The modelling and control of a 1-octene dividing wall distillation column.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2009.Partitioned or Dividing Wall Columns (DWC) for distillation are currently receiving a lot mor

Controlling trace impurities in a dividing wall distillation column

Dividing wall distillation columns (DWCs) separate a feed mixture into three pure product streams using one column shell. Though attractive due to capital and operational savings, DWCs have yet to gain widespread industrial acceptance. One notable concern is controllability. The research within this document examines a four component feed mixture to evaluate the operational flexibility of a fixed-design DWC through experimental and simulation-based studies. A pilot DWC was successfully controlled at multiple operating points, and a dynamic model was developed to reflect the pilot dividing wall column. As a form of process intensification, DWCs have a higher risk for controller interaction making conventional PID control potentially inadequate. This work successfully used two PID temperature controllers to maintain the column at steady state, transition the column between steady states, and reject feed disturbances without controller interaction. These controller pairings were determined using conventional controller design techniques. Therefore, for this chemical system and column design, traditional approaches to distillation control are sufficient to handle the intensified nature of DWCs. Because more components are present in DWCs in larger amounts, there is concern that temperature control will no longer imply composition control. Temperature control proved successful in this study. Controlling two temperatures maintained column operation against feed disturbances. In addition, prefractionator temperature correlated well with reboiler duty for multiple feed qualities therefore serving as a promising control variable though more disturbances such as feed composition should be examined. The minimum energy controller was not tested experimentally. A steady state model with heat transfer matching the pilot data was scaled to the size of an industrial tower and used to generate a minimum energy response surface for different vapor and liquid split values. In summary, this research investigated the operational flexibility of a fixed-design DWC using a four component mixture, tested the ability of conventional distillation control design techniques to determine control structures for a DWC, and created a minimum energy operating surface that could be used to examine control structures. A technique to determine the overall heat transfer coefficients was developed, and the model closely matched experimental steady state data.Chemical Engineerin

Dynamic control of sensor and actuator failures in multivariable distillation column

This paper examines the impact of sensor and actuator failures in the operation of a multivariable distillation column. Several failure scenarios are evaluated including failures of sensors and actuators in various scales of magnitudes and durations. The results obtained illustrate the ability of process controllers in suppressing the impact of these unwanted events. Closed-loop dynamic responses of the process revealed capabilities of these controllers in dealing with upsets that are small in magnitude and duration. In the case of larger and longer process upsets, process controllers are not adequate in providing the necessary corrective measures. This leaves the necessary interventions to be taken by the plant operators, following alarms that would have been triggered in typical plant operation scenario

Thermodynamic Insight for the Design and Optimization of Extractive Distillation of 1.0-1a Class Separation

