Dynamic optimization of a copolymerization reactor using tabu search

Anand, Prashant; Rao, M Bhagvanth; Venkateswarlu, Ch.

doi:10.1016/j.isatra.2014.07.014

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…Most of the studies are based on classical methods of optimization such as Pontryagin's maximum principle [17], which has been applied to solve the optimal control problems of different polymerization reactors [18][19][20][21][22][23]. However, the classical methods have certain limitations for optimal control of polymerization reactors and these drawbacks have been discussed in literature [24]. The stochastic and evolutionary optimization methods are found beneficial over the conventional gradient-based search techniques because of their ability in locating the global optimum of multi-modal functions and searching design spaces with disjoint feasible regions.…”

Section: Application Of Tabu Search For Optimal Control Of a Polymeri...mentioning

confidence: 99%

“…In a system involving multiple stages, the output from one stage becomes the input to the subsequent stage. The multistage dynamic optimization procedure can be referred elsewhere [24]. This type of optimization needs special techniques to split the problem into computationally manageable units.…”

Section: Multistage Dynamic Optimization Strategymentioning

confidence: 99%

“…More details on modeling equations, reaction kinetics and numerical data pertaining to this system can be referred elsewhere [24].…”

Section: The Description Of Polymerization Process and Its Mathematic...mentioning

confidence: 99%

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A Metaheuristic Tabu Search Optimization Algorithm: Applications to Chemical and Environmental Processes

Venkateswarlu¹

2022

Engineering Problems - Uncertainties, Constraints and Optimization Techniques

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Stochastic optimization methods are increasingly used for optimizing processes that are difficult to solve by conventional techniques. These methods are widely employed to optimize the processes which have higher dimensionality with severe nonlinearities. Different methods of this kind include the genetic algorithm (GA), simulated annealing (SA), differential evolution (DE), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), artificial bee colony (ABC) algorithm, and cuckoo search (CS) algorithm. Among these methods, tabu search (TS) is a potential tool used to find a feasible optimal solution from a finite set of solutions. The memory used in TS will remember the current best solution and it also enables the TS to track the last solutions while guiding the search moves. The capability of memory and strategic adaptation features of TS enable it to make use of good solutions and also search for new feasible regions in the search space. TS has been successfully applied to solve a wide spectrum of optimization problems in different disciplines. This chapter describes the TS algorithm in detail and its applications to chemical and environmental processes, specifically, dynamic optimization of a copolymerization reactor and inverse modeling of a biofilm reactor. In dynamic optimization of copolymerization reactor, the meta heuristic Tabu search (TS) is designed and applied to determine the optimal control policies of a styrene–acrylonitrile (SAN) copolymerization reactor. In inverse modeling of biofilm reactor, the tabu search is designed and applied to determine the parameters of kinetic and film thickness models as consequence of the validation of the mathematical models of the process with the aid of measured data acquired from an experimental fixed bed anaerobic biofilm reactor used in the treatment of pharmaceutical industry wastewater. For both the cases, optimization by Tabu search is carried out by suitably formulating the desired objective functions and the problems are solved by encoding the variables and parameters using real floating point numbers. The results explain the efficacy of TS for optimal control of polymerization reactor and inverse modeling of biofilm reactor.

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Section: Application Of Tabu Search For Optimal Control Of a Polymeri...mentioning

confidence: 99%

Section: Multistage Dynamic Optimization Strategymentioning

confidence: 99%

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A Metaheuristic Tabu Search Optimization Algorithm: Applications to Chemical and Environmental Processes

Venkateswarlu¹

2022

Engineering Problems - Uncertainties, Constraints and Optimization Techniques

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“…Since when it was developed by Glover (1989), the tabu search is widely used in logistics problems, with restrictions mainly on vehicle routing, as cited in the work of Chaves, Biajoli, Mine, and Souza (2007), Ma, Hao, and Wang (2017) and Silvestrin and Ritt (2017). However, because to its easy use, the tabu search has been applied in several fields, such as in the modeling of chemical reactions and polymerization of molecules in the area of chemistry (Anand, Rao, & Venkateswarlu, 2015;Nandi, McAnanama-Brereton, Waller, & Anoop, 2017), in obtaining of PID controller and Mimo systems in telecommunications (Ateş & Yeroglu, 2016;Karthikeyan & Saraswady, 2018), in the control of data flow in the computer science (Michalska, Zufferey, & Mattavelli, 2016), in product development and design in the industrial engineering (Bożejko, Gnatowski, Pempera, & Wodecki, 2017;Soto, Rosarius, Rieger, Chen, & Adey, 2017), in nuclear power management (Hill & Parks, 2015) and in the control of cash flow in the economic area (He, He, Liu, & Wang, 2017).…”

Section: Use Of Tabu Search In Process Optimizationmentioning

confidence: 99%

<b>Combination of optimization techniques to find of processing optimal condition of postharvest broccoli by vacuum cooling process

et al. 2018

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This study aimed to investigate the impact of each factor on the weight loss of postharvest broccoli and treatment efficacy, and also attempted to fix the optimal condition for vacuum cooling treatment on postharvest broccoli by response surface methodology combined with tabu search techniques. Fresh broccoli samples were harvested from a Chinese farm and the green heads of selected samples were cut into smaller ones with approximately 3~4 cmdiameter, and sequentially equilibrated to room temperature. Pressure (200-600 Pa), broccoli weight (200-500 g), water volume (2-6 %, v v-1) and time (20-40 min) were used as factors and weight loss, final temperature and cost as responses. A tabu search algorithm was developed to find the optimum condition for processing broccoli and its initial condition were from response surface methodology. Results demonstrates a good adjust of tabu search algorithm in simulation of the broccoli freezing process. From tabu list the best condition were found as follows: the broccoli weight between 273.5 and 278.0 g with a water volume of 3.0%, processed for 40.0 min and at 200 Pa, where the weight loss was 0.34 ± 0.01%, of end temperature was 2.0 ± 0.0°C and profit percent was 99.66 ± 0.01%.

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“…Sarkar et al [16] used genetic algorithms to optimize feed bioreactors: multiple control variables. Anand et al [17] proposed a tabu search method to dynamically optimize the copolymerization reactor. Schluter et al [18] proposed the design of an integrated process and control system based on the ant colony optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

et al. 2021

Processes

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The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.

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Dynamic optimization of a copolymerization reactor using tabu search

Cited by 12 publications

References 29 publications

A Metaheuristic Tabu Search Optimization Algorithm: Applications to Chemical and Environmental Processes

A Metaheuristic Tabu Search Optimization Algorithm: Applications to Chemical and Environmental Processes

<b>Combination of optimization techniques to find of processing optimal condition of postharvest broccoli by vacuum cooling process

Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

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