Performance Analysis of Three Controllers for the Polymerisation of Styrene in a Batch Reactor

Ekpo, Emmanuel E; Mujtaba, Iqbal M.

doi:10.2202/1934-2659.1014

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The function f is assumed to be continuously differentiable with respect to all its arguments. 32,33 The optimization problem is posed as a Nonlinear Programming (NLP) problem and is solved using a Successive Quadratic Programming (SQP) method within gPROMS software.…”

Section: Model Equationsmentioning

confidence: 99%

“…f ( x ( z ), u ( z ), v ) = 0 denotes the process model discussed in section , where x ( z ) refers to the set of all algebraic variables, u ( z ) denotes the control variables, and v represents the design variables (constant parameters). The function f is assumed to be continuously differentiable with respect to all its arguments. , The optimization problem is posed as a Nonlinear Programming (NLP) problem and is solved using a Successive Quadratic Programming (SQP) method within gPROMS software.…”

Section: Modeling Industrial Tbrmentioning

confidence: 99%

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Whole Crude Oil Hydrotreating from Small-Scale Laboratory Pilot Plant to Large-Scale trickle-Bed Reactor: Analysis of Operational Issues through Modeling

2011

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Catalytic hydrotreating (HDT) is a mature process technology practiced in the petroleum refining industries and used to treat the oil fractions from a crude oil distillation (CDU) unit for the removal of contaminants such as sulfur, nitrogen, etc. Hydrotreating of the whole crude oil before it goes to a CDU unit is a new technology and is regarded as one of the more difficult tasks that have not been reported widely in the public domain. Our recent study using a laboratory small-scale pilot plant shows significant improvement in middle distillate yields and quality of crude oil in terms of contaminants present. Recently, we also determined best kinetic parameters for several hydrotreating reactions using experimental data from the small-scale trickle-bed reactor (TBR), using parameter estimation techniques. With these parameters, we were able to develop a process model of TBR that was validated using the experimental data from the pilot plant. In this study, we scale up the pilot-scale TBR where the throughput of crude oil changes from 45 × 10–6 m3/h to 66.243 m3/h (10 000 bpd), the reactor height increases from 65 cm to 1061 cm, and the diameter changes from 2 cm to 399 cm. While isothermal conditions could be easily maintained in the small-scale TBR (an isothermal steady state model mentioned above was sufficient for the reactor), it is not the case for a large-scale reactor. Hence, a detailed model with energy balance was considered for the analysis of the large-scale reactor, and the temperature control issues are discussed. The ratio of reactor length to reactor diameter (L R /D R) is chosen in such a way so that radial variation could be neglected and it was obtained using an optimization technique. All the main simultaneously occurring hydrotreating reactions are considered. These reactions are hydrodesulfurization (HDS), hydrodenitrogentaion (HDN), hydrodeasphaltenization (HDAs), and hydrodemetallization (HDM), which includes hydrodevanadization (HDV), hydrodenickelation (HDNi). In addition, the chemical reactions responsible for converting part of the crude oil to middle distillate are also considered. The gPROMS modeling software is used for modeling and simulation of the scaled-up TBR process.

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Section: Model Equationsmentioning

confidence: 99%

Section: Modeling Industrial Tbrmentioning

confidence: 99%

Whole Crude Oil Hydrotreating from Small-Scale Laboratory Pilot Plant to Large-Scale trickle-Bed Reactor: Analysis of Operational Issues through Modeling

2011

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“…The first scenario involves an increase in the heat of reaction which represents the possible unmodelled side reactions. The second scenario is the reduction of the heat transfer coefficient to simulate the effect of fouling on the heat transfer surfaces (Cott and Macchietto, 1989;Ekpo and Mujtaba, 2007). The third scenario tests the robustness in case of disturbance in the input feed temperature due to overheating in the heat exchanger E-1.…”

Section: Resultsmentioning

confidence: 99%

“…Another model-based control strategy for the styrene polymerization studied by Hidalgo and Brosilow (1990) with some modification was presented in another research in which the manipulated variables were coolant, initiator, monomer, and solvent flowrates (Prasad et al, 2002). Temperature control of jacketed batch polystyrene reaction systems was investigated by manipulating a heater providing the required heating energy to the reaction mixture in Ö zkan et al (1998), Ekpo and Mujtaba (2007), Ghasem et al (2007), Altınten et al (2008), and Hosen et al (2014). Reactor temperature was controlled only through the manipulation of jacket coolant flowrate in Fileti et al (2007), Alipoor et al (2009), and Copelli et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Model development and control of an auto-refrigerated polystyrene polymerization reactor

Mokhtarname

Safavi

Urbas

et al. 2021

Transactions of the Institute of Measurement and Control

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Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

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“…Figure 5. Kinetic parameters ANN training data sets (training data: 1-24, testing data: 25-36 and validation data: 37-48).…”

Section: Asia-pacific Journal Of Chemical Engineeringmentioning

confidence: 99%

Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach

Hosen

Hussain

Mjalli

2011

Asia-Pacific J Chem Eng

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Modelling polymerization processes involves considerable uncertainties due to the intricate polymerization reaction mechanism involved. The complex reaction kinetics results in highly nonlinear process dynamics. Available conventional models are limited in applicability and cannot describe accurately the actual physico-chemical characteristics of the reactor dynamics. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile because the end use properties of the product polymer depend highly on temperature. However, to obtain accurate models in order to optimize the temperature profile, the kinetic parameters (i.e. frequency factors and activation energies) for a specific reactor must be determined accurately. Kinetic parameters vary considerably in batch reactors because of its high sensitivity to other reactor design and operational variables such as agitator geometry and speed, gel effects, heating systems, etc. In this work, the kinetic parameters were estimated for a styrene-free radical polymerization conducted in an experimental batch reactor system using a nonlinear least squares optimization algorithm. The estimated kinetic parameters were correlated with respect to reactor operating variables including initial reactor temperature (T o ), initial initiator concentration (I o ) and heat duty (Q) using artificial neural network (ANN) techniques. The ANN kinetic model was then utilized in combination with the conventional mechanistic model. The experimental validation of the model revealed that the new model has high prediction capabilities compared withother reported models. 

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Performance Analysis of Three Controllers for the Polymerisation of Styrene in a Batch Reactor

Cited by 6 publications

References 27 publications

Whole Crude Oil Hydrotreating from Small-Scale Laboratory Pilot Plant to Large-Scale trickle-Bed Reactor: Analysis of Operational Issues through Modeling

Whole Crude Oil Hydrotreating from Small-Scale Laboratory Pilot Plant to Large-Scale trickle-Bed Reactor: Analysis of Operational Issues through Modeling

Model development and control of an auto-refrigerated polystyrene polymerization reactor

Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach

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