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您的检索式：作者名="Vladimir Mahalec"
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| 1 | Global Optimization of Nonlinear Blend-Scheduling Problems显示文摘The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation (PMCR) and normalized multiparametric disaggregation tech-nique (NMDT) to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches. | Pedro A.Castillo Castillo Pedro M.Castro Vladimir Mahalec | 2017 | Engineering2017，3，2： | 4 |
| 2 | Impact of crude distillation unit model accuracy on refinery production planning显示文摘In this work, we examine the impact of crude distillation unit(CDU) model errors on the results of refinery-wide optimization for production planning or feedstock selection. We compare the swing cut + bias CDU model with a recently developed hybrid CDU model(Fu et al., 2016). The hybrid CDU model computes material and energy balances, as well as product true boiling point(TBP) curves and bulk properties(e.g., sulfur% and cetane index, and other properties). Product TBP curves are predicted with an average error of 0.5% against rigorous simulation curves. Case studies of optimal operation computed using a planning model that is based on the swing cut + bias CDU model and using a planning model that incorporates the hybrid CDU model are presented. Our results show that significant economic benefits can be obtained using accurate CDU models in refinery production planning. | Gang FU Pedro A.Castillo CASTILLO Vladimir MAHALEC | 2018 | Frontiers of Engineering Management2018，5，2： | 3 |
| 3 | Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units显示文摘This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part.In the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted easily.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products.The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker operation.Moreover,the MISR model has smoother error convergence than the previous model.Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants. | Jiannan Zhu Vladimir Mahalec Chen Fan Minglei Yang Feng Qian | 2023 | Frontiers of Chemical Science and Engineering2023，17，6： | 0 |
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