Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method

Vallerio, Mattia; Telen, Dries; Cabianca, Lorenzo; Manenti, Flavio; Impe, Jan Van; Logist, Filip

doi:10.1016/j.ces.2015.09.012

Cited by 44 publications

(18 citation statements)

References 47 publications

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“…The dynamic optimization domain covers many real‐life optimization problems, such as management , engineering design , traffic problems , and economic policy . These processes are often modeled by differential algebraic equations (DAEs).…”

Section: Introductionmentioning

confidence: 99%

Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

et al. 2018

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A novel optimal approach named invasive weed optimization‐control vector parameterization (IWO‐CVP) for chemical dynamic optimization problems is proposed where CVP is used to transform the problem into a nonlinear programming (NLP) problem and an IWO algorithm is then applied to tackle the NLP problem. To improve efficiency, a new adaptive dispersion IWO‐based approach (ADIWO‐CVP) is further suggested to maintain the exploration ability of the algorithm throughout the entire searching procedure. Several classic chemical dynamic optimization problems are tested and detailed comparisons are carried out among ADIWO‐CVP, IWO‐CVP, and other methods. The research results demonstrate that ADIWO‐CVP not only is efficient, but also outperforms IWO‐CVP in terms of both accuracy and convergence speed.

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Section: Introductionmentioning

confidence: 99%

Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

et al. 2018

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“…The parameters used in the cross-over and mutation operators are set as their suggested values, according to Eqs. (11) and (12). In the experiment, a total of 100 independent runs are performed for each problem to collect the statistical performance of each algorithm.…”

Section: Solving Robust Optimization Of Molten Steel Tem-mentioning

confidence: 99%

A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

Tian

Yu-dong

et al. 2017

ISIJ International

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LF (Ladle Furnace) refining plays an important role during secondary metallurgic process. The traditional LF refining operation relies on the workers' experience, it is disadvantageous to ensure the stable production, high-quality products and energy saving. A new robust operation optimization method of molten steel temperature based on AdaBoost.IR soft sensor is proposed in LF refining process. Firstly, an intelligent model based on BP (Back Propagation) neural network is established by analyzing the changes of energy during whole refining process of LF as sub intelligent model. Then an AdaBoost.IR is designed for the characters of industrial data, and is suitable for industrial soft sensor modeling. The ensemble soft sensor model is established for realizing the online real time measurement of molten steel temperature with better accuracy by using AdaBoost.IR. Secondly the robust operation optimization model is described by analyzing the process of LF refining based on above AdaBoost.IR soft sensor. And the HPOS-GA is used to solve the optimal operation solution of robust optimization model. The new robust operation optimization of temperature based on AdaBoost.IR is used in 300 t LF of the Baosteel Company. The results of experiments demonstrate the soft sensor can predict the temperature more accuracy and the end temperature of LF refining after robust optimization become more stable.

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“…Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.The concept of robust optimization (RO) was first proposed by [4] and has been extensively applied to design upstream synthesis units [5,6] and downstream separation units [3,7] for bio(chemical) processes. RO concepts can be categorized into three groups: worst-case [7,8], probability-based [5,6,9] and possibility-based [10]. The worst-case and possibility-based approaches are a good choice for crude uncertainty expressions, but might lead to conservative results [11].…”

mentioning

confidence: 99%

“…The worst-case and possibility-based approaches are a good choice for crude uncertainty expressions, but might lead to conservative results [11]. Probability-based concepts, which include detailed parameter uncertainty information regarding probability density functions (PDFs), are very relevant and have attracted considerable attention in the last decade [5,6,11]. However, the probability-based RO requires methods for uncertainty propagation and quantification (UQ), which pose obvious challenges in computational efficiency and approximation accuracy.…”

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confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

2018

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Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

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Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method

Cited by 44 publications

References 47 publications

Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

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