炼厂生产计划优化模型

This article addresses a production planning optimization problem of overall refinery. The authors formulated the optimization problem as mixed integer linear programming. The model considers the main factors for optimizing the production plan of overall refinery related to the use of run-modes of processing units. The aim of this planning is to decide which run-mode to use in each processing unit in each period of a given horizon, to satisfy the demand, such as the total cost of production and inventory is minimized. The resulting model can be regarded as a generalized lot-sizing problem where a run-mode can produce and consume more than one product. The resulting optimization problem is large-sized and NP-hard. The authors have proposed a column generation-based algorithm called branch-and-price （BP） for solving the interested optimization problem. The model and implementation of the algorithm are described in detail in this article. The computational results verify the effectiveness of the proposed model and the solution method.     

不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

A novel two-stage Lagrangian decomposition approach for refinery production scheduling with operational transitions in mode switching☆

To address large scale industrial processes, a novel Lagrangian scheme is proposed to decompose a refinery scheduling problem with operational transitions in mode switching into a production subproblem and a blending and delivery subproblem. To accelerate the convergence of Lagrange multipliers, some auxiliary constraints are added in the blending and delivery subproblem. A speed-up scheme is presented to increase the efficiency for solving the production subproblem. An initialization scheme of Lagrange multipliers and a heuristic algorithm to find feasible solutions are designed. Computational results on three cases with different lengths of time hori-zons and different numbers of orders show that the proposed Lagrangian scheme is effective and efficient.     

炼油厂氢气网络建模与多目标优化(英文)

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.     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

间歇过程的故障分析和互锁控制器综合的集成

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of common models used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batch processes is presented using information about fault propagation and the possibilities of integration of fault analysis and controller synthesis are discussed on the basis of the Petri net based common models.     

Automated synthesis of steady-state continuous processes using reinforcement learning

Automated flowsheet synthesis is an important field in computer-aided process engineering.The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design.The environment consists of a steady-state flowsheet simulator that contains all physical knowledge.An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem.A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem.Therein,flowsheet synthesis is modelled as a game of two competing players.The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning.The method is applied successfully to a reaction-distillation process in a quaternary system.     

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries.However,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP) problem.Considering the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP) strategy is used to solve the problem.This paper uses the linear blending rules plus the...     

废物最小化的过程集成方法及多目标优化

By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.     

Application of response surface methodology for optimization of purge gas recycling to an industrial reactor for conversion of CO2 to methanol

Nowadays, by the increasing attention to environment and high rate of fuel production, recycling of purge gas as reactant to a reactor is highly considered. In this study, it is proposed that the purge gases of methanol production unit, which are approximately15.018 t·h~(-1) in the largest methanol production complexes in the world, can be recycled to the reactor and utilized for increasing the production rate. Purge gas streams contain 63% hydrogen,20% carbon monoxide and carbon dioxide as reactants and 17% nitrogen and methane as inert. The recycling effect of beneficial components on methanol production rate has been investigated in this study. Simulation results show that methanol production enhances by recycling just hydrogen, carbon dioxide and carbon monoxide which is an effective configuration among the others. It is named as Desired Recycle Configuration(DRC) in this study. The optimum fraction of returning purge gas is calculated via one dimensional modeling of process and Response Surface Methodology(RSM) is applied to maximize the methanol flow rate and minimize the carbon dioxide flow rate. Simulation results illustrate that methanol flow rate increases by 0.106% in DRC compared to Conventional Recycle Configuration(CRC) which therefore shows the superiority of applying DRC to CRC.     

乙烯-1,3,5-三甲苯的气液相平衡与乙烯回收工艺的流程模拟(英文)

The amount of ethylene in refinery off-gas is high with a mass fraction of 20%,but the refinery off-gas is usually used as fuel gas in most refineries.The separation and recovery of ethylene is of remarkable significance for saving energy and reducing carbon dioxide emission.The aim of this paper is to use a novel absorbent mesitylene for the ethylene absorption process and assess its application feasibility through the ethylene + mesitylene vapor-liquid equilibrium data measurement and its binary interaction parameter correlation,as well as the simulation for ethylene separation process.     

一种针对原油调合过程的新型调度方法(英文)

For those refineries which have to deal with different types of crude oil, blending is an attractive solution to obtain a quality feedstock. In this paper, a novel scheduling strategy is proposed for a practical crude oil blending process. The objective is to keep the property of feedstock, mainly described by the true boiling point (TBP) data, consistent and suitable. Firstly, the mathematical model is established. Then, a heuristically initialized hybrid iterative (HIHI) algorithm based on a two-level optimization structure, in which tabu search (TS) and differential evolution (DE) are used for upper-level and lower-level optimization, respectively, is proposed to get the model solution. Finally, the effectiveness and efficiency of the scheduling strategy is validated via real data from a certain refinery.     

Data cleaning method for the process of acid production with flue gas based on improved random forest

Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring, optimal control, and fault diagnosis.However, the operating environment of acid production with flue gas is complex and there is much equipment. The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers. Therefore, to solve the problem of abnormal data in the ...     

甲醇营养型重组毕赤酵母高效表达外源蛋白过程的控制与优化(英文)

The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts have been made to increase heterologous protein productivity by P. pastoris in recent years. When new engineered yeast strains are constructed and are ready to use for industrial protein production, process control and optimization techniques should be applied to improve the fermentation performance in the following aspects: (1) increase recombinant cell concentrations in fermentor to high density during growth phase; (2) effectively induce heterologous proteins by enhancing/stabilizing titers or concentrations of the proteins during induction phase; (3) decrease operation costs by relieving the working loads of heat-exchange and oxygen supply. This article reviews and discusses the key and commonly used techniques in heterologous protein production by P. pastoris, with the focus on optimizations of fermentation media and basic operation conditions, development of optimal glycerol feeding strategies for achieving high density cultivation of P. pastoris and effective heterologous protein induction methods by regulating specific growth rate, methanol concentration, temperatures, mixture ratio of multi-carbon substrates, etc. Metabolic analysis for recombinant protein production by P. pastoris is also introduced to interpret the mechanism of sub-optimal heterologous protein production and to explore further optimal expression methods.     