Nous étudions la distillation extractive continue de mélanges azéotropiques à temperature de bulle minimale avec un entraineur lourd (classe 1.0-1a) avec comme exemples les mélanges acétone-méthanol avec l’eau et DIPE-IPA avec le 2-méthoxyethanol. Le procédé inclut les colonnes de distillation extractive et de régénération de l’entraineur en boucle ouverte et en boucle fermée. Une première stratégie d’optimisation consiste à minimiser la fonction objectif OF en cherchant les valeurs optimales du débit d’entraineur FE, les positions des alimentations en entraineur et en mélange NFE, NFAB, NFReg, les taux de reflux R1, R2 et les débits de distillat de chaque colonne D1, D2. OF décrit la demande en énergie par quantité de distillat et tient compte des différences de prix entre les utilités chaudes et froides et entre les deux produits. La deuxième stratégie est une optimisation multiobjectif qui minimise OF, le coût total annualisé (TAC) et maximise deux nouveaux indicateurs thermodynamiques d’efficacité de séparation extractive totale Eext et par plateau eext. Ils décrivent la capacité de la section extractive à séparer le produit entre le haut et le bas de la section extractive. L’analyse thermodynamique des réseaux de courbes de résidu ternaires RCM et des courbes d’isovolatilité montre l’intérêt de réduire la pression opératoire dans la colonne extractive pour les séparations de mélanges 1.0-1a. Une pression réduite diminue la quantité minimale d’entraineur et accroît la volatilité relative du mélange binaire azéotropique dans la région d’opération de la colonne extractive. Cela permet d’utiliser un taux de reflux plus faible et diminue la demande énergétique. La première stratégie d’optimisation est conduite avec des contraintes sur la pureté des produits avec les algorithmes SQP dans les simulateurs Aspen Plus ou Prosim Plus en boucle ouverte. Les variables continues optimisées sont : R1, R2 et FE (étape 1). Une étude de sensibilité permet de trouver les valeurs de D1, D2 (étape 2) et NFE, NFAB, NFReg (étape 3), tandis l’étape 1 est faite pour chaque jeu de variables discrètes. Enfin le procédé est resimulé en boucle fermée et TAC, Eext et eext sont calculés (étape 4). Les bilans matières expliquent l’interdépendance des débits de distillats et des puretés des produits. Cette optimisation permet de concevoir des procédés avec des gains proches de 20% en énergie et en coût. Les nouveaux procédés montrent une amélioration des indicateurs Eext et eext. Afin d’évaluer l’influence de Eext et eext sur la solution optimale, la seconde optimisation multiobjectif est conduite. L’algorithme génétique est peu sensible à l’initialisation, permet d’optimiser les variables discrètes N1, N2 et utilise directement le shéma de procédé en boucle fermée. L’analyse du front de Pareto des solutions met en évidence l’effet de FE/F et R1 sur TAC et Eext. Il existe un Eext maximum (resp. R1 minimum) pour un R1 donné (resp. Eext). Il existe aussi un indicateur optimal Eext,opt pour le procédé optimal avec le plus faible TAC. Eext,opt ne peut pas être utilisé comme seule fonction objectif d’optimisation mais en complément des autres fonctions OF et TAC. L’analyse des réseaux de profils de composition extractive explique la frontière du front de Pareto et pourquoi Eext augmente lorsque FE diminue et R1 augmente, le tout en lien avec le nombre d’étage. Visant à réduire encore TAC et la demande énergétique nous étudions des procédés avec intégration énergétique double effet (TEHI) ou avec des pompes à chaleur (MHP). En TEHI, un nouveau schéma avec une intégration énergétique partielle PHI réduit le plus la demande énergétique. En MHP, la recompression partielle des vapeurs VRC et bottom flash partiel BF améliorent les performances de 60% et 40% respectivement. Au final, le procédé PHI est le moins coûteux tandis que la recompression totale des vapeurs est la moins énergivore. ABSTRACT : We study the continuous extractive distillation of minimum boiling azeotropic mixtures with a heavy entrainer (class 1.0-1a) for the acetone-methanol with water and DIPE-IPA with 2-methoxyethanol systems. The process includes both the extractive and the regeneration columns in open loop flowsheet and closed loop flowsheet where the solvent is recycled to the first column. The first optimization strategy minimizes OF and seeks suitable values of the entrainer flowrate FE, entrainer and azeotrope feed locations NFE, NFAB, NFReg, reflux ratios R1, R2 and both distillates D1, D2. OF describes the energy demand at the reboiler and condenser in both columns per product flow rate. It accounts for the price differences in heating and cooling energy and in product sales. The second strategy relies upon the use of a multi-objective genetic algorithm that minimizes OF, total annualized cost (TAC) and maximizes two novel extractive thermodynamic efficiency indicators: total Eext and per tray eext. They describe the ability of the extractive section to discriminate the product between the top and to bottom of the extractive section. Thermodynamic insight from the analysis of the ternary RCM and isovolatility curves shows the benefit of lowering the operating pressure of the extractive column for 1.0-1a class separations. A lower pressure reduces the minimal amount of entrainer and increases the relative volatility of original azeotropic mixture for the composition in the distillation region where the extractive column operates, leading to the decrease of the minimal reflux ratio and energy consumption. The first optimization strategy is conducted in four steps under distillation purity specifications: Aspen Plus or Prosim Plus simulator built-in SQP method is used for the optimization of the continuous variables: R1, R2 and FE by minimizing OF in open loop flowsheet (step 1). Then, a sensitivity analysis is performed to find optimal values of D1, D2 (step 2) and NFE, NFAB, NFReg (step 3), while step 1 is done for each set of discrete variables. Finally the design is simulated in closed loop flowsheet, and we calculate TAC and Eext and eext (step 4). We also derive from mass balance the non-linear relationships between the two distillates and how they relate product purities and recoveries. The results show that double digit savings can be achieved over designs published in the literature thanks to the improving of Eext and eext. Then, we study the influence of the Eext and eext on the optimal solution, and we run the second multiobjective optimization strategy. The genetic algorithm is usually not sensitive to initialization. It allows finding optimal total tray numbers N1, N2 values and is directly used with the closed loop flow sheet. Within Pareto front, the effects of main variables FE/F and R1 on TAC and Eext are shown. There is a maximum Eext (resp. minimum R1) for a given R1 (resp. Eext). There exists an optimal efficiency indicator Eext,opt which corresponds to the optimal design with the lowest TAC. Eext,opt can be used as a complementary criterion for the evaluation of different designs. Through the analysis of extractive profile map, we explain why Eext increases following the decrease of FE and the increase of R1 and we relate them to the tray numbers. With the sake of further savings of TAC and increase of the environmental performance, double-effect heat integration (TEHI) and mechanical heat pump (MHP) techniques are studied. In TEHI, we propose a novel optimal partial HI process aiming at the most energy saving. In MHP, we propose the partial VRC and partial BF heat pump processes for which the coefficients of performance increase by 60% and 40%. Overall, optimal partial HI process is preferred from the economical view while full VRC is the choice from the environmental perspective

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Inferential active disturbance rejection control of distillation columns

