Operation optimization of hydrocracking process based on Kriging surrogate model

Abstract Hydrocracking is one of the key technologies in oil refining. It has become a critical secondary processing unit in the refinery for improving the quality of product oil and increasing the light oil volume of production. As such, operation optimization for this process is significant. The basis of operation optimization is the model, and several mechanisms for hydrocracking models have been proposed and studied. However, these models usually require time consuming and exhibit low efficiency especially when applied to optimize operating conditions. In this study, a Kriging surrogate model of hydrocracking is developed based on the mechanism and industrial data. An optimization algorithm is then proposed to optimize operating conditions. The proposed algorithm integrates adaptive step-size global and local search strategy (GLSS) for minimizing the predictor. Simulation results indicate that this optimization strategy integrating GLSS and Kriging surrogate model obtains better revenue of the process production than conventional algorithms such as EGO, DDS, and CAND.

[1]  Eric Walter,et al.  An informational approach to the global optimization of expensive-to-evaluate functions , 2006, J. Glob. Optim..

[2]  Benoit Celse,et al.  Hydrotreatment modeling for a variety of VGO feedstocks: A continuous lumping approach , 2015 .

[3]  Kwang Y. Lee,et al.  An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem , 2017 .

[4]  Nirupam Chakraborti,et al.  A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem , 2017 .

[5]  José A. Caballero,et al.  Rigorous design of distillation columns using surrogate models based on Kriging interpolation , 2015 .

[6]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[7]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[8]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[9]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..

[10]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[11]  Robert Piché,et al.  Mixture surrogate models based on Dempster-Shafer theory for global optimization problems , 2011, J. Glob. Optim..

[12]  N. Zheng,et al.  Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models , 2006, J. Glob. Optim..

[13]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[14]  Fred W. Glover,et al.  Scatter Search and Local Nlp Solvers: A Multistart Framework for Global Optimization , 2006, INFORMS J. Comput..

[15]  Rubens Maciel Filho,et al.  Neural network and hybrid model: a discussion about different modeling techniques to predict pulping degree with industrial data , 2001 .

[16]  Bryan A. Tolson,et al.  Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .

[17]  Shapour Azarm,et al.  A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization , 2008 .

[18]  Juergen Hahn,et al.  Modeling and dynamic optimization of fuel-grade ethanol fermentation using fed-batch process , 2014 .

[19]  Deoki N. Saraf,et al.  Hydrocracking: A Review , 1975 .

[20]  Michael Baldea,et al.  Real-time optimization of an industrial steam-methane reformer under distributed sensing , 2016 .

[21]  Du Wenli,et al.  Surrogate model of acetylene hydrogenation reactor based on bootstrap GEI algorithm , 2015 .

[22]  N. M. Alexandrov,et al.  A trust-region framework for managing the use of approximation models in optimization , 1997 .

[23]  Xi Guang.  Aerodynamic Optimization Design for Airfoil Based on Kriging Model , 2005 .

[24]  Maria Pia Fanti,et al.  A simulation and control model for building energy management , 2018 .

[25]  Kenny Q. Ye,et al.  Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .

[26]  Christine A. Shoemaker,et al.  Parallel radial basis function methods for the global optimization of expensive functions , 2007, Eur. J. Oper. Res..

[27]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[28]  N. Cressie.  The origins of kriging , 1990 .

[29]  Vincent Fortin,et al.  Performance of the dynamically dimensioned search algorithm: influence of parameter initialization strategy when calibrating a physically based hydrological model , 2018 .

[30]  Michael D. Shields,et al.  The generalization of Latin hypercube sampling , 2015, Reliab. Eng. Syst. Saf..

[31]  Liping Wang,et al.  Gaussian Process Meta-Models for Efficient Probabilistic Design in Complex Engineering Design Spaces , 2005, DAC 2005.