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy al constraints while meeting demand requirement of packed products from various product fam-ilies. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore, we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromo-somes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to de-termine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for com-parison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, al heuristics show the capability to solve large instances within reason-able computational time. In al problem instances, genetic algorithm averagely outperforms ant colony optimiza-tion and Tabu search with slightly longer computational time.     

Upgrading Siberian (Russia) crude oil by hydrodesulfurization: Kinetic parameter estimation in a trickle-bed reactor

Hydrodesulfurization(HDS) of sour crude oil is an effective way to address the corrosion problems in refineries and is an economic way to process sour crude oil in an existing refinery built for sweet oil.Siberian crude oil transported through the Russia-China pipeline could be greatly sweetened and could be refined directly in local refinery designed for Daqing crude oil after the effective HDS treatment.In this study,the HDS of Siberian crude oil was carried out in a continuous flow isothermal trickle-bed reactor over Ni-Mo/γ-Al_2 O_3.The effects of temperature,pressure and LHSV were investigated in the ranges of 320-360℃,3-5 MPa and 0.5-2 h~(-1),keeping constant hydrogen to oil ratio at 600 L·L~(-1).The HDS conversion could be up to 92.89% at the temperature of 360℃, pressure of 5 MPa,and LHSV of 0.5 h~(-1), which is sufficient for local refineries(84%).A three phase heterogeneous model was established to analyze the performance of the trickle-bed reactor based on the two-film theory using Langmuir-Hinshelwood mechanism.The order of sulfur component is estimated as 1.28,and the order of hydrogen is 0.39.By simulating the reactor using the established model,the concentration of H_2, H_2 S and sulfur along the catalyst bed is discussed.The model is significantly useful for industrial application with respect to reactor analysis,optimization and reactor design,and can provide further insight of the HDS of Siberian crude oil.     

Hydrodesulfurization and Hydrodenitrogenation Kinetics of a Heavy Gas Oil over NiMo/Al2O3 Catalysts

1 Introduction The increasing demands of high quality and environment-friendly petroleum products require refineries to treat more heavy oil and residue in the worldwide. Athabasca bitumen derived heavy gas oil(HGO) is a typical feedstock comprising high nitrogen and sulfur heteroatomic compounds, which results in many problems in utilization, such as reducing the stability of fuels, causing storage problems and air pollution. So they need to be removed before being treated in the following u…     

Effect of endogenous hydrogen utilization on improved methane production in an integrated microbial electrolysis cell and anaerobic digestion: Employing catalyzed stainless steel mesh cathode

Improving the production of methane,while maintaining a significant level of process stability,is the main challenge in the anaerobic digestion process.Recently,microbial electrolysis cell (MEC) has become a promising method for CO2 reduction produced during anaerobic digestion (AD) and leads to minimize the cost of biogas upgrading technology.In this study,the MEC-AD coupled reactor was used to generate and utilize the endogenous hydrogen by employing biocompatible electrodeposited cobalt-phosphate as catalysts to improve the performance of stainless steel mesh and carbon cloth electrodes.In addition,the modified version of ADM1 model (ADM1da) was used to simulate the process.The result indicated that the MEC-AD coupled reactor can improve the CH4 yield and production rate significantly.The CH4 yield was enhanced with an average of 48％ higher than the control.The CH4 production rate was also increased 1.65 times due to the utilization of endogenous hydrogen.The specific yield,flow rate,content of CH4,and pH value were the variables that the model was best at predicting (with indexes of agreement:0.960/0.941,0.682/0.696,0.881/0.865,and 0.764/0.743) of the process with SS-meshes 80/SS-meshes 200,respectively.Employing the catalyzed SS mesh cathode,in the MEC-AD coupled reactor,could be an effective approach to generate and facilitate the utilization of endogenous hydrogen in anaerobic digestion of CH4 production technology,which is a promising and feasible method to scale up to the industrial level.     

Integration of a Gas Fired Steam Power Plant with a Total Site Utility Using a New Cogeneration Targeting Procedure

A steam power plant can work as a dual purpose plant for simultaneous production of steam and elec-trical power. In this paper we seek the optimum integration of a steam power plant as a source and a site utility sys-tem as a sink of steam and power. Estimation for the cogeneration potential prior to the design of a central utility system for site utility systems is vital to the targets for site fuel demand as well as heat and power production. In this regard, a new cogeneration targeting procedure is proposed for integration of a steam power plant and a site utility consisting of a process plant. The new methodology seeks the optimal integration based on a new cogenera-tion targeting scheme. In addition, a modified site utility grand composite curve （SUGCC） diagram is proposed and compared to the original SUGCC. A gas fired steam power plant and a process site utility is considered in a case study. The applicability of the developed procedure is tested against other design methods （STAR？ and Thermoflex software） through a case study. The proposed method gives comparable results, and the targeting method is used for optimal integration of steam levels. Identifying optimal conditions of steam levels for integration is important in the design of utility systems, as the selection of steam levels in a steam power plant and site utility for integration greatly influences the potential for cogeneration and energy recovery. The integration of steam levels of the steam power plant and the site utility system in the case study demonstrates the usefulness of the method for reducing the overall energy consumption for the site.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.