工业PTA氧化过程的多目标优化

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process model that has been proved to describe industrial process quite well. The model is a semiempirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithm applied in this study is non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ). The performance of NSGA-II is further illustrated by application to the title process.     

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.     

基于多目标模糊优选理论的化合物环境影响指数确定及应用

Environment impact (EI) is a multi- decision-making, multi-objective and multi-criteria system with uncertainties and ambiguities, and environment impact index (EII) of a chemical compound is difficult to calculate appropriately with traditional methods. In this paper, fuzzy optimal selection theory based on the concept of relative importance degree is adopted to determine relativity of the degree of environment impact of chemical compounds as EII, and environment impact assessment (EIA) of a chemical process is done with values of compounds EII to select alternatives that cause less pollution to the environment. It is useful in further process optimization. The paper presents a practical application to the treatment of process condensate in a synthesis ammonia plant to illustrate the procedure, and the calculation result demonstrates the feasibility, practicability, and effectiveness of the suggested method.     

应用群能量恒定粒子群优化算法的批次流加发酵过程批次间优化(英文)

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.     

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.     

一种基于测量的双层动态优化策略及其在青霉素发酵过程中的应用(英文)

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of al kinds of uncertainties. This assumption is not satisfied in some cases and the stan-dard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is il ustrated via a bioreactor in penicil in fermentation process.     

解析导数及稀疏矩阵技术在大规模过程优化命题中的应用

The performance of analytical derivative and sparse matrix techniques applied to a traditional dense sequential quadratic programming (SQP) is studied, and the strategy utilizing those techniques is also presented.Computational results on two typical chemical optimization problems demonstrate significant enhancement in efficiency, which shows this strategy is promising and suitable for large-scale process optimization problems.     

Study of Total Process Energy Integration

1 INTRODUCTIONIt is known that a process system,typically a chemical process system,is composed of subsystemsof reaction,separation,heat recovery and utility with strong interactions among them.In thepast,in order to reduce the complexity of the problem,the decomposition strategy was oftenused in process energy analysis and optimization design.According to the decomposition strategy,analysis and optimization of a system are performed heuristically in several steps which correspond     

表面活性素集成生产过程(Ⅲ)吸附柱的模型

The study is focused on modeling of separation process and optimization.An adsorption separation process is simulated.The surfactin production process by Bacillus subtilis ATCC 21332 followed by surfactin adsorption in a fixed-bed column packed with commercial active carbon is studied in laboratory.The adsorption column achieves high surfactin recovery(94%)by up-flow methanol elution at 25°C.The adsorption column is simulated with a complex one-dimensional plug flow dispersion model coupled with nonlinear adsorption equilibrium,based on the assumption that the adsorption of surfactin is monomolecular layer and no micelle is formed.The molecular diffusion coefficient of surfactin in water solution with electric neutrality is estimated to be 0.428×10 -5 cm 2 ·s -1 by molecular dynamics simulation.The model developed can describe the complex interplay of adsorption kinetics,fluid dynamics,and mass-transfer phenomena based on the assumption of no radial temperature and concentration gradients,and is of adequate precision.The work involved in this paper is valuable for the optimization of the production process of surfactin.     

甘油生物歧化为1,3-丙二醇过程的优化控制

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.     

A process performance index and its application to optimization of the rtm process

Resin transfer molding (RTM) is a promising manufacturing process for hig formance composite materials. However, the fact that RTM process design has traditionally been an expensive, time‐consuming trial‐and‐error procedure has p ited its wide application base. This paper proposes a solution to that problem—a simulation‐based optimum process design scheme for RTM. This scheme ei engineers to determine the optimum locations of injection gates and vents so both process efficiency and high part quality can be ensured. Essential to this mum process design scheme is a process performance index, which is defined respect to the major factors influencing RTM process efficiency and part quality This index is then used as the objective function for the RTM process design optimization model. Gate and vent locations are the process design parameters optimized. All data is obtained by running an RTM simulation program, and th netic algorithm (GA) is employed to carry out the optimization procedure for design parameters. It is found that constant pressure optimization will yi process with a short flow path, whereas constant flow optimization will yield process with smooth and vent‐oriented flow pattern. Although there is no dry factor in the objective function, it is interesting to note that both constant pres and constant flow optimization procedures result in process designs with a mil mum probability of dry spot formation. This study finds that, in general, cons flow optimization should be employed if injection pressure is not a major cone otherwise, constant pressure optimization should be used. Two case studies presented to illustrate the efficacy of this approach.     

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

This article presents an integrated, simulation‐based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn‐around time and adequate accuracy for substituting time‐consuming computer simulations during system‐level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684–694, 2007. © 2007 Society of Plastics Engineers.     

Optimization of Methanol Synthesis Loops with Quench Reactors

An optimization procedure for increasing the methanol production in synthesis loops with quench reactors is presented. The process decision variables are represented by a set of split fractions of the reactor feed to the catalyst beds. The optimization routine is coupled to a steady‐state model of the methanol synthesis loop. Numerical results illustrate the performance of the proposed optimization procedure, presenting an example, where a considerable increase in methanol production can be reached after the application of the optimization procedure.     

