An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems

Zhang, Panpan; Chen, Hongmei; Liu, Xinggao; Zhang, Zeyin

doi:10.1016/j.bej.2015.07.004

Cited by 38 publications

(13 citation statements)

References 47 publications

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“…The key feature of ODE models is the existence of a large array of computational and theoretical techniques of model analysis beyond simple simulations. This includes sensitivity analysis (Savageau, 1971 ; Zi, 2011 ; Castillo-Montiel et al, 2015 ), symbolic analysis (Ibargüen-Mondragón et al, 2014 ), bifurcation analysis (Duan et al, 2011 ; Yuri, 2017 ), design space analysis (Savageau, 2011 ) model optimization (Vera et al, 2003 ; Zhang et al, 2015 ) and parameter estimation and identifiability (Raue et al, 2009 ).…”

Section: Models In Ordinary Differential Equationsmentioning

confidence: 99%

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

et al. 2017

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Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.

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Section: Models In Ordinary Differential Equationsmentioning

confidence: 99%

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

et al. 2017

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“…The weights assigned to the various process targets to produce a single objective function may be considered arbitrary in many cases, with decision-makers (brewers) not necessarily able to quantify a priori the relative importance of competing objectives. A number of multi-objective optimisation algorithms have been successfully applied to a wide range of engineering problems, where visualisation of the trade-offs can provide decision makers with valuable insight (Li et al, 2014;Gujarathi et al, 2015;Zhang et al, 2015;Fraga and Amusat, 2016;Che at al., 2017;Maria and Crişan, 2017;Kessler et al, 2017). Systematically exploring the trade-off and visualising Pareto optimal temperature manipulations for efficient fermentation is desirable to gain insight and assist brewers with the selection of the most preferable operation strategy.…”

Section: Introductionmentioning

confidence: 99%

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multi-objective beer fermentation optimisation

Rodman

Fraga

Gerogiorgis

2018

Computers & Chemical Engineering

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On the application of a nature-inspired stochastic evolutionary algorithm to constrained multiobjective beer fermentation optimisation General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. AbstractFermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time consuming nature of the process stage (duration >120hrs), where a trade-off exists between attainable ethanol concentration and required batch time. To explore this trade-off we employ a multi-objective Plant Propagation algorithm (the Strawberry algorithm), for identifying temperature manipulations for improved fermentation performance. The methodology employed successfully produces families of favourable temperature profiles which exist along the Pareto front. A subset of these output profiles can simultaneously reduce batch time and increase product ethanol concentration while satisfying constraints on by-products produced in the fermenters, representing significant improvements in comparison with current industrial practice. A potential batch time reduction of over 12 hours has been highlighted, coupled with a moderate improvement in ethanol content.

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“…PSO is a type of swarm intelligence optimization algorithm for global optimization and has proven to be a competitor to GA when it comes to optimization problems[ 24 ].Compared with other biological evolution algorithms, PSO occupies the bigger optimization ability using simple relations [ 25 ]. It is widely used in optimization because of its need for less parameter sets and its faster convergence rate and easy escape from the local optimum compared with other algorithms[ 26 – 31 ]. At the same time, it can perform strong parallel search and global optimization.…”

Section: Introductionmentioning

confidence: 99%

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

Huang

2017

PLoS ONE

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Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

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An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems

Cited by 38 publications

References 47 publications

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multi-objective beer fermentation optimisation

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

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