采用同步策略的化工动态优化求解(英文)

An approach of simultaneous strategies with two novel techniques is proposed to improve the solution accuracy of chemical dynamic optimization problems. The first technique is to handle constraints on control variables based on the finite-element collocation so as to control the approximation error for discrete optimal problems, where a set of control constraints at element knots are integrated with the procedure for optimization leading to a significant gain in the accuracy of the simultaneous strategies. The second technique is to make the mesh refinement more feasible and reliable by introducing length constraints and guideline in designing appropriate element length boundaries, so that the proposed approach becomes more efficient in adjusting elements to track optimal control profile breakpoints and ensure accurate state and control profiles. Four classic benchmarks of dynamic optimization problems are used as illustrations, and the proposed approach is compared with literature reports. The research results reveal that the proposed approach is preferable in improving the solution accuracy of chemical dynamic optimization problem.     

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.     

An improved control vector iteration approach for nonlinear dynamic optimization. II. Problems with path constraints简

This paper considers dealing with path constraints in the framework of the improved control vector iteration （CVI） approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.     

一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.     

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.     

基于浓度间隔分析的用水系统集成(Ⅰ)非传质操作

A strategy for water and wastewater minimization is developed for continuous water utilization systems involving fixed flowrate(non-mass-transfer-based)operations,based on the fictitious operations that is introduced to represent the water losing and/or generating operations and a modified concentration interval analysis(MCIA) technique.This strategy is a simple,nongraphical,and noniterative procedure and is suitable for the quick yields of targets and the identification of pinch point location.Moreover,on the basis of the target method,a heuristic-based approach is also presented to generate water utilization networks,which could be demonstrated to be optimum ones. The proposed approaches are illustrated with example problems.     

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.     

柔性换热网络综合与清洗计划同步优化(英文)

A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network (HEN) by genetic/simulated annealing algorithms (GA/SA). Through taking into account the effect of fouling process on optimal network topology, a preliminary network structure possessing twofold oversynthesis is obtained by means of pseudo-temperature enthalpy (T-H) diagram approach prior to simultaneous optimization. Thus, the computational complexity of this problem classified as NP (Non-deterministic Polynomial)-complete can be significantly reduced. The promising matches resulting from preliminary synthesis stage are further optimized in parallel with their heat exchange areas and cleaning schedule. In addition, a novel continuous time representation is introduced to subdivide the given time horizon into several variable-size intervals according to operating periods of heat exchangers, and then flexible HEN synthesis can be implemented in dynamic manner. A numerical example is provided to demonstrate that the presented strategy is feasible to decrease the total annual cost (TAC) and further improve network flexibility, but even more important, it may be applied to solve large-scale flexible HEN synthesis problems.     

Synthesis of Water Utilization System Using Concentration Interval Analysis Method （ Ⅰ ） Non-Mass-Transfer-Based Operation

A strategy for water and wastewater minimization is developed for continuous water utilization systems involving fixed flowrate（non-mass-transfer-based）operations,based on the fictitious operations that is introduced to represent the water losing and/or generating operations and a modified concentration interval analysis（MCIA） technique.This strategy is a simple,nongraphical,and noniterative procedure and is suitable for the quick yields of targets and the identification of pinch point location.Moreover,on the basis of the target method,a heuristic-based approach is also presented to generate water utilization networks,which could be demonstrated to be optimum ones. The proposed approaches are illustrated with example problems.     

高纯度精馏过程的集成控制与在线优化策略

For high-purity distillation processes, it is difficult to achieve a good direct product quality control using traditional pro-portional-integral-differential (PID) control or multivariable predictive control technique due to some difficulties, such as long re-sponse time, many un-measurable disturbances, and the reliability and precision issues of product quality soft-sensors. In this paper, based on the first principle analysis and dynamic simulation of a distillation process, a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable. Correspondingly, a new strategy with integrated control and on-line optimization is developed, which consists of model predictive control of the split ratio, surrogate model based on radial basis function neural network for optimization, and modified differential evolution optimization algorithm. With the strategy, the process achieves its steady state quickly, so more profit can be obtained. The proposed strategy has been successfully applied to a gas separation plant for more than three years, which shows that the strategy is feasible and effective.     

基于原-对偶内点法的化工过程优化算法

在基于积极集SQP的拟序贯算法研究基础上,提出了基于原-对偶内点法的拟序贯化工过程优化算法。拟序贯算法分为模拟层和优化计算层双层。模拟层中使用正交配置法同时离散状态变量和控制变量,变量的边界约束加于配置点上。同时,每次NLP迭代均求解离散DAE系统,消除等式约束和状态变量,从而减小NLP问题的规模。最新研究表明,在大规模优化问题中内点法相对于积极集SQP算法具有明显优势,因此,优化计算层中用原-对偶内点法来求解NLP问题。使用FORTRAN语言独立编写了整个算法程序,并通过热集成精馏系统最优控制的动态优化问题验证了算法的有效性。结果显示,该算法具有求解大规模动态优化问题的能力。     

受扰双线性系统的近似最优扰动抑制方法

研究具有外界持续扰动作用下双线性系统的最优控制问题.关于二次型性能指标给出了一种设计最优扰动抑制控制律的逐次逼近方法.利用该算法可将在扰动作用下双线性系统的最优控制问题转化为求解一组线性非齐次两点边值序列问题.通过迭代序列得到的最优扰动抑制控制律由解析的线性前馈-反馈项和序列极限形式的非线性补偿项组成.通过截取非线性补偿序列的有限项,可以得到近似最优扰动抑制控制律.仿真结果表明,该方法抑制外部持续扰动的鲁棒性优于经典反馈最优控制.     

