基于混合微分进化算法的生化过程动态参数估计(英文)

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.     

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.     

一种基于状态估计的间歇过程模型实时更新预测控制策略简

Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies： a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim- plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.     

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.     

Optimization of rice wine fermentation process based on the simultaneous saccharification and fermentation kinetic model☆

Chinese rice wine making is a typical simultaneous saccharification and fermentation(SSF) process.During the fermentation process,temperature is one of the key parameters which decide the quality of Chinese rice wine.To optimize the SSF process for Chinese rice wine brewing,the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model.The kinetic parameters as a function of temperature were evaluated using the software Origin8.0.Combing these functions with the mathematical model,an appropriate form of the model equations for the SSF considering the effects of temperature were developed.The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model.The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm.The optimum temperature profile began at a low temperature of 26 °C up to 30 h.The operating temperature increased rapidly to 31.9 °C,and then decreased slowly to 18 °C at 65 h.Thereafter,the temperature was maintained at18 °C until the end of fermentation.A maximum ethanol production of 89.3 g·L~(-1)was attained.Conceivably,our model would facilitate the improvement of Chinese rice wine production at the industrial scale.     

甲苯液相催化氧化过程模拟与优化

Liquid-phase oxidation of toluene with air has become the main technology for producing benzoic acid in a reactor at present. Based on the kinetic model of the toluene oxidation process obtained from laboratory and mass balance of key component, a novel model is established to simulate the industrial toluene oxidation process, in which the effects of benzaldehyde and benzyl alcohol are considered and the kinetic parameters are revised by industrial data. The simulation results show that the error of benzoic acid yield is within 3.5%. Based on the simulation model, to maximize the benzoic acid yield, an optimization model is proposed to optimize the operating parameters, including toluene feed-in mass flux and temperature. The optimization result indicates that on the allowable operating conditions the maximum benzoic acid yield obtained with the reaction temperature at 167.2 C an the mass flux at 104.1 t·h^- 1 is greater than the current one, which can be used to guide industrial reactor s operation.     

基于线性时变扰动模型的间歇过程最优迭代学习控制

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.     

间歇生产过程鲁棒统计监控及其应用

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework, which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.     

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework, which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.     

间歇提馏塔的操作

A stripping batch distillation column is preferred when the amount of the light component in the feed is small and the products are to be recovered at high purity. The operation modes of a batch stripping are believed to be the same as those of a rectifier. However, the control system of a stripper is different. In this paper, we explore three different control methods with Hysys (Hyprotech Ltd. 1997) for a batch stripper. The main difference is the control scheme for reboiler liquid level: (a) controlled by reflux flow; (b) controlled by reboiler heat duty; (c)controlled by bottom product flow. The main characteristics of operating a batch stripper with different control scheme are presented in this paper. Guidelines are provided for the startup of a batch stripper, the effects of some control tuning parameters on the column performance are discussed.     

Product quality improvement of batch crystallizers by a batch-to-batch optimization and nonlinear control approach

Batch crystallization is one of the widely used processes for separation and purification in many chemical industries. Dynamic optimization of such a process has recently shown the improvement of final product quality in term of a crystal size distribution (CSD) by determining an optimal operating policy. However, under the presence of unknown or uncertain model parameters, the desired product quality may not be achieved when the calculated optimal control profile is implemented. In this study, a batch-to-batch optimization strategy is proposed for the estimation of uncertain kinetic parameters in the batch crystallization process, choosing the seeded batch crystallizer of potassium sulfate as a case study. The information of the CSD obtained at the end of batch run is employed in such an optimization-based estimation. The updated kinetic parameters are used to modify an optimal operating temperature policy of a crystallizer for a subsequent operation. This optimal temperature policy is then employed as new reference for a temperature controller which is based on a generic model control algorithm to control the crystallizer in a new batch run.     

间歇化学反应温度先进控制系统

由于间歇化学反应器中动力学参数的不确定性和时变性,使其成为先进控制算法工业应用的主要对象。本文对以预测函数控制算法（PFC）为核心并结合PID控制的先进控制算法进行了研究,针对间歇化学反应器的温度跟踪控制问题,提出了包括PFC在内的四种控制方案的先进控制策略。并在SUPCON－JX300集散控制系统上开发了间歇反应温度跟踪先进控制软件包,工业现场运行取得了很好的结果。     

苏云金杆菌生长的结构动力学模型

考察了苏云金杆菌(Bt)的间歇及流加培养.根据对Bt间歇发酵实验现象的分析和讨论,提出了一个基于Williams分室模型的四组分结构模型,并从实验数据确定了各组分的初值.该模型能较好地拟合Bt间歇发酵实验数据.从间歇发酵获得的模型参数推算出流加培养曲线与实验值也基本吻合.     

