Integrated Design and Control under Uncertainty: A Fuzzy Modeling Approach

Lu, XinJiang; Li, Han-Xiong; Duan, Ji’an; Sun, Dong

doi:10.1021/ie901457j

Cited by 19 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, it has an unknown complex relationship between the design variables and its performance. For example, the view factor F i, j (d), a measure of radiation heat contribution from the heating block to the (i , j ) zone, is the function of the design variable d. This view factor plays a major role in heat transfer [4] and has a complex relationship with the geometric parameters [6]- [8]. Furthermore, some boundary conditions are unknown.…”

Section: Problem Statementmentioning

confidence: 99%

“…According to the heat transfer theory and [4], since the view factor has stronger nonlinear relation with the H than the a, the H effect are more than the a. The orders of a and H on RSM are set to match the strong nonlinear relations.…”

Section: A Modeling Performancementioning

confidence: 99%

“…The curing process must provide the specified temperature profile to cure the materials, such as epoxy resins and encapsulation moulding compounds on electronic components, as well as to maintain a uniform temperature at every location on the cured object [3], [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Robust Design for a Curing Oven

Huang

2014

IEEE Trans. Compon., Packag. Manufact. Technol.

Self Cite

View full text Add to dashboard Cite

If no external control is employed, the cure performance will be fully dependent on the oven design. Design of such a system is challenging because of its inherent complexity: complex heat transfer, unknown relationship between design variable and curing performance, unknown boundary conditions and large parameter variation, and so on. In this paper, a novel data-based robust design approach is proposed to design a new curing oven with simple structure. First, a modeling method with hybrid singular value decomposition and response surface method is developed to extract the system model from experimental data. The coupling influence between design variable and model parameter is also separated to make the design simpler, because only the part affected by design variable will be considered in the design. A robust design method is then developed to minimize the influence of parameter variation on the performance. Finally, the effectiveness of the proposed method is demonstrated in the design of the new curing oven.Index Terms-Design for manufacture, design methodology, semiconductor device manufacture, semiconductor device modeling.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Section: A Modeling Performancementioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Robust Design for a Curing Oven

Huang

2014

IEEE Trans. Compon., Packag. Manufact. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…controllability index-based optimization approach [6]; mixed integer dynamic optimization-based approach [7][8][9][10]; robust-based optimization approach [11][12][13]; embedded control optimization approach [2,14,15], and intelligent optimization approach [16,17].…”

Section: Introductionmentioning

confidence: 99%

A hierarchical scheme for integrating process design and control using optimal PID control

Zhou

Tao

et al. 2015

2015 34th Chinese Control Conference (CCC)

View full text Add to dashboard Cite

Integrating process design and control (IPDC) can help chemical processes operate close to tighter boundaries safely and thus results in numerous economic and operability benefits over the traditional sequential design approach. The last three decades have seen growing interest in the integration of process design and control. However, rigorous methods for solving the problem of IPDC meet challenging mathematical formulations which are computationally intractable. In this paper, a problem of this kind is formulated as a hierarchical optimization problem, which is then solved by keeping the problem size manageable with a two-stage sequential method. The proposed approach is featured using an optimal PID controller which adaptively adjusts suitable control moves for each candidate design. And the control decisions are incorporated in a superstructure-based dynamic optimization formulation which considers both cost and controllability. The application of proposed method is illustrated and compared with the traditional sequential design approach in two chemical processes.

show abstract

“…, 2008; Peng et al. , 2008; Hernández, 2009; Liang & Cheng, 2009; Lu et al. , 2010; Çakmak & Boyacı, 2011 to cite just a few).…”

Section: Introductionmentioning

confidence: 99%

A neuro‐fuzzy computational approach for multicriteria optimisation of the quality of espresso coffee by pod based on the extraction time, temperature and blend

Russo¹,

Albanese

Siettos

et al. 2012

Int J of Food Sci Tech

View full text Add to dashboard Cite

Summary We demonstrate how soft computing methods can be exploited to solve multicriteria quality optimisation problems in food science and technology. In particular, we link neuro‐fuzzy modelling techniques with simulated annealing to optimise/design the quality of espresso coffee by pod. The design variables are the extraction time (ranging from 10 to 30 s), temperature (80–110 °C) and blends (100% Arabica, 100% Robusta and Arabica Robusta: A20R80, A80R20 and A40R60); they are not the only variables that affect the sensory profile of a cup of espresso coffee, but have a strong impact on the sensory quality of the beverage. Based on the framework, we show that the particular problem is a nonlinear one. Hence, an espresso coffee characterised by a specific sensory profile can be extracted using different sets of parameter values. For example, the same sensory profile can be obtained using either pure Robusta extracted at 22 s and 94 °C or 90% Arabica and 10% Robusta extracted at 25 s and 99 °C. Yet, the global optimum with respect to the distance to the optimum sensorial values is obtained using 70% Arabica and 30% Robusta extracted at 15 s around 93 °C.

show abstract

Integrated Design and Control under Uncertainty: A Fuzzy Modeling Approach

Cited by 19 publications

References 19 publications

Data-Driven Robust Design for a Curing Oven

Data-Driven Robust Design for a Curing Oven

A hierarchical scheme for integrating process design and control using optimal PID control

A neuro‐fuzzy computational approach for multicriteria optimisation of the quality of espresso coffee by pod based on the extraction time, temperature and blend

Contact Info

Product

Resources

About