Applications of Multiobjective Optimization in Chemical Engineering

Bhaskar, Vidhyacharan; Gupta, Santosh K.; Ray, Ajay K.

doi:10.1515/revce.2000.16.1.1

Cited by 301 publications

(217 citation statements)

References 45 publications

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“…niching, fitness sharing) in order to generate uniformly distributed solutions over the entire PF. The interested reader may refer to [15][16][17] among others for details about these algorithms.…”

Section: Methods For Computing the Pareto Frontmentioning

confidence: 99%

Optimal design and operation of activated sludge processes: State-of-the-art

Hreiz

Latifi

Roche

2015

Chemical Engineering Journal

170

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To cite this version:Rainier Hreiz, M A Latifi, Nicolas Roche. Optimal design and operation of activated sludge processes: State-of-the-art. Chemical Engineering Journal, Elsevier, 2015Elsevier, , 281, pp.900 -920. 10.1016Elsevier, /j.cej.2015 The paper deals with the optimal control and design of activated sludge processes. Studies on the topic are critically reviewed. Focus is set on the problem formulation that allows one getting reliable solutions. The selection of appropriate cost functions and constraints is discussed. Aspects related to dealing with model mismatch are presented. a r t i c l e i n f o t r a c tThe activated sludge process (ASP) is the most commonly used process for wastewater treatment. Improving its performance is necessary from economic and environmental point of views. In this context, dynamic optimization is a powerful tool for assisting engineers in determining optimal operations and designs for ASPs.However, the real optimality of the solution strongly depends on the optimization problem statement, for which unfortunately, there is no standard or commonly accepted formulation. In a hopeful attempt to provide a guideline for future works on the topic, this paper reviews the literature devoted to optimal control and design of ASPs. The main issues to be addressed in order to get reliable solutions are discussed, among which: (1) Managing the inevitable mismatch between the model predictions and the real ASP operation. (2) Dealing with the unpredictable variations in the wastewater characteristics. (3) Accounting for the slowest dynamic processes occurring in ASPs. (4) Appropriately selecting the decision variables and the flowsheet structure in order to simplify the problem formulation from a mathematical perspective. (5) Conveniently choosing the cost functions expressions/correlations. (6) Successfully selecting the mathematical constraints in order to guarantee physically relevant operations.

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Section: Methods For Computing the Pareto Frontmentioning

confidence: 99%

Optimal design and operation of activated sludge processes: State-of-the-art

Hreiz

Latifi

Roche

2015

Chemical Engineering Journal

170

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“…In practice, optimising the design and operation of (bio)chemical processes often gives rise to optimisation problems with different and conflicting objectives (Bhaskar et al, 2000;Sendin et al, 2006). Typically, these multiple objective optimisation (MOO) problems produce a set of optimal solutions (or the Pareto set) instead of one sole.…”

Section: Introductionmentioning

confidence: 99%

“…Stochastic approaches on the other hand have been quite successful over the last decades (Bhaskar et al, 2000). However, these routines (i ) may become time consuming due to the repeated model simulations required, (ii ) may require the proper selection of algorithmic parameters (e.g., population size), (iii ) are less suited to incorporate constraints exactly, and (iv ) are limited to rather low dimensional search spaces (due to their more systematic exploration of these spaces).…”

Section: Introductionmentioning

confidence: 99%

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Logist

Erdeghem

Impe

2009

Chemical Engineering Science

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In practical optimal control problems multiple and conflicting objectives are often present, giving rise to a set of Pareto optimal solutions. Although combining the different objectives into a convex weighted sum and varying the weights is the most common approach to generate the Pareto front (when deterministic optimisation routines are exploited), it suffers from several intrinsic drawbacks. A uniform variation of the weights does not necessarily lead to an even spread on the Pareto front, and points in non-convex parts of the Pareto front cannot be obtained (Das and Dennis, 1997). Therefore, this paper investigates alternative approaches based on novel methods as Normal Boundary Intersection (Das and Dennis, 1998) and Normalised Normal Constraint (Messac et al., 2003;Messac and Mattson, 2004) to mitigate these drawbacks. The resulting multiple objective optimal control procedures are successfully used in (i ) the design of a chemical reactor with conflicting conversion and energy costs, and (ii ) the control of a bioreactor with a conflict between yield and productivity.

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“…Indeed, engineering design problems are usually characterized by the presence of many conflicting objectives that the design has to fulfil. Therefore, it is natural to look at the engineering design problem as a multiobjective optimization problem (MOOP) (Bhaskar et al, 2000;Coello, 2000, Ehrgott, 2000. As most optimization problems are multiobjective by nature, there are many methods available to tackle these kinds of problems.…”

Section: General Frameworkmentioning

confidence: 99%

Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

Dietz¹,

Pantel²,

Pibouleau³

et al. 2007

International Journal of Chemical Reactor Engineering

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This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.

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Applications of Multiobjective Optimization in Chemical Engineering

Cited by 301 publications

References 45 publications

Optimal design and operation of activated sludge processes: State-of-the-art

Optimal design and operation of activated sludge processes: State-of-the-art

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

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