Hydrogen network management is important to refineries. Various systematic management techniques have been developed to improve the efficiency of refinery hydrogen networks. However, existing methods all treat the hyd...Hydrogen network management is important to refineries. Various systematic management techniques have been developed to improve the efficiency of refinery hydrogen networks. However, existing methods all treat the hydrogen network separately. The tradeoff between hydrogen network cost and oil processing network benefit has not been explored in the hydrogen network management yet. A novel sensitivity analysis scheme is presented to take oil processing network into consideration. Oil processing unit which is sensitive to both oil processing networks and hydrogen networks is identified first. Then, sensitivity analysis of the unit around the operating point of oil processing networks and hydrogen networks is carried out. Finally, the overall optimal operating condition is obtained. An example of a real Chinese refinery demonstrates the effectiveness of the proposed analysis method.展开更多
The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-obje...The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.展开更多
Understanding the holistic relationship between refinery production scheduling(RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineeri...Understanding the holistic relationship between refinery production scheduling(RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling can impact operations in the high-performing chemical process industry. We conclude that, more than any traditional off-the-shelf RPS solution available today, flexible and integrative specialized modeling platforms will be increasingly necessary to perform decentralized and collaborative optimizations,since they are the technological alternatives closer to the advanced manufacturing philosophy.展开更多
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 w...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.展开更多
The empirical Complex Model developed by the US Environmental Protection Agency （EPA） is used by refiners to predict the toxic emissions of reformulated gasoline with respect to gasoline properties. The difficulty i...The empirical Complex Model developed by the US Environmental Protection Agency （EPA） is used by refiners to predict the toxic emissions of reformulated gasoline with respect to gasoline properties. The difficulty in implementing this model in the blending process stems from the implicit definition of Complex Model through a series of disjunctions assembled by the EPA in the form of spreadsheets. A major breakthrough in the refinery-based Complex Model implementation occurred in 2008 and 2010 through the use of generalized disjunctive and mixed- integer nonlinear programming （MINLP）. Nevertheless, the execution time of these MINLP models remains prohibitively long to control emissions with our online gasoline blender. The first objective of this study is to present a new model that decreases the execution time of our online controller. The toxic thresholds as hard second objective is to consider constraints to be verified and search for blends that verify them. Our approach introduces a new way to write the Complex Model without any binary or integer variables. Sigmoid functions are used herein to approximate step functions until the measurement precision for each blend property is reached. By knowing this level of precision, we are able to propose an extremely good and differentiable approximation of the Complex Model. Next, a differentiable objective function is introduced to penalize emission values higher than the threshold emissions. Our optimization module has been implemented and tested with real data. The execution time never exceeded 1 s, which allows the online regulation of emissions the same way as other traditional properties of blended gasoline.展开更多
基金financial supported by National Natural Science Foundation of China(No.20409205 & 60421002)National High Technology Research and Development Program of China(No.2007AA04Z191 & 2007AA040702)
文摘Hydrogen network management is important to refineries. Various systematic management techniques have been developed to improve the efficiency of refinery hydrogen networks. However, existing methods all treat the hydrogen network separately. The tradeoff between hydrogen network cost and oil processing network benefit has not been explored in the hydrogen network management yet. A novel sensitivity analysis scheme is presented to take oil processing network into consideration. Oil processing unit which is sensitive to both oil processing networks and hydrogen networks is identified first. Then, sensitivity analysis of the unit around the operating point of oil processing networks and hydrogen networks is carried out. Finally, the overall optimal operating condition is obtained. An example of a real Chinese refinery demonstrates the effectiveness of the proposed analysis method.
基金Supported by the National High Technology Research and Development Program of China （2008AA042902, 2009AA04Z162）, the Program of Introducing Talents of Discipline to University （B07031） and the National Natural Science Foundation of China （21106129）.
文摘The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.
文摘Understanding the holistic relationship between refinery production scheduling(RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling can impact operations in the high-performing chemical process industry. We conclude that, more than any traditional off-the-shelf RPS solution available today, flexible and integrative specialized modeling platforms will be increasingly necessary to perform decentralized and collaborative optimizations,since they are the technological alternatives closer to the advanced manufacturing philosophy.
基金supported by the Ontario Research FoundationMc Master Advanced Control ConsortiumImperial Oil
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.
基金financial support from TOTAL Refining and Chemicals
文摘The empirical Complex Model developed by the US Environmental Protection Agency （EPA） is used by refiners to predict the toxic emissions of reformulated gasoline with respect to gasoline properties. The difficulty in implementing this model in the blending process stems from the implicit definition of Complex Model through a series of disjunctions assembled by the EPA in the form of spreadsheets. A major breakthrough in the refinery-based Complex Model implementation occurred in 2008 and 2010 through the use of generalized disjunctive and mixed- integer nonlinear programming （MINLP）. Nevertheless, the execution time of these MINLP models remains prohibitively long to control emissions with our online gasoline blender. The first objective of this study is to present a new model that decreases the execution time of our online controller. The toxic thresholds as hard second objective is to consider constraints to be verified and search for blends that verify them. Our approach introduces a new way to write the Complex Model without any binary or integer variables. Sigmoid functions are used herein to approximate step functions until the measurement precision for each blend property is reached. By knowing this level of precision, we are able to propose an extremely good and differentiable approximation of the Complex Model. Next, a differentiable objective function is introduced to penalize emission values higher than the threshold emissions. Our optimization module has been implemented and tested with real data. The execution time never exceeded 1 s, which allows the online regulation of emissions the same way as other traditional properties of blended gasoline.
