工业流化床乙烯共聚合生产过程中牌号切换的最优化研究

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process.The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables,such as polymerization temperature,the ratio of hydrogen to ethylene,the ratio of co-monomer to ethylene,feed rate of catalyst,and bed level.A new performance index,the ratio of melt flow(MFR),is included in the objective function,for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resion.It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product.This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variables due to the non-linearity of the system.The grade transition model is extended to a high space time yield(STY)Process with the so-called condensed model operation.In the end,an optimization strategy for multi-product transition is proposed with two-level optimization of the objective function J(x,u) on the the basis of the optimal grade transition model.A sequential transition of six commercial polyethylene grades in illustrated for an optimal multi-product operation.     

通过模型为基础的溶液选择来调控药品化合物的形貌

In this paper we present a strategy for tuning the crystal morphology of pharmaceutical compounds by the appropriate choice of solvent via an optimization model. A three-stage approach involving a pre-design stage, a product design stage and a post-design experimental verification stage is presented. The pre-design stage addresses the tormulation of the property constraint tor crystal morphology. This involves crystallization experiments aria development of property models and constraints for morphology. In the design stage various property requirements for the solvent along with crystal morphology are considered and the product design problem is formulated as a mixed integer nonlinear programming model.The design stage provides an optimal solvent/list of candidate solvents. Similar to the pre-design stage, in the post design experimental verification stage, the morphology of the crystals （precipitated from the designed solvent） is verified through crystallization experiments followed by product characterization via scanni＇ng electron microscopy, powder X-ray diffraction imaging and Fourier transform spectra analysis.     

化工过程设计的多目标不确定优化研究

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.     

A systematic approach for synthesizing a low-temperature distillation system

In this paper, by combining a stochastic optimization method with a refrigeration shaft work targeting method, an approach for the synthesis of a heat integrated complex distillation system in a low-temperature process is presented. The synthesis problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by simulated annealing algorithm under a random procedure to explore the optimal operating parameters and the distillation sequence structure. The shaft work targeting method is used to evaluate the minimum energy cost of the corresponding separation system during the optimization without any need for a detailed design for the heat exchanger network (HEN) and the refrigeration system (RS). The method presented in the paper can dramatical y reduce the scale and complexity of the problem. A case study of ethylene cold-end separation is used to il ustrate the application of the approach. Compared with the original industrial scheme, the result is encouraging.     

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.     

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.     

Study on Optimal Strategy of Grade Transition in Industrial Fluidized Bed Gas-Phase Polyethylene Production Process

A model of grade transition is presented for a commercialized fluidized bed gas-phase polyethylene production process. The quantity of off-specification product and the time of grade transition can be minimized by the optimization of operating variables, such as polymerization temperature, the ratio of hydrogen to ethylene, the ratio of co-monomer to ethylene, feed rate of catalyst, and bed level. A new performance index, the ratio of melt flow (MFR), is included in the objective function, for restraining the sharp adjustment of operation variables and narrowing the distribution of molecular weight of the resin. It is recommended that catalyst feed rate and bed level are decreased in order to reduce the grade transition time and the quantity of off-specification product. This optimization problem is solved by an algorithm of sequential quadratic programming (SQP) in MATLAB. There is considerable difference between the forward transition and reverse transition of grade with regard to the operating variab     

Simultaneous optimization of heat-integrated crude oil distillation systems☆

Crude oil distil ation is important in refining industry. Operating variables of distil ation process have a critical ef-fect on product output value and energy consumption. However, the objectives of minimum energy consumption and maximum product output value do not coordinate with each other and do not lead to the maximum eco-nomic benefit of a refinery. In this paper, a systematic optimization approach is proposed for the maximum an-nual economic benefit of an existing crude oil distil ation system, considering product output value and energy consumption simultaneously. A shortcut model in Aspen Plus is used to describe the crude oil distillation and the pinch analysis is adopted to identify the target of energy recovery. The optimization is a nonlinear program-ming problem and solved by stochastic algorithm of particle warm optimization.     

A LCA Based Biofuel Supply Chain Analysis Framework

This paper presents a life cycle assessment （LCA） based biofuel supply chain （SC） analysis framework which enables the study of economic, energy and environmental （3E） performances by using multi-objective opti-mization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy （FE） inputs per MJ biofuel and the environmental objective is measured by greenhouse gas （GHG） emissions per MJ biofuel. A multi-objective linear fractional programming （MOLFP） model with multi-conversion pathways is formulated based on the framework and is solved by using theε-constraint method. The MOLFP prob-lem is turned into a mixed integer linear programming （MILP） problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network （SCN）. As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.     

化工过程分离循环系统的多目标模糊优化研究

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.     

Multi‐Objective Optimization of Interior Ballistic Performance Using NSGA‐II

This paper investigates an interior ballistic design with equal and unequal web thicknesses of seven‐perforation propellant grains using optimization methods. In order to reveal the influence of the web thickness of the propellant grains on the overall interior ballistic performance, burning seven‐perforation propellant grains with both equal and unequal web thickness is modeled. A currently popular evolution algorithm (EA) is used to compare two charge shapes, and to seek which one could achieve the optimal ballistic performance. Complete optimization of the interior ballistic performance is a complex process in view of the conflicting objectives to be achieved and a solution to such problems is sought by converting them into a single composite objective and using many tedious measurements. In this paper, a true multi‐objective optimization of the interior ballistic charging design is carried out by considering three objectives simultaneously. The non‐dominated sorting genetic algorithm version II (NSGA‐II) is used to solve this multi‐objective optimization problem (MOP). In order to check its implementation, both the conventional optimization algorithm‐hill climbing method (HCM) and NSGA‐II are used to solve the same single objective problem. The NSGA‐II used to capture the full Pareto‐optimal front is capable of identifying the trade‐off among the conflicting objectives thereby providing alternative useful designs for a designer. Furthermore, for seven‐perforation propellant grains, the results of using equal web thickness are compared with those of unequal web thickness, and it is shown that the two charge shapes produce no distinct difference in the interior ballistic performance.     