PhD ThesisThe distillation column is an important processing unit in the chemical and oil refining industry. Distillation is the most widely employed separation method in the world’s oil plants, chemical and petrochemical industrial facilities. The main drawback of the technique is high energy consumption, which leads to high production costs. Therefore, distillation columns are required to be controlled close to the desired steady state conditions because of economic incentives. Most industrial distillation columns are currently controlled by conventional multi-loop controllers such as proportional-integral-derivative (PID) controllers, which have several shortcomings such as difficulty coping with sudden set-point jumps, complications due to the integral term (I), and performance degradation due to the effect of noise on the derivative term (D). The control of ill-conditioned and strongly non-linear plants such as high purity distillation needs advanced control schemes for high control performance. This thesis investigates the use of active disturbance rejection control (ADRC) for product composition control in distillation columns. To the author’s knowledge, there are few reported applications of ADRC in the chemical industry. Most ADRC applications are in electrical, robotics and others. Therefore, this research will be the first to apply the ADRC scheme in a common chemical processing unit, and can be considered as a first contribution of this research. Initially, both PI and ADRC schemes are developed and implemented on the Wood–Berry distillation column transfer function model, on a simulated binary distillation column based on a detailed mechanistic model, and on a simulated heat integrated distillation column (HIDiC) based on a detailed mechanistic model. Process reaction curve method and system identification tools are used to obtain the 2×2 multi-input multi-output (MIMO) transfer function of both binary and HIDiC for the purpose of PI tuning where the biggest log-modulus tuning (BLT) method is used. Then, the control performance of ADRC is compared to that of the traditional PI control in terms of set-point tracking and disturbance rejection. The simulation result clearly indicates that the ADRC gives better control performance than PI control in all three case studies. The long time delay associated with product composition analysers in distillation columns such as gas chromatography deteriorates the overall control performance of the ADRC scheme. v To overcome this issue an inferential ADRC scheme is proposed and can be considered as a second contribution of this research. The tray temperatures of distillation columns are used to estimate both the top and bottom product compositions that are difficult to measure on-line without a time delay. Due to the strong correlation that exists in the tray temperature data, principal component regression (PCR) and partial least square (PLS) are used to build the soft sensors, which are then integrated into the ADRC. In order to overcome control offsets caused by the discrepancy between soft sensor estimation and actual compositions measurement, an intermittent mean updating technique is used to correct both the PCR and PLS model predictions. Furthermore, no significant differences were observed from the simulation results in the prediction errors reported by both PCR and PLS. The proposed inferential ADRC scheme shows effective and promising results in dealing with non-linear systems with a large measurement delay, where the ADRC has the ability to accommodate both internal uncertainties and external disturbances by treating the impact from both factors as total disturbances that will then be estimated using the extended state observer (ESO) and cancelled out by the control law. The inferential ADRC control scheme provides tighter product composition control that will lead to reduced energy consumption and hence increase the distillation profitability. A binary distillation column for separating a methanol–water mixture and an HIDiC for separating a benzene–toluene mixture are used to verify the developed inferential ADRC control scheme.Petroleum Development of Oman (PDO) for their generous support and scholarshi

Improvement of Batch Distillation Separation of Azeotropic Mixtures

La distillation est le procédé de séparation le plus répandu dans l'industrie chimique. Pour la séparation des mélanges azéotropiques, une méthode spéciale de distillation doit être appliquée. Le but de mon travail était d'améliorer la séparation des mélanges azéotropiques par distillation discontinue (DD). Un nouvel algorithme a été présenté pour la détermination de la séquence des produits de DD pour des mélanges multicomposants azéotropiques. Contrairement aux méthodes publiées précédemment, cet algorithme n'a pas besoin des paramètres d'équilibre. Configurations non-conventionnelles de DD ont été étudiées par simulation rigoureuse avec un accent sur l'opération fermée. Nombreux modes d'opération fermés étaient proposés, lesquelles diffèrent en l'opération de réservoir supérieur. Les effets du recyclage des fractions sur un procédé de séparation existant de 6 lots d'un mélange déchet azéotropique ont été étudiés. Les études ont été étendues pour un procédé de distillation extractive discontinue (DED). Un volume minimal de pré-fraction doit être incinéré. Le cas optimal de DED a donné un profit plus grand que celui de DD. DED a été étudié pour la séparation des deux mélanges azéotropiques. La séparation a été infaisable ou le rendement a été bas par DD, mais DED et le procédé hybride ont donné des rendements élevés. Une nouvelle politique de DED a été aussi proposée. Un modèle généralisé de la distillation hétéroazéotropique discontinue avec une rétention variable de décanteur a été développé. Dans une analyse de faisabilité, toutes les politiques opérationnelles possibles ont été identifiées. Ce modèle a été étendu pour la distillation extractive hétérogène discontinue. ABSTRACT : Distillation is the most widespread method for separating liquid mixtures. The separation of azeotropic mixtures requires a special distillation method. My aim was to improve the batch distillation separation of azeotropic mixtures. A new algorithm was presented for the determination of product sequences of batch distillation of multicomponent azeotropic mixtures. Non-conventional configurations were studied by simulation with emphasis on closed operation. The effects of off-cut recycle on a six-batch separation process of a waste solvent mixture were also investigated. Batch extractive distillation was studied for the separation of two azeotropic mixtures. A new extractive policy was also proposed. A generalised model of batch heteroazeotropic distillation with variable decanter hold-up was developed. This model was extended for batch heterogeneous extractive distillation