An energy effective PID tuning method for the control of polybutadiene latex reactor based on closed-loop identification

The PBL (Polybutadiene Latex) production process is a typical batch process. Changes of the reactor characteristics due to the accumulated scaling with the increase of batch cycles require adaptive tuning of the PID controller being used. In this work we propose a tuning method for PID controllers based on the closed-loop identification and the genetic algorithm (GA) and apply it to control the PBL process. An approximated process transfer function for the PBL reactor is obtained from the closed-loop data by using a suitable closed-loop identification method. Tuning is performed by GA optimization in which the objective function is given by ITAE for the setpoint change. The proposed tuning method showed good control performance in actual operations.     

Control of pH neutralization process using simulation based dynamic programming

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.     

Identification of kinetics of direct esterification reactions for PET synthesis based on a genetic algorithm

In this study we propose a method to identify the kinetics of direction esterification reactions for polyethylene terephthalate (PET) based on a genetic algorithm. The reaction rate parameters could be identified successfully by using a genetic algorithm and plant data. The effects of key operating variables (temperature, pressure, monomer feed ratio and residence time) on the reactor performance were also investigated. It was observed that the reactor performance strongly depends on the degree of dissolution of the solid terephthalic acid (TPA) in the reaction mixtures.     

模糊PID控制在梭式窑控制系统中的应用研究

将模糊控制和普通的PID控制相结合,提出了一种基于模糊控制规则的模糊PID控制器的设计方法并将其应用于梭式窑温度控制系统中。试验表明,这种方法比普通的PID控制方法精度高,超调量减少,具有较强的可靠性。     

Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

An analytical method for the design of a proportional-integral-derivative (PID) controller cascaded with a second-order lead-lag filter is proposed for various types of time-delay process. The proposed design method is based on the IMC-PID method to obtain a desired, closed-loop response. The process dead time is approximated by using the appropriate Pade expansion to convert the ideal feedback controller to the proposed PID·filter structure with little loss of accuracy. The resulting PID·filter controller efficiently compensates for the dominant process poles and zeros and significantly improves the closed-loop performance. The simulation results demonstrate the superior performance of the proposed PID·filter controller over the conventional PID controllers. A guideline for the closed-loop time constant, λ, is also suggested for the FOPDT and SOPDT models.     

Computation of multiloop controllers having desired closed-loop responses

For n-by-n multivariable processes, multiloop controllers have n degrees of freedom and hence the n diagonal elements of closed-loop transfer functions can be designed to have desired closed-loop responses. Multiloop controllers having desired closed-loop responses can be considered as an extension of the single-input single-output internal model control and they can be used as reference controllers. However, computations of such multiloop controllers have not been well developed. The Newton-Raphson method and the iterative sequential loop closing method can be used, but they can suffer from a divergence problem for some processes. Here, the continuation method is applied to obtain multiloop control systems with desired closed-loop responses for a robust computation. The multiloop controllers with desired closed-loop responses can be used to obtain dynamic interaction measures and design multiloop PID controllers.     

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

A self-organizing fuzzy controller is constructed for control of substrate concentration in fed-batch operation of a cell culture process. A genetic algorithm is used to generate fuzzy rules of the self-organizing fuzzy controller and to modify the universe of discourse automatically. The fuzzy controller is designed for the application ofScutellaria baicalensis G. plant cell culture process as a model system. A substrate feeding strategy of a two stage culture method to maximize flavone glycoside production in fed-batch culture ofScutellaria baicalensis G. plant cell is proposed based on structured model of growth and product synthesis. As a two stage culture, the feeding strategy consists of the first period at 22 g/L of glucose concentration to promote cell growth and the second period at 10 g/L of glucose concentration to promote flavone glycoside synthesis. The designed self-organizing fuzzy controller is applied to regulate the glucose concentration at a given set-point to increase flavone glycoside synthesis. The simulation results show that the proposed feeding strategy in a fed-batch culture enhances flavone glycoside production and the self-organizing fuzzy logic controller generated by genetic algorithm improves controller performance.     

基于OPC技术的糖厂澄清工段pH值控制系统

采用OPC技术运用澄清模拟实验装置建立糖厂工业澄清工艺pH值控制系统,实现MATLAB与力控监控软件实时信息的共享与数据交互;在Simulink中实现控制算法的模块化设计和快速调整算法策略.并以模糊PID算法为例,验证了该控制系统的可行性.     

