation column. This paper provides three main results. Firstly, the controller with disturbance rejection is successful in eliminating steady-state offset in a closed-loop system. Secondly, the controller is unable to reach satisfactory performance while using the recursive least squares method. Thirdly, the results from case studies support the chosen disturbance modeling approach, since the disturbance models chosen with the approach lead to improved or equal controller performance compared to using other disturbance models. Furthermore, the results support presenting a useful heuristic about how to perform disturbance modeling with Koopman-Wiener models by having the disturbances affect the slow dynamics of the model.Säätöpoikkeamasta vapaa malliprediktiivinen säädin käyttäen Koopman-Wiener malleja. Tiivistelmä. Tämä diplomityö perustui aiemmin kehitettyyn epälineaariseen Koopman-Wiener malliprediktiiviseen säätimeen. Diplomityön tavoitteena oli löytää sopiva strategia eliminoimaan tasapainotilan säätöpoikkeama, joka on seurausta tilanteesta, jossa säätimen käyttämä malli ei vastaa ohjattavaa prosessia. Työssä tavoiteltiin myös säätimen toiminnan mahdollistamista sovelluksissa, joissa prosessin jokaista tilamuuttujaa ei mitata, ja saatavilla olevissa mittauksissa on kohinaa. Diplomityössä harkittiin useita eri strategioita vastaamaan säätimen ja prosessin mallien yhteensopimattomuuteen, mutta häiriön torjunta valikoitui pääasialliseksi lähestymistavaksi. Diplomityössä ehdotetaan, että häiriön torjuntaan käytettävä häiriömalli voidaan valita laskemalla empiirinen havaittavuus Gramin matriisi yhdessä alkupisteessä jokaiselle harkitulle häiriömallille ja sitten valitsemalla malli, joka tulkitaan eniten havaittavaksi. Ehdotettu havaittavuusanalyysi tuottaa tietoa heikosta havaittavuudesta häiriöaugmentoidulle mallille vain valitussa alkupisteessä. Siitä huolimatta, tässä työssä argumentoitiin, että tulosten voidaan olettaa kuvastavan olennaista prosessin toiminta-aluetta, ja menetelmä soveltuu täten häiriömallin valitsemiseen. Vaihtoehtona häiriön torjunnalle, tässä työssä tutkittiin myös rekursiivista pienimmän neliösumman menetelmää adaptoimaan säätimessä käytettävää Koopman-Wiener-mallia ajon aikana. Tilaestoimointiin tässä työssä käytettiin hajustamatonta Kalman suodinta. Diplomityö demonstroi valittujen menetelmien suorituskykyä kahdella epälineaarisella tapaustutkimuksella: simuloitu jatkuvatoiminen sekoitusreaktori ja simuloitu tislauskolonni. Tässä työssä esitetään kolme tärkeää tulosta. Ensimmäiseksi, säädin joka käyttää häiriön torjuntaa, onnistuu poistamaan tasapainotilan säätöpoikkeaman takaisinkytketyssä systeemissä. Toiseksi, säädin ei saavuta tyydyttävää suorituskykyä rekursiivista pienimmän neliösumman menetelmää käytettäessä. Kolmanneksi, tapaustutkimukset tukevat ehdotettua lähestymistapaa häiriömallinnukseen, koska valitut häiriömallit johtavat parempaan tai yhtä hyvään säätimen suorituskykyyn verrattuna muiden häiriömallien käyttämiseen. Lisäksi tulokset tukevat hyödyllisen heuristisen säännön esittämistä Koopman-Wiener-mallien häiriömallintamiselle siten, että häiriömuuttujat vaikuttavat mallin dynaamisesti hitaisiin tilamuuttujiin

Mathematical modeling, control and simulation of petroleum distillation column

Distillation column processing is the most vital separation technology in the petroleum industries for purification of final products. Distillation columns are made up of several components each of which is used either to transfer heat energy or to enhance mass transfer. An inclination to controlling distillation columns in a manner that is economically efficient depends much on the selection of reliable systems. This thesis presents a detailed methodology to derive a calculation procedure of the distillation column to build a gas processing plant to raise the utility value of condensate. The quality of the output products are the purity of the distillate product should higher than or equal to 98% and the impurity of the bottoms product, should less or equal than 2%. The L-V structure, which is called energy balance structure, is considered as the standard control structure for the distillation system design. In this design system the liquid flow rate and the vapor flow rate are inputs variable parameters to determine the purity and impurity of the output product concentration. Therefore, an appropriate control system is essential for designing stage. The main role of the controller is to sustain the output concentration despite the disturbance in the feed flow and the feed concentration. This thesis will deliberate a control model system development via three steps. Firstly, develop a calculation procedure of a distillation column for simulation and analysis. Second for the controller design: a reduced-order linear model is derived such that it best reflects the dynamic of the distillation process and used as the reference model for a model-reference adaptive control (MRAC) system and thirdly, verify the ability of a conventional adaptive controller for the distillation system when dealing with process mismatch and feeding disturbances. However, in this study, the system identification is not fully employed as the actual production factors and designed structures are not validated. In this research, the calculations and simulations are implemented by using MATLAB (version 7.0) software package

Design and implementation of embedded adaptive controller using ARM processor.

This thesis is concerned with development of embedded adaptive controllers for industrial applications. Many industrial processes present challenging control problems such as high nonlinearity, time-varying dynamic behaviors, and unpredictable external disturbances. Conventional controllers are too limited to successfully resolve these problems. Therefore, the adaptive control strategy, an advanced control theory, is applied to overcome deficiencies of the conventional controllers

Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery: Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery

This paper presents a detailed procedure to develop a mathematical modelling and simulation of a distillation column for a real feedstock from a condensate processing plant as an initial step of a project feasibility study. The mathematical model of overall dynamics is established on the dynamic continuity equations of the mass and the energy for each unit operation where the mass and the energy can accumulate. The paper provides a case study tutorial for a typical petroleum refinery engineering design. The dynamic analysis and controller for the distillation systems are extremely complicated due to their nonlinearity and multivariable. A nonlinear model predictive control (NMPC) computational scheme for with soften constraints is developed to verify the applicable ability of a direct NMPC controller for a distillation column dealing with the disturbance and the model-plant mismatch as the influence of the plant feed disturbances

Development of Soft Sensor Model Using Moving Window Approach

Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advantages and performances of Moving Window approach over the traditional soft sensor models. Time invariant and stationary process conditions are those assumptions made in developing soft sensors, and these assumptions causes degradations and limitations to the soft sensors in estimating process variables. Degradations of soft sensors are caused by process shift, catalyst performance lost and et cetera. Besides that, the restrictions of sensors in estimating difficult-to-measure variables and the delays during the laboratory tests have becomeone of the factors in developing soft sensor. This paper presents a study regarding the multivariate statistical process control techniques that can be used in developing soft sensors such as Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis. The scope of study for the project includes understanding the concept andwhat are the adaptive schemes available to construct the soft sensors. Besides that further research on Moving Window approach together with MSPC techniques will be carried out which can be adapted into the adaptive models to develop the soft sensors. Systematic approach will be presented through this project in using Moving Window approach to construct the soft sensors and this includes an analysis of an appropriate case study where the approach can be implemented. Keywords: Multivariate Statistical Process Control techniques, Least Square Regression method, Partial Least Square Regression method and Principle Component Analysi