Stochastic Optimization-based Approach for Simultaneous Process Design and HEN Synthesis of Tightly-coupled RO-ORC-HI Systems Under Seasonal Uncertainty

2021 ◽  
pp. 116961
Author(s):  
Zhichao Chen ◽  
Zhibin Lu ◽  
Bingjian Zhang ◽  
Qinglin Chen ◽  
Chang He ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Process Design ◽  
Tightly Coupled ◽  
Simultaneous Process

An Algorithm for Simultaneous Process Design and Control

2001 ◽  
Vol 40 (19) ◽  
pp. 4079-4088 ◽  
Author(s):  
Ioannis K. Kookos ◽  
John D. Perkins
Keyword(s):  
Process Design ◽  
Simultaneous Process ◽  
And Control

Simultaneous Process Design of a Cooled Tubular Fischer–Tropsch Reactor

2020 ◽  
Vol 8 (12) ◽  
pp. 2000683
Author(s):  
Evan Pretti ◽  
John Ludy ◽  
Carlos Pico ◽  
Jonas Baltrusaitis
Keyword(s):  
Process Design ◽  
Fischer Tropsch ◽  
Simultaneous Process

scholarly journals Simultaneous Process Design and Control Optimization using Reinforcement Learning

2021 ◽  
Vol 54 (3) ◽  
pp. 510-515
Author(s):  
Steven Sachio ◽  
Antonio E. del-Rio Chanona ◽  
Panagiotis Petsagkourakis
Keyword(s):  
Reinforcement Learning ◽  
Process Design ◽  
Control Optimization ◽  
Simultaneous Process ◽  
And Control

scholarly journals Reliable Tension Leveling Process Design Using Stochastic Optimization

2009 ◽  
Vol 95 (11) ◽  
pp. 740-746 ◽  
Author(s):  
Hiroshi Hamasaki ◽  
Masaki Shigaki ◽  
Fusahito Yoshida ◽  
Vassili Toropov
Keyword(s):  
Stochastic Optimization ◽  
Process Design

scholarly journals Reducing the Dimensions of the Stochastic Programming Problems of Metallurgical Design Procedures

Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Freddy A. Lucay
Keyword(s):  
Stochastic Optimization ◽  
Process Design ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Supervised Machine Learning ◽  
Design Procedures ◽  
Stochastic Formulation ◽  
Input And Output ◽  
Uncertainty Space

Process design procedures under uncertainty result in stochastic optimization problems whose resolution is complex due to the large uncertainty space, which hinders the application of optimization approaches, as well as the establishment of relationships between input and output variables. On the other hand, supervised machine learning (SML) offers tools with which to develop surrogate models, which are computationally inexpensive and efficient. This paper proposes a procedure based on modern design of experiments, deterministic optimization, SML tools, and global sensitivity analysis (GSA) to reduce the size of the uncertainty space for stochastic optimization problems. The proposal is illustrated with a case study based on the stochastic design of flotation plants. The results reveal that surrogate models of stochastic formulation enable the prediction of the structure, profitability parameters, and metallurgical parameters of designed flotation plants, as well as reducing the size of the uncertainty space via GSA and, consequently, establishing relationships between the input and output variables of the stochastic formulation.

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