Optimal control policy for tracking optimal progression of temperature in a batch reactor – Some insights into the choice of objective function

Senthil, S. Arun; Sundaramoorthy, S.

doi:10.1016/j.ifacol.2018.05.019

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…On the other hand, the optimal time not only affects the heat duty, but exceeding this time may result in a reversible reaction, decreasing the process yield [34][35][36]. Recent efforts have determined the optimal temperature profile as a key parameter for yield in biodiesel batch operations [37][38][39].…”

Section: Case Study: Production Of Biodiesel In a Batch Reactormentioning

confidence: 99%

A New Approach to Solving Stochastic Optimal Control Problems

et al. 2019

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A conventional approach to solving stochastic optimal control problems with time-dependent uncertainties involves the use of the stochastic maximum principle (SMP) technique. For large-scale problems, however, such an algorithm frequently leads to convergence complexities when solving the two-point boundary value problem resulting from the optimality conditions. An alternative approach consists of using continuous random variables to capture uncertainty through sampling-based methods embedded within an optimization strategy for the decision variables; such a technique may also fail due to the computational intensity involved in excessive model calculations for evaluating the objective function and its derivatives for each sample. This paper presents a new approach to solving stochastic optimal control problems with time-dependent uncertainties based on BONUS (Better Optimization algorithm for Nonlinear Uncertain Systems). The BONUS has been used successfully for non-linear programming problems with static uncertainties, but we show here that its scope can be extended to the case of optimal control problems with time-dependent uncertainties. A batch reactor for biodiesel production was used as a case study to illustrate the proposed approach. Results for a maximum profit problem indicate that the optimal objective function and the optimal profiles were better than those obtained by the maximum principle.

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Section: Case Study: Production Of Biodiesel In a Batch Reactormentioning

confidence: 99%

A New Approach to Solving Stochastic Optimal Control Problems

et al. 2019

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“…Though it is important to a form temperature profile, it is focused only on the literature. 1,2,18 Equation 7 gives the random nonlinear profile which is used as a reference trajectory for closed-loop validation of nonlinear control algorithms. In this case, if the actual chemical reagents are used as feed, the optimal temperature profile will be formed using any optimization technique involving the chemical properties of reagents.…”

Section: Introductionmentioning

confidence: 99%

“…Srinivas Palanki et al worked on neural-network-based optimal temperature profile formation . Arun Senthil et al formed the temperature profile based on Pontryagin’s minimum principle. Lopez Francisco formed the temperature profile for the polymerization reactor.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is obtained from the chemical analysis of the reactant. Though it is important to a form temperature profile, it is focused only on the literature. ,, Equation gives the random nonlinear profile which is used as a reference trajectory for closed-loop validation of nonlinear control algorithms. In this case, if the actual chemical reagents are used as feed, the optimal temperature profile will be formed using any optimization technique involving the chemical properties of reagents. Similarly, for the intended chemical feed, also the temperature profile can be formed using the reaction rate and the rate constant (Arrhenius equation) by any standard optimization technique.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking

Yadav

Poojary

et al. 2021

ACS Omega

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Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.

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“…However, to accurately detect the dangerous state set, the controller that meets the requirements is redesigned to consume time. In another study [12], PID adaptive control was used to track temperature quickly, which has superior control performance; moreover, the application effect of adaptive control method on piezoelectric actuators is useful [13]. In [14], an equivalent adaptive neural network controller was constructed for the CSTR nonlinear model to achieve robust progressive output tracking control and it significantly reduces the control workload.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control

Ouyang

Wang

2020

Complexity

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In the chemical production process, the temperature of the continuous reactor has nonlinear characteristics such as large inertia. An improved autodisturbance control method is proposed. By improving the tracking differentiator with adjustable parameters, the expanded state observer and the control structure obtained an improved automatic disturbance rejection control model and realized the optimal control of the nonlinear and large-delay systems. On the process control training system, the experiment of the continuous system process flow is compared with the anti-interference of the Smith predictive compensation system, which uses an optimal set of data. The reactor temperature fluctuates, but the self-resistance is improved. The optimal overshoot of the disturbance is close to 0. Compared with the original antidisturbance controller, the antidisturbance time is not affected by the number of iterations, which reduces the high requirements on the accuracy of the parameters. Therefore, the improved automatic disturbance rejection control improves the operation and enhances the ability to resist external disturbance that changes the controlled object.

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Optimal control policy for tracking optimal progression of temperature in a batch reactor – Some insights into the choice of objective function

Cited by 3 publications

References 2 publications

A New Approach to Solving Stochastic Optimal Control Problems

A New Approach to Solving Stochastic Optimal Control Problems

Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking

Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control

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