Global optimization of large-scale MIQCQPs via cluster decomposition: Application to short-term planning of an integrated refinery-petrochemical complex

Uribe-Rodríguez, Ariel; Castro, Pedro M.; Guillén‐Gosálbez, Gonzalo; Chachuat, Benoît

doi:10.1016/j.compchemeng.2020.106883

Cited by 21 publications

(7 citation statements)

References 59 publications

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“…MIPOPs arise frequently in chemical engineering applications, including pooling and blending (Meyer and Floudas, 2006), water network synthesis and design (Teles et al, 2012), short-term planning of integrated refinery petrochemical complexes (Uribe-Rodriguez et al, 2020), and optimal scheduling of multi-product plants (Castro and Novais, 2009). MIPOPs belong to the class of NP-hard problems, and significant research effort has been devoted to developing global optimization algorithms for MIPOPs or the broader class of MINLPs over the past decades.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation

Karia

Adjiman²,

Chachuat³

2022

Computers & Chemical Engineering

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Section: Introductionmentioning

confidence: 99%

Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation

Karia

Adjiman²,

Chachuat³

2022

Computers & Chemical Engineering

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“…Several global optimization methods for handling bilinear functions are reported in the literature [38,[43][44][45][46][47][48][49]. A common technique uses a spatial branch-and-bound framework [50], which is similar to the class of branch-and-bound methods developed for integer optimization problems (e.g., pure integer linear program (ILP) or MILP [51]) with the main difference in that spatial branch-and-bound methods perform branching on continuous rather than discrete variables.…”

Section: Fractionation Index Nonlinearmentioning

confidence: 99%

Computational Experience with Piecewise Linear Relaxations for Petroleum Refinery Planning

2021

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Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. The computational results showed that applying these schemes improves the relaxation tightness compared to only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It was also seen that there is a limit on the number of partitions that contribute to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relationship between relaxation size and tightness does not always hold for non-uniform partition sizes.

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“…Most of the literature that uses commercial software for refinery production planning, such as RPMS (Refinery and Petrochemical Modeling System) and PIMS (Process Industry Modeling System), is based on very simple models that are mainly composed of linear relations and do not consider more complex process models or nonlinear mixing properties . On the other hand, the nonlinear behavior of refinery processes introduces nonconvexities to the optimization problem . This nonconvex nonlinear optimization problem typically encountered in refineries tends to be easily trapped into local solutions .…”

Section: Introductionmentioning

confidence: 99%

“…6 On the other hand, the nonlinear behavior of refinery processes introduces nonconvexities to the optimization problem. 14 This nonconvex nonlinear optimization problem typically encountered in refineries tends to be easily trapped into local solutions. 15 Hence, some plants operate reactively based on product properties and change the operating conditions to meet the base oil specifications, causing delayed actions resulting in production loss, product quality deterioration, waste generation, and higher energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

Mohd fadzil,

Razali,

Zabiri

2023

Ind. Eng. Chem. Res.

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Base oil is the core of almost any lubricant and is one of the specialty products that can be produced in an oil refinery. Base oil processing is a complex process consisting of multiple units such as the atmospheric distillation unit, the vacuum distillation unit, intermediate tanks, the treating unit, the isomerization unit, the dearomatization unit, and the product fractionation unit. Variations in the properties of the different crude oil feedstocks, combined with the complexity of the integrated processing units with batch/continuous processes, result in a significant challenge in maximizing the base oil production to meet increasing consumer demands. Typical solutions to improve the yield of the base oil adopted in practice are to manually adjust the operating conditions based on past experiences. However, manual adjustment is not trivial, as changing one parameter affects several base oil properties. Thus, only one operating condition is changed at a time, while others remain fixed, resulting in a substantial delay in assessing the impact of the changes made. Consequently, production loss, product quality deterioration, waste generation, and higher energy consumption are inevitable. In addition, most planning analyses reported in the literature tend to assume a fixed yield of the base oil for each grade, regardless of feedstock quality. However, the yield function of the base oil is greatly dependent on the feed properties and operating conditions. Hence, in this paper, an attempt is made to automate the base oil yield maximization of an integrated industrial base oil processing complex via a machine learning-based optimization framework. Five machine learning models are explored: decision tree regression, support vector regression, artificial neural networks, random forest regression, and extreme gradient boosting (XGBoost) to represent the highly integrated industrial base oil processing complex. The XGBOOST models were found to be superior in performance and subsequently introduced as part of optimization functions to the global Bayesian optimization and differential evolution optimization techniques. The resulting machine learning-based optimization framework has been successfully validated during the actual plant trial at the integrated industrial base oil complex from January 2020 to December 2021, and the results show considerable improvement in yield further by 5.24 and 4.48% for both viscosity index optimization cases using Bayesian optimization and differential evolution techniques, respectively, and 4.02 and 2.05% for pour point optimization cases using Bayesian optimization and differential evolution techniques, respectively.

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Global optimization of large-scale MIQCQPs via cluster decomposition: Application to short-term planning of an integrated refinery-petrochemical complex

Cited by 21 publications

References 59 publications

Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation

Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation

Computational Experience with Piecewise Linear Relaxations for Petroleum Refinery Planning

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

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