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

Two different approaches of the dynamic optimization for chemical process control engineering applications are presented. The first approach is based on discretizing both the control region and the time interval. This method, known as the Region Reduction Strategy (RRS), employs the previous solution in its next iteration to obtain more accurate results. Moreover, the procedure will continue unless the control region becomes smaller than a prescribed value. The second approach is called Control Vector Parameterization (CVP) and appears to have a large number of advantages. In this approach, control action is generated in feedback form, i.e., a set of trial functions of the state variables are expanded by multiplying by some unknown coefficients. By utilizing an optimization method, these coefficients are calculated. The Ant Colony Optimization (ACO) algorithm is employed as an optimization method in both approaches.     

Optimal Temperature and pH Control for a Batch Simultaneous Saccharification and Co-Fermentation Process

Crisp and fuzzy optimization approaches were applied to design an optimal temperature and pH control policy for a batch process of simultaneous saccharification and co-fermentation (SSCF) for ethanol production from lignocellulose, using the enzyme and recombinant strain Zymomonas mobilis ZM (pZB5). To determine an optimal temperature and pH control policy, we applied the Arrhenius relationship to each rate constant to express the temperature and pH effects in the kinetic model for both saccharification and fermentation. The goal of the optimal design was to determine the optimal temperature, pH value, initial lignocellulosic concentration, and fermentation time for maximizing the ethanol productivity under the constraints of the follow-up separation specifications. The interactive crisp and fuzzy optimization methods were applied to solve the trade-off optimization problems for obtaining a compromised design. The fuzzy goal attainment approach obtained a compromised design more flexibly than did the crisp optimization. We also compared the performances for batch and fed-batch SSCF, and used various composition proportions for the batch SSCF to determine a series of optimal designs for the fuzzy goal attainment problem. Batch SSCF was slightly more effective than fed-batch fermentation, and spruce exhibited the maximum productivity because of its higher cellulose and lower hemicellulose contents compared with those of other sources.     

Optimal reaction conditions for the minimization of energy consumption and by‐product formation in a poly(ethylene terephthalate) process

We determined the optimal reaction conditions to minimize the energy cost and the quantities of by‐products for a poly(ethylene terephthalate) process by using the iterative dynamic programming (IDP) algorithm. Here, we employed a sequence of three reactor models: the semibatch transesterification reactor model, the semibatch prepolymerization reactor model, and the rotating‐disc‐type polycondensation reactor model. We selectively chose or developed the reactor models by incorporating experimentally verified kinetic models reported in the literature. We established the model for the entire reactor system by connecting the three reactor models in series and by resolving some joint problems arising when different types of reactor models were interconnected. On the basis of the simulation results of the reactor system, we scrutinized the cause and effect between the reaction conditions and the final quality of the polymer product. Here, we set up the optimization strategy by using IDP on the basis of the integrated reactor model, and the process variables with significant influence on the properties of polymer were selected as control variables with the help of a simulation study. With this method, we could refine the reaction conditions at the end of each iteration step by contracting the spectra of control regions, and the iteration process finally stopped when the profile of the optimal trajectory converged. We also took the constraints on the control variables into account to guarantee polymer quality and to suppress side reactions. Constituting six different strategies by setting weighting vectors differently, we examined the differences in optimal trajectories, the trend of optimality, and the quality of the final polymer product. For each of the strategies, we conducted the optimization to examine whether the number‐average degree of polymerization approached the desired value. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 993–1008, 2002     

A nested, simultaneous approach for dynamic optimization problems-II: the outer problem

In a previous paper (Tanartkit, P. and Biegler, L. T. (1996) A nested, simultaneous approach for dynamic optimization problems - I. Comput. Chem. Eng. 20(6/7), 735–741), we introduced and demonstrated a general framework for solving dynamic optimization using bilevel programming. This framework decouples the element placement from the optimal control procedure and leads to a more robust algorithm. The optimization problem is replaced by two connected but simpler formulations, the inner and outer problems. The inner problem is essentially a dynamic optimization with fixed time steps. On the other hand, the outer problem adjusts the time step given the gradient information from the inner counterpart. By coupling a well-implemented collocation solver with reduced Hessian successive quadratic programming (SQP), we are able to tackle the inner part of the system in an efficient and stable fashion for both initial value and boundary value problems. However, the overall success of the algorithm still depends on robustness and performance of the outer problem. In this article this is achieved by combining a bundle underestimator with SQP. Also included in this article are different options of obtaining subgradients for the outer problem via sensitivity analysis and finite difference schemes. Here a decomposition is presented by taking advantage of the inner problem structure to reduce computational expense of the sensitivity evaluation. We will also address the limitations and properties involved in both schemes. In the final segment of the paper the focus is shifted to the issue of finite element addition. By utilizing insight from optimal control theory, we develop a systematic procedure for element addition with a rigorous stopping criterion. Finally, examples are given to illustrate the effectiveness and potential of the algorithm.     

A quasi-sequential parameter estimation for nonlinear dynamic systems based on multiple data profiles

A three-stage computation framework for solving parameter estimation problems for dynamic systems with multiple data profiles is developed. The dynamic parameter estimation problem is transformed into a nonlinear programming (NLP) problem by using collocation on finite elements. The model parameters to be estimated are treated in the upper stage by solving an NLP problem. The middle stage consists of multiple NLP problems nested in the upper stage, representing the data reconciliation step for each data profile. We use the quasi-sequential dynamic optimization approach to solve these problems. In the lower stage, the state variables and their gradients are evaluated through integrating the model equations. Since the second-order derivatives are not required in the computation framework this proposed method will be efficient for solving nonlinear dynamic parameter estimation problems. The computational results obtained on a parameter estimation problem for two CSTR models demonstrate the effectiveness of the proposed approach.     

Inferential Control of Reactive Destillation Columns – An Algorithmic Approach

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed‐integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed‐integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.