scholarly journals A Framework for Multiproduct Batch Plant Design with Environmental Consideration:  Application To Protein Production

2005 ◽  
Vol 44 (7) ◽  
pp. 2191-2206 ◽  
Author(s):  
A. Dietz ◽  
C. Azzaro-Pantel ◽  
L. Pibouleau ◽  
S. Domenech
Keyword(s):  
Protein Production ◽  
Plant Design ◽  
Batch Plant ◽  
Environmental Consideration

scholarly journals A Framework for Multiproduct Batch Plant Design with Environmental Consideration: Application to Protein Production

2004 ◽  
Vol 37 (3) ◽  
pp. 583-588
Author(s):  
A. Dietz ◽  
C. Azzaro-Pantel ◽  
A. Davin ◽  
L. Pibouleau ◽  
S. Domenech
Keyword(s):  
Protein Production ◽  
Plant Design ◽  
Batch Plant ◽  
Environmental Consideration

scholarly journals Modelling and Prediction of Compressive Strength of Hydraulic Concrete Structure in Multiproduct Batch Plant Design for Protein Production using Extreme Gradient Boosting Regressor with Grid Search support

2021 ◽  
Keyword(s):  
Compressive Strength ◽  
Concrete Structure ◽  
Protein Production ◽  
Gradient Boosting ◽  
Grid Search ◽  
Plant Design ◽  
Light Gradient ◽  
Batch Plant ◽  
Extreme Gradient Boosting ◽  
Investment Cost

This work deals with the problem of modeling and prediction of compressive strength of concrete structure in multiproduct batch plant design of protein production found in a chemical engineering process with uncertain demand. Modeling the strength of concrete for this process is very complex. However, it can be solved by minimizing the investment cost. Therefore, the aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using extreme gradient boosting regressor with grid search support (XGBoost), could be interpreted as an optimization algorithm on a suitable cost function, which take into account, the uncertainty on the demand using gaussian process modeling. The results about number and size of equipment’s, investment cost, production time, process time and idle times in plant obtained by light gradient boosted trees regressor are the best. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.

scholarly journals Optimization of Multiproduct Batch Plant Design for Protein Production Using Gradient Boosting Algorithms

2021 ◽  
Keyword(s):  
Protein Production ◽  
Chemical Engineering ◽  
Decision Makers ◽  
Gradient Boosting ◽  
Process Time ◽  
Plant Design ◽  
Modeling And Prediction ◽  
Batch Plant ◽  
Investment Cost ◽  
Boosting Algorithms

This work deals with the problem of modeling and prediction of compressive strength of concrete structure in multiproduct batch plant design of protein production found in a chemical engineering process with uncertain demand. Modeling the strength of concrete for this process is very complex. However, it can be solved by minimizing the investment cost. Therefore, the aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using gradient boosting algorithms, could be interpreted as an optimization algorithm on a suitable cost function. Which take into account, the uncertainty on the demand using gaussian process modeling. The results about number and size of equipments investment cost, production time, process time and Idle times in plant obtained by gradient boosting algorithms are the best. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.

Multiobjective Multiproduct Batch Plant Design Under Uncertainty: Application to Protein Production

2009 ◽  
pp. 1101-1106 ◽  
Author(s):  
Alberto Aguilar-Lasserre ◽  
Rubén Posada-Gómez ◽  
Giner Alor-Hernández ◽  
Guilllermo Cortés-Robles ◽  
Constantino Moras-Sánchez ◽  
...  
Keyword(s):  
Protein Production ◽  
Design Under Uncertainty ◽  
Plant Design ◽  
Batch Plant

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

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Adrian Dietz ◽  
Catherine Azzaro Pantel ◽  
Luc Guy Pibouleau ◽  
Serge Domenech
Keyword(s):  
Genetic Algorithm ◽  
Discrete Event ◽  
Operating Conditions ◽  
Batch Processes ◽  
Stochastic Algorithm ◽  
Plant Design ◽  
Environment Design ◽  
Design Strategies ◽  
Design Variables ◽  
Batch Plant

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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