添加晶种的间歇结晶器数学模型

动力学参数是设计结晶器和优化过程操作的主要依据。针对添加晶种的二次成核结晶过程，采用经验方程将反映结晶固相信息的透光率数据与CSD的二阶矩量进行关联，建立了包含透光率、液相浓度和相对过饱和度的动力学模型。并以KNO3-H2O为对象，运用该模型求取了结晶成核和生长动力学参数，结果与文献值相近。     

Nonlinear model of pneumatic conveying dryer for economic process control

Abstract

This paper presents a nonlinear dynamic model, suitable for economic process control of pneumatic conveying dryer for drying of food grains. The dynamic model is developed by reshaping the process equations derived for the batch drying, dilute phase, and a negative-pressure conveying system. The dynamic model parameters are identified by numerically solving a nonlinear least squares optimization problem, subject to a set of differential and algebraic equality constraints that describe the system dynamics and bounds in the parameters. A detailed parametric uncertainty and sensitivity analysis are performed providing valuable insight into the influence of critical model parameters on observables, the interplay among various parameter-state-measured disturbances, and quantifying uncertainties in the model. Further, different process economic performance and product quality indicator of uncertain dryer model are studied. The model validation study as performed with the underlying process shows a very good agreement in understanding necessary dynamic characteristics and interplay between the various parameter of interest.     

Integrated B2B‐NMPC control strategy for batch/semibatch crystallization processes

The uncertainty in crystallization kinetics is of major concern in manufacturing processes, which can result in deterioration of most model‐based control strategies. In this study, uncertainties in crystallization kinetic parameters were characterized by Bayesian probability distributions. An integrated B2B‐NMPC control strategy was proposed to first update the kinetic parameters from batch to batch using a multiway partial least‐squares (MPLS) model, which described the variances of kinetic parameters from that of process variables and batch‐end product qualities. The process model with updated kinetic parameters was then incorporated into an NMPC design, the extended prediction self‐adaptive control (EPSAC), for online control of the final product qualities. Promising performance of the proposed integrated strategy was demonstrated in a simulated semibatch pH‐shift reactive crystallization process to handle major crystallization kinetic uncertainties of L‐glutamic acid, wherein smoother and faster convergences than the conventional B2B control were observed when process dynamics were shifted among three scenarios of kinetic uncertainties. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

State observer scheme for joint kinetic parameter and state estimation

The development and acceptance of model-based monitoring tools in the bioprocess industry is made difficult by the usually large uncertainty associated with the process model. A natural approach to handle this issue is the design of adaptive state observers for the joint estimation of the process state and some of the uncertain model parameters. However, the state extension is often restricted to a few parameters only, for which observability conditions are satisfied with the available measurement information. In this study, this latter issue is circumvented by the combination of two observers: (a) a receding-horizon observer is designed for the joint estimation of the state and uncertain model parameters, and (b) an asymptotic observer, which provides state estimates independently of the kinetic model, is used to provide the missing additional information to the receding-observer, thus avoiding observability loss. This paper derives the properties of this combined state estimation scheme and demonstrates its performance with a realistic simulation case study of animal cell cultures.     

Iterative parameter estimation for extraction of crystallization kinetics of potassium chloride from batch experiments

Extraction of crystallization kinetics, the fundamental information which governs the performance of a crystallization process, is characterized by experimental difficulties in MSMPR and mathematical difficulties in batch crystallization. Here, a rigorous approach is taken to estimate kinetic parameters from a batch crystallizer. The two step LaxWendorff technique is adapted for the solution of the population balance in a batch crystallizer and an iterative self-correcting least squares algorithm is implemented for the estimation of the kinetic parameters. The need for multi-response estimation as opposed to single-response from terminal CSD is demonstrated. The kinetics extracted are average ones, representing primary and secondary nucleation kinetics. It is found that the kinetic parameters estimated by multi-response technique have a superior predictive capability as opposed to those obtained using the terminal CSD only. An advantage of the proposed algorithm is that the measurement of course of supersaturation, which is difficult to perform, is not required.     

中性β-甘露聚糖酶分批发酵动力学研究

以魔芋粉为碳源,研究了10 L自控发酵罐中枯草芽孢杆菌TJ-200603分批发酵产中性β-甘露聚糖酶的过程动力学。实验数据表明,菌体生长呈现典型S型曲线,而酶的合成与菌体生长同步进行,属于生长耦联型。基于这些过程曲线的变化规律,构建了β-甘露聚糖酶分批发酵过程的动力学模型。并经非线性拟合和优化,获得了最佳的模型参数值,最终确定了能够较好表征实际发酵过程中菌体细胞生长、产物β-甘露聚糖酶合成以及基质总糖消耗的3个动力学方程。