Multiobjective Pareto Optimization of an Industrial Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm

Mitra, Kishalay; Majumder, Sushanta; Runkana, Venkataramana

doi:10.1080/10426910802679428

Cited by 17 publications

(9 citation statements)

References 24 publications

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“…Such observation has also been observed in other engineering design problems such as gearbox design, truss‐structure design, etc. [47] and in other chemical process optimization problems, such as in deterministic multi‐objective optimization of various processes like epoxy polymerization [40–43], industrial grinding [53], iron ore sintering process [54], continuous casting process [55], poly‐propylene terepthalate (PPT) polymerization [56], and iron ore induration [57]. In this way, the parametric sensitivity of uncertain parameters can be studied for a system which can help a decision maker to fix the level of tolerable uncertainty based on the system requirements (risk handling capability) and thereby the corresponding PO front and the trend analysis among the decision variables in that PO front can be used to run the process toward further improvement.…”

Section: Resultsmentioning

confidence: 99%

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Mitra

2011

Polymer Engineering & Sci

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Kinetic constants in a proposed reaction scheme act as a set of parameters that are to be tuned with the experimental data and henceforth any model‐based optimization study related to the concerned reactor performance assumes the values of these kinetic parameters as fixed as obtained from the previous curve fitting exercise. However, it is known that these parameters are subjected to inherent source of uncertainties such as errors in measurement, change in concentrations for which they are not tuned for, etc. Assuming these kinetic parameters fixed for rest of the optimization is, therefore, not realistic and one should ideally check the sensitivity of these parameters on the final results. Fuzzy Chance Constrained Programming (FCCP) is one such approach that allows a decision maker to carry out such an analysis and this has been depicted here for determining the effect of uncertain kinetic parameters on the optimal performance of a semibatch epoxy polymerization reactor. Additionally, this study captures the tradeoff between solution quality and solution reliability that provides the angle of parametric sensitivity to this study. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers

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Section: Resultsmentioning

confidence: 99%

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Mitra

2011

Polymer Engineering & Sci

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“…Modeling and optimization of gas-solid reactors has been studied extensively (Schaefer et al, 1974;Cumming and Thurlby, 1990;Rovaglio et al, 1994;Cross and Blot, 1999;Sadri et al, 2007) and it is difficult to review the entire subject in detail here. The author has been involved in the development of mathematical models for industrial scale gas-solid reactors, related software tools (Virtual Sinter TM , Virtual Indurator TM and DRIKS TM ) and their application for various industrial problems, for example, sintering of iron ore fines (Venkataramana et al, 1997(Venkataramana et al, , 1998(Venkataramana et al, , 1999(Venkataramana et al, , 2002Gupta and Runkana, 2000;Kapur and Runkana, 2003), induration of iron ore pellets on a traveling grate Mitra et al, 2009;Cavalcante et al, 2012;Runkana and Majumder, 2013), direct reduction of iron ore by coal in a rotary kiln (Runkana et al, 2007;, 2010c, and calcination of limestone in a vertical shaft kiln, etc. The objective here is to share this knowledge of model development incorporating important physico-chemical phenomena, simulation and optimization techniques employed and details of their demonstration for a couple of industrial cases.…”

Section: Need For Soft-sensors and Data Analyticsmentioning

confidence: 99%

“…The optimization algorithm may be based on rigorous nonlinear programming techniques such as sequential quadratic programming (SQP) (Edgar and Himmelblau, 1989) or based on repeated simulations which may be warranted in case of difficulties in convergence for constrained optimization. In case of multi-objective optimization, it may be necessary to invoke techniques such as goal programming or genetic algorithms (Deb, 2001;Mitra et al, 2009).…”

Section: Framework For Optimizationmentioning

confidence: 99%

“…After validating the model, process optimization was carried out using an appropriate optimization algorithm. Initially, multi-objective optimization was carried out using a genetic algorithm (Mitra et al, 2009) but since it was computationally intensive, a single objective optimization problem was formulated and solved using the SQP . The induration model and the optimization algorithm were tested extensively with sev-eral months of process operating data to check numerical convergence, robustness of the model and accuracy of model predictions and optimization solutions obtained.…”

Section: Model Validation and Process Optimizationmentioning

confidence: 99%

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Model-Based Optimization of Industrial Gas-Solid Reactors

Runkana

2015

KONA

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Heterogeneous non-catalytic gas-solid reactors are commonly used in the production of chemicals, metals and metal oxides, for example, rotary drum reactor for producing quick lime; blast furnace, a moving bed reactor for producing iron, etc. Industrial processes involve multicomponent mixtures of solids as most of the naturally occurring materials such as minerals are multicomponent in nature. Generally the raw materials are heterogeneous and their physical and chemical characteristics vary from one source to another. The solid particles are also usually moist, the moisture content varying from season to season. The physico-chemical phenomena that take place in these reactors include flow of gases through porous media, heat transfer between the gases, solids, equipment and the environment, evaporation and condensation of moisture, reactions between gases and solids and within a single phase, melting and solidification depending on the temperatures inside the reactor, etc. The flow of particles is also of great importance especially in rotary drum and moving bed reactors. General features of the mathematical models for non-catalytic reactors are described along with models for the phenomena mentioned above. Case studies from the iron and steel industry on model-based optimization of production of direct reduced iron in a rotary kiln and induration of wet iron ore pellets on a moving packed bed reactor are discussed here.

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“…Many optimization problems in engineering involve multiple conflicting objective functions, as for example [1][2][3][4][5]. Such problems are called multiobjective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

Sindhya

Miettinen

2011

Materials and Manufacturing Processes

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In this article, we present a new perspective in solving computationally demanding problems such as the optimal control of the continuous casting of steel. We consider a multiobjective formulation of the optimal control of the surface temperature of the steel strand with five objectives, where constraint violations are minimized as objectives because no feasible solutions exist otherwise. A hybrid evolutionary multiobjective algorithm (HNSGA-II) is used to overcome discrepancies of evolutionary multiobjective optimization algorithms such as slow convergence and lack of convergence proof to the Pareto front. HNSGA-II uses NSGA-II as an underlying evolutionary multiobjective algorithm and an achievement scalarization function to scalarize objective functions for local search, which improves the population and speeds up the convergence. This is important because most evolutionary multiobjective algorithms are known to have difficulties with five objective functions. The algorithm is used to generate a set of Pareto solutions showing different trade-offs. However, it is difficult to generate Pareto solutions showing all the different trade-offs with a finite small population size. Hence, we use the preference information related to constraint violations in the weights of the achievement scalarization function to generate solutions with different trade-offs in preferable regions of the Pareto front. In addition, we use clustering and present typical solutions of different clusters so that the most preferred solution to be implemented can easily be identified. We demonstrate the approach and compare the results to previous studies.QC 2012020

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Multiobjective Pareto Optimization of an Industrial Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm

Cited by 17 publications

References 24 publications

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Model-Based Optimization of Industrial Gas-Solid Reactors

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

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