Using NSGA‐II and TOPSIS Methods for Interior Ballistic Optimization Based on One‐Dimensional Two‐Phase Flow Model

In order to meet the design demands of new gun systems or new types of projectiles, the interior ballistic charge design seems especially important. In this paper, a one‐dimensional two‐phase flow model is presented. The model describes the transient combustion of granular propellants in a gun, and pressure waves are considered as an objective. This study adopts a hybrid method to solve the problem. In the first stage, the non‐dominated sorting genetic algorithm (NSGA‐II) with “a “filter” is employed to approximate a set of Pareto‐optimal solutions. In the subsequent stage, a multi‐attribute decision‐making (MADM) approach is adopted to rank these solutions from the best to the worst. The ranking of Pareto‐optimal solutions is based on the technique ordered preference by similarity to ideal solution (TOPSIS) method. In TOPSIS method each objective needs a corresponding weight coefficient, and a practical problem is introduced. Both the entropy method and linear analysis method are adopted to get two sets of weights for the objectives, respectively. The two pairs of final, best compromise solutions are compared for satisfying the designer’s aim. For the analysis of the results, a two‐phase flow interior ballistic model for design optimization is established, and the hybrid approach could get a reasonable design scenario.     

Integration of Design and Control: A Robust Control Approach Using MPC

This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.     

LMI-based robust model predictive control for a continuous MMA polymerization reactor

A linear matrix inequality (LMI)-based robust model predictive control (MPC) is applied to a continuous stirred-tank reactor for the polymerization of methyl methacrylate (MMA). The polytopic model is constructed to predict the responses to various control input sequences by using Jacobians of uncertain nonlinear model at several operating points and the controller design is characterized as the problem of minimizing an upper bound on the ‘worst-case’ infinite horizon objective function subject to constraints on the control input and plant output. Simulation studies under different conditions are conducted to validate the feasibility of the optimization problem and evaluate the applicability of such a control scheme. Simulation results show that, despite the model uncertainty, the LMI-based robust model predictive controller performs quite satisfactorily for the property control of the continuous polymerization reactor and guarantees the robust stability.     

混合装药内弹道性能优化及应用

为了研究混合装药结构对内弹道性能的影响,以某舰炮为工程背景,提出内弹道多目标优化设计方案,研究了目标函数、设计变量及约束条件.采用e-约束法对次要目标函数进行简化处理,利用当前流行的进化算法—遗传算法进行优化计算,并针对内弹道数学模型特性,对算法进行改进.最后,通过进化计算得到的优化方案为一组非劣解集,同时验证了该改进...     

Computer‐Aided Molecular Design of Environmentally Friendly Solvents for Separation Processes

This review paper presents an optimization technology for the computer‐aided molecular design of environmentally friendly solvents. The approach combines a stochastic optimization method and group contribution methods (GC‐methods) to design solvents with desirable physicochemical and environmental properties. A simulated annealing algorithm is used to investigate feasible molecular structures. The main objective method is adopted to balance the multi‐objective functions. One property is chosen as the main objective function, while the other properties are considered as constraints, and thus, the multi‐objective problem is transformed into a single objective one. The optimal solution is a set of molecules satisfying the formulated target. The properties of each molecule are evaluated through GC‐methods, including pure component properties, mixture properties and environmentally properties. Finally, the proposed methodology is illustrated with several examples of industrial separations.     

Impact of Fuel Cell Power Plants on Multi‐objective Optimal Operation Management of Distribution Network

This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO‐DE) to solve the multi‐objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi‐objective problem by assuming that the decision‐maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

基于KPLS模型的间歇过程产品质量控制

针对间歇过程所具有的非线性特性,提出了一种基于核偏最小二乘(KPLS)模型的最终产品质量控制策略。利用初始条件、批次展开后的过程数据以及最终产品质量建立了间歇过程的KPLS模型;采用基于主成分分析(PCA)映射的预估方法对未知的过程数据进行补充,实现了最终产品质量的在线预测。为了解决最终产品质量的控制,利用T2统计量确定KPLS模型的适用范围,并作为约束引入产品质量控制问题,提高控制策略的可行性;采用粒子群优化(PSO)算法实现了优化问题的高效求解。仿真结果表明,与基于偏最小二乘(PLS)模型的控制策略相比,所提出的方法具有更高的预测精度,且能有效解决产品质量控制中出现的各种问题。     

基于实验设计的L-谷氨酸连续结晶过程优化

对于采用连续振荡挡板式结晶器进行β型L-谷氨酸的冷却结晶,提出一种基于实验设计的晶体产品尺寸分布预测模型和过程操作条件优化方法。将连续管式结晶器的振荡频率、2个区段的降温速率作为操作变量,设计一个包含3因素3水平的响应面实验方案。通过检测不同实验条件下的晶体产品弦长分布,构建一个基于双层基函数的晶体产品尺寸分布预测模型。然后引入一个关于目标晶体尺寸分布和产品收率的目标函数,并且利用上述预测模型,建立一个优化过程操作条件的方法。通过对β型L-谷氨酸冷却结晶过程的仿真和实验,验证了该方法的有效性和优点。