WSA过程的数据挖掘系统设计与应用

提出了一种建立于实时数据库基础上的稳态数据挖掘实施流程,并应用于某冶炼厂的湿法烟气制酸(WSA)过程实现数据挖掘.所建立的系统可在线或离线运行,能针对特定需求实现过程数据的自动预处理和分析,为技术人员提供有效的操作参数优化辅助信息.该实施流程可推广应用于一般工业过程数据挖掘系统的建立.     

Crystallization process optimization using artificial neural networks

This paper presents a new procedure for optimization of continuous mixed suspensionmixed product removal (MSMPR) crystallizing systems. Owing to the difficulties of theoretical modelling, simulation of the MSMPR crystallization process is based on the use of artificial neural networks (ANN). The optimization criterion is a compound objective function corresponding to an intended mean crystal size dimension and a minimal dispersion. The presence of multiple local minima has called for investigation by several optimization techniques. Ultimately, Luus' and Jaakola's random adaptive method proved to be most effective. The results obtained lend support to the general procedure proposed.     

Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis

Combustion in a boiler is too complex to be analytically described with mathematical models. To meet the needs of operation optimization, on-site experiments guided by the statistical optimization methods are often necessary to achieve the optimum operating conditions. This study proposes a new constrained optimization procedure using artificial neural networks as models for target processes. Information analysis based on random search, fuzzy c-mean clustering, and minimization of information free energy is performed iteratively in the procedure to suggest the location of future experiments, which can greatly reduce the number of experiments needed. The effectiveness of the proposed procedure in searching optima is demonstrated by three case studies: (1) a bench-mark problem, namely minimization of the modified Himmelblau function under a circle constraint; (2) both minimization of NO_x and CO emissions and maximization of thermal efficiency for a simulated combustion process of a boiler; (3) maximization of thermal efficiency within NO_x and CO emission limits for the same combustion process. The simulated combustion process is based on a commercial software package CHEMKIN, where 78 chemical species and 467 chemical reactions related to the combustion mechanism are incorporated and a plug-flow model and a load-correlated temperature distribution for the combustion tunnel of a boiler are used.     

Freeze drying process: real time model and optimization

Freeze drying is a separation process based on the sublimation phenomenon. This process has the following advantages compared to the conventional drying process: the material structure is maintained, moisture is removed at low temperature (reduced transport rates), product stability during the storage is increased, the fast transition of the moisturized product to be dehydrated minimizes several degradation reactions. Freeze drying process has not been studied well enough. In order to put it to practice, a mathematical model based on fundamental mass and energy balance equations has been developed, based on a deterministic mathematical model proposed by Liapis and Sadikoglu [Drying Technol. 15 (3–4) (1997) 791], and used to calculate the amount of removed water and amount of residual water. The proposed model contains the freeze drying equations, which are solved by the orthogonal collocation and polynomial approximation—Jacobi method. The results show that the dynamic mathematical model represents well the process and is especially well suited for real time optimization. As a case study to illustrate the model utilization in a real time optimization procedure, the freeze drying process was optimized by the method of Successive Quadratic Programming (SQP) used for solution of non-linear equations, for skimmed milk and soluble coffee. The optimization procedure showed to be an important tool to improve the process performance since lower energy consumption and hence lower cost has been achieved to obtain the product with the same quality.     

Dynamic optimization of semibatch vinyl acetate/acrylic acid suspension copolymerizations

This work performs the dynamic optimization of semibatch vinyl acetate (VAc)/acrylic acid (AA) suspension copolymerizations. The proposed dynamic optimization strategy is based on a direct search Complex algorithm and is used to control the copolymer composition along the batch. First, a sequential optimization procedure is used to determine the optimum AA concentration and feed rate profiles, required to provide the specified copolymer composition. In the second step, a sequential optimization procedure is coupled with a predictive controller to guarantee that the manipulation of feed flow rates can allow for attainment of the desired copolymer compositions. The optimization strategy is validated through simulation, by assuming that reactions are subject to perturbations of the reaction temperature, initiator, and VAc concentrations. It is shown that the proposed optimization strategy can be used successfully both for design of monomer feed rate profiles and removal of process disturbances during semibatch suspension copolymerizations, to keep the copolymer composition constant throughout the batch. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers     

On the costs of parameter uncertainties. Part 2: Impact of EVOP procedures on the optimization and design of experiments

Pinto (1998) showed that an economical value might be assigned to model parameter uncertainties, which could be used for process optimization and for taking decisions during sequential experimental designs. The procedure is extended to take into account the fact that plant operation conditions change with time, leading to improvements of the plant operation and allowing the partial recovering of losses introduced by uncertain parameters during plant design. It is shown that the use of EVOP (Evolutionary Operation Procedures) during plant operation may reduce the cost of parameter uncertainties, exerting a major impact on process optimization and optimal design of experiments.     

Intelligent injection molding: Parameters self-learning optimization using iterative gradient-approximation adaptive method

Intelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self-learning parameter optimization method named iterative gradient-approximation adaptive optimization (IGAO) method, which adopts the batch-to-batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation. The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.