Mathematical modeling and simulation of control strategies for continuous stirrer tank reactor

Ibrahim, HG

doi:10.3329/bjsir.v57i3.62017

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Their models solely took into account fixed-interval and fixed-size shipments as a form of shipping mechanism. The issues of raw material, WIP, and finished product inventories were taken into consideration separately by some researchers (Suzaki, 2021;Wemmerlow, 2022;Svensson, 2020;Palas and Bunduchi, 2021;Ibrahim, 2022;Jamshidi and Osanloo, 2018) during the model development; however, it makes more sense to analyze all of these issues concurrently. Blackburn and Millen (2010) proposed an exact solution procedure for time-varying demand models that took into account two-stage and multi-stage manufacturing systems, but they did not take into account the manufacturing situation where the production capacity is flexible.…”

Section: Methodsmentioning

confidence: 99%

Manufacturing system optimization using computer-based control kanban loops

Ezema

Nwobodo-Nzeribe

Eneh

et al. 2022

Bangladesh J. Sci. Ind. Res.

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The study assessed manufacturing system optimization using computer-based control kanban loops. The study assessed JIT's effect on average throughput-time, demand fill rate, net operating income for a certain product mix complexity and manufacturing overhead level. ARENA/SIMAN and WITNESS simulation tools were used to study implementation. The Drug Process Plant's JIT system reduced end buffer inventory, shortened customer lead time, and improved visual control. The mean response of the simulated new physical system in terms of NOI was 85.54 but gave a mean response of 85.30 when implemented on the shop floor. The mean response of the simulated new physical system in terms of cycle time was 617 but gave a mean response of 634 when implemented on the shop floor. Simulated new physical system in terms of DFR gave a mean response of 93.11 when implemented on the shop floor. Overwhelmingly, the new JIT system outperformed the old physical system. Bangladesh J. Sci. Ind. Res. 57(4), 221-228, 2022

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Section: Methodsmentioning

confidence: 99%

Manufacturing system optimization using computer-based control kanban loops

Ezema

Nwobodo-Nzeribe

Eneh

et al. 2022

Bangladesh J. Sci. Ind. Res.

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“…The first-order reaction with dead time approximated transfer function model presented provides a scoop for the development of control strategies for both single-input, single-output (SISO) and multi-input, multi-output (MIMO) CSTR configurations. Ibrahim (2022) [14], researched mathematical modelling and simulation of control strategies for stirrer tank reactors. According to the researcher, the concept of control theory is primarily aimed at responding to unsteady state operation by subjecting the control system to severe constraints in its operating space to ensure safe and maximum operation of the system to enhance economic efficiency or viability of the process [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…The multilayer feeds forward neural networks (MFFNN) are capable of modelling any non-linear process without prior knowledge [21,22,23]. The self-learning ability of neural networks which is capable of eliminating the use of complex and difficult mathematical analysis encountered in traditional methods of model development makes it more efficient based on gradient descent optimization, backpropagation algorithm for feed forward networks is commonly used in a chemical engineering field [26,27].…”

Section: Introductionmentioning

confidence: 99%

Development and assessment of a Manual and Automated PID Controllers for the Optimum Production of Ethylene Glycol in a CSTR

Wosu,

Ezeh,

Elias

2024

Preprint

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Industrially, one of the techniques used to enhance the optimum production of petrochemicals is the configuration of process control units such as gas chromatography (G.C. Analyzer) for concentration and thermocouple for temperature in the production system. This research focused on the application of the principles of mass and energy conservation in the development of dynamic or unsteady state models for predicting the behaviour of the system or process involved in the production of ethylene glycol from a reaction involving oxidation of ethylene to ethylene oxide which further undergoes hydrolysis to produce ethylene glycol in a continuous stirred tank reactor (CSTR). The high mathematical complexities of the mass and energy balance models of the Process as a result of the reaction kinetic scheme of the non-elementary reaction of the process which integrates both linear and non-linear terms into the models were resolved by applying the principles of Taylor’s series expansion, Laplace Transform, partial fraction and matrix appropriately in the development of the dynamic models. Process simulation tool Simulink was utilized in the configuration of closed-loop systems (thermocouple and G. C. Analyzer) at the feed point of the reactor for process variables (temperature and concentration) control during the process using proportional, integral and derivative (PID) as the controller parameters. The results of the process behaviour as shown in the temperature and concentration profiles versus time show that the use of manual tuning of different controller parameters (PID) of (0, 1 and 0), (0, 2 and 3) and (1.5,0.5 and 0) for temperature control and (3,0 and 0), (0,1 and 2) and (4,1 and 2) for concentration. The system experienced a high level of fluctuations and would not attain its stability or steady state even at process time above 16seconds. Whereas, the use of automatic tuning of desired controller gains of (1.361, 1.056 and 0.4109) for KP, KI and KD (PID) for temperature and pressure with tuning of 143.9 times requires a maximum time of 8 seconds for the system to attain stability. Ths study showed that automated tuning of the controller resulted in better performance characteristics for optimum production of ethylene glycol in a non-isotheral continuous stirred tank reactor within the shortest possible time.

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Mathematical modeling and simulation of control strategies for continuous stirrer tank reactor

Cited by 2 publications

References 5 publications

Manufacturing system optimization using computer-based control kanban loops

Manufacturing system optimization using computer-based control kanban loops

Development and assessment of a Manual and Automated PID Controllers for the Optimum Production of Ethylene Glycol in a CSTR

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