Ants Foraging Mechanism in the Design of Multiproduct Batch Chemical Process

Wang, Chunfeng; Zhao, Xin

doi:10.1021/ie010932r

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Several possible routes through the different cells are first tried, but the shortest ones stand out. The ACO was used among others for optimization of chemical synthesis (Raeesi et al, 2008) and for design of multiproduct batch chemical process (Chunfeng and Xin, 2002). The same principles can be implemented for defining an optimum hydrometallurgical process sequence.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…1) consisted of 3 process steps, 8 unit operations and 30 levels of operating parameters for each step (linear interpolation of data was used). The number of alternative processes and operating parameter combinations is approximately 1.4 · 10 7 but, with ACO-based method, the CPU time was only 13 s. The short computational time to solve the current process synthesis problem can be explained by simplicity of the model, as only algebraic calculus is used, and by efficiency of the ACO in solving combinatorial optimization problems (Raeesi et al, 2008;Chunfeng and Xin, 2002).…”

Section: Zinc Recovery From Aod Dustmentioning

confidence: 99%

“…However, the problem can be addressed in an efficient manner by using meta-heuristics to find approximate solutions (Raeesi et al, 2008;Biswas et al, 2009). The stochastic meta-heuristic ant colony optimization (ACO) algorithm has been found to be promising for efficient synthesis and optimization of processes (Raeesi et al, 2008;Chunfeng and Xin, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of hydrometallurgical processes for valorization of secondary raw materials using ant colony optimization and key performance indicators

2015

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An algorithm-based method for synthesis of hydrometallurgical processes using limited amounts of experimental data is presented. The method enables simultaneous selection and sequencing of unit operations and optimization of operating parameters. An ant colony optimization (ACO) based algorithm is used to identify the most economic process alternative in an iterative manner. Key performance indicators are used for comparison of candidate processes: a purification performance index measures purity improvement and a separation cost indicator is used as an objective function in process optimization. Computational times were reduced significantly with the suggested method compared to an algorithm which evaluates all the possible process options. The practical applicability of the method to hydrometallurgy is demonstrated by investigating zinc recovery from argon oxygen decarburization dust with two alternative leaching methods and recovery of lanthanides from nickel metal hydride (NiMH) batteries. In the first zinc recovery process, 150 min normal batch leaching with 0.5 M H 2 SO 4 is used, and in the other one 270 min batch leaching with H 2 SO 4 is done by controlling the pH (N 3.0). In both cases the leachate is extracted with D2EHPA at pH 4.27, and stripped with circulating solution from zinc electrolysis. For lanthanides recovery the algorithm suggested a process in which the raw material is leached with 1.3 M HCl, the leachate is extracted with D2EHPA at pH 2.2, organic phase is stripped with 2.0 M HCl and 99% pure Ln-oxalates are precipitated with oxalic acid at pH 0.6. Compared to previously suggested process for the same raw material, the algorithm suggests operating the leaching step such that higher selectivity is achieved by sacrificing some yield. AbbreviationsACO ant colony optimization AOD argon oxygen decarburization CPU central processing unit EDR energy dissipation rate IRR internal rate of return KPI key performance indicator PPI purification performance index SCI separation cost indicator Notation A flow rate, m 3 /s c i concentration of contaminants, kg/m 3 E concentration of extractant, m 3 /m 3 K cost, item of expenses, €/kg k L specific cost of a leaching step, €/kg k pur,l specific cost of a purification step, €/kg L probability M number of components in a chemical system N number of ants in a colony n number of process steps P number of discrete values of operating parameters SL solvent loss in solvent extraction T concentration of a target metal in the system, kg/m 3 t b batch time in leaching, s U number of unit operations V L volume of leaching vessel, m 3 x purity Y yieldGreek symbols α the degree of importance of the pheromones ξ parameter used to control the scale of the global updating of the pheromone τ l,u,p amount of pheromone in a cell ρ pheromone decay factor Hydrometallurgy 153 (2015) 121-133

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: Zinc Recovery From Aod Dustmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthesis of hydrometallurgical processes for valorization of secondary raw materials using ant colony optimization and key performance indicators

2015

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“…Comprehensive information on ACO can be found in Dorigo and Stuetzle [14] Several extensions of the ACO metaheuristic for continuous search domains can be found in the literature, among them Socha and Dorigo [32], Yu et al [36], Dreo and Siarry [15] or Kong and Tian [27]. Other applications of ACO frameworks for real-world problems, arising from engineering design applications, can be found in Jayaraman et al [25], Rajesh et al [29], Chunfeng and Xin [10] or Zhang et al [37]. In contrast extensions for mixed integer search domains are very rare in the literature.…”

Section: Introductionmentioning

confidence: 99%

Extended ant colony optimization for non-convex mixed integer nonlinear programming

Schlüter

Egea

Banga

2009

Computers & Operations Research

211

124

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Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results.These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach.

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“…It should be emphasized that the batch plant's design has long been identified as a key problem in chemical engineering as reported in the literature (Montagna et al. 2000; Cao and Yuan 2002; Chunfeng and Xin 2002; Heo et al. 2003; Cavin et al.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Optimization of Multiproduct Batch Chemical Process Using Genetic Algorithm

Mokeddem

Khellaf

2010

J Food Process Engineering

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Optimal design problems are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is first formulated as a multiobjective optimization problem, to be solved using the well‐suited nondominating sorting genetic algorithm (NSGA‐II). The NSGA‐II has the capability to achieve fine tuning of variables in determining a set of nondominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA‐II ability to identify a set of optimal solutions provides the decision maker with a complete picture of the optimal solution space to gain better and appropriate choices. The effectiveness of NSGA‐II method with multiobjective optimization problem is illustrated through a carefully referenced example. PRACTICAL APPLICATIONS The inherent dynamic nature of batch processes allows for their ability to handle variations in feedstock and product specifications, and provides the flexibility required for multiproduct or multipurpose facilities. They are thus best suited for the manufacture of low‐volume, high‐value products, such as specialty chemicals, pharmaceuticals, agricultural, food and consumer products, and most recently the constantly growing spectrum of biotechnology‐enabled products. Batch processing in the food industry has some unit operations, such as panning, whipping (emulsifying), and the pulling of taffy, which are not too often used in other industries, as well as other operations,such as blending, tabletting (including granulation) and lyophilization, which are common in other industries. Reduced time to market, lower production costs and improved flexibility are all critical success factors for batch processes.

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Ants Foraging Mechanism in the Design of Multiproduct Batch Chemical Process

Cited by 11 publications

References 18 publications

Synthesis of hydrometallurgical processes for valorization of secondary raw materials using ant colony optimization and key performance indicators

Synthesis of hydrometallurgical processes for valorization of secondary raw materials using ant colony optimization and key performance indicators

Extended ant colony optimization for non-convex mixed integer nonlinear programming

Multicriteria Optimization of Multiproduct Batch Chemical Process Using Genetic Algorithm

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