甲醇工艺废水pH值超标问题的探讨

从甲醇工艺废水的来源分析造成废水pH值超标的原因,并采取相应的措施,从而大幅提高工艺废水的合格率。     

丙烯腈生产过程中pH值控制的研究

讨论了pH值控制对丙烯腈生产工艺过程的影响,介绍了丙烯腈生产过程中pH值的控制方法,说明了pH值控制的重要性。     

Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

The pH neutralization process is a representative nonlinear process. If a change in feed or buffer streams is introduced, the characteristics of the titration curve are altered and the way of change in titration curve is highly nonlinear. Moreover, if the changes are introduced in the middle of operation, then the nature of the process becomes nonlinear and time-varying. This is the one of the reason why conventional PID controller may fail. Even though the use of buffer solution may alleviate the nonlinearity, the improvement may be limited. A better way to tackle this type of process is to use nonlinear model-based control techniques with online parameter estimation. However, in most cases, the measurements of the process are not adequate enough so that the full state feedback control techniques can be utilized. If the states and crucial parameters are estimated online simultaneously, the effectiveness of the nonlinear state feedback control can be greatly enhanced. Thus, in this study, the capability of simultaneous estimation of states and parameters using Extended Kalman Filter (EKF) are experimentally investigated for a pH neutralization process. The process is modelled using reaction invariants and the concentrations of reaction invariants of the effluent stream (states) and the feed concentrations (parameters) are estimated online. From the comparison of experiments and simulations, it is found that the states and parameters can efficiently be identified simultaneously with EKF so that the estimated information can be exploited by state-feedback control techniques     

基于模式搜索加速遗传算法的逆向法确定竞争吸附等温线

基于局部优化算法的逆向法,被广泛应用于确定吸竞争附等温线模型,但其存在的主要问题是:不能保证结果的广义收敛性和低下的计算效率.本文提出了一种基于模式搜索加速遗传算法的反晨向法.并将该算法成功应用于柠檬酸/葡萄糖的竞争吸附等温线的确定,在保证了计算结果的广义收敛性的前提下,算法执行效率相对传统遗传算法提高了一倍以上.     

Simulink仿真在PID控制教学中的探索与设计

在“过程控制系统”的教学中,传统的理论教学往往使学生对教学内容缺乏感性认识,感觉枯燥,记忆不深刻。将Simulink仿真应用到PID控制部分教学中,采用建模仿真带动理论教学的方法,让学生对PID控制有更为直观的认识。教学实践表明,该方法增强了学生的学习积极性,提高了他们对过程控制系统的分析与设计能力,取得了良好的课堂教学效果。     

A DNA based genetic algorithm for parameter estimation in the hydrogenation reaction

Inspired by the mechanism of the biological DNA, a DNA based genetic algorithm (DNA-GA) is proposed to determine the kinetic parameters for the hydrogenation reaction. The considered chemical process contains five reactions and 25 unknown parameters. The DNA-GA uses the DNA encoding method to represent the potential parameters and genetic operators inspired from the biological DNA are designed to find the global optimum. The study on the performance for typical benchmark functions shows that the DNA-GA outperforms the other two methods in both convergence speed and accuracy. Based on the operating data gathered from an industrial hydrogenation unit, 25 parameters are obtained by the DNA-GA and the kinetic model for the hydrogenation reaction is established. To verify the validity of the established model, another four groups of data are used to test the established model and two previously reported models. The comparison results show that the sum of square relative errors of the model obtained by the DNA-GA is the least of the test models, and its prediction is in good agreement with the practical operating data.     

An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based on a novel selection strategy is presented to handle nonlinear programming problems. Each individual in selection process is represented as a three-dimensional feature vector composed of objective function value, the degree of constraints violations and the number of constraints violations. We can distinguish excellent individuals through two indices according to Pareto partial order. Additionally, IGA incorporates a local search (LS) process into selection operation so as to find feasible solutions located in neighboring areas of some infeasible solutions. Experimental results over a set of benchmark problems demonstrate that proposed IGA has better robustness, effectiveness and stableness than other algorithm reported in literature.     

基于遗传算法的人工神经网络对增韧尼龙冲击性能的预测

采用基于遗传算法的人工神经网络预测了马来酸酐接枝乙烯-辛烯共聚物(POE-g-MAH)/线型低密度聚乙烯(LLDPE)对尼龙6(PA6)冲击性能的影响,探索了基于遗传算法的人工神经网络在高分子复合材料研究中的应用途径。结果表明:基于遗传算法的人工神经网络能够很好地预测高分子复合材料的性能;当POE-g-MAH和LLDPE的含量分别为12%和8%时,PA6的冲击强度达到94.12 kJ/m2。     

Studies on the relationship between slurry pH,volatilization processes and the influence of acidifying additives

The mutual influence of slurry pH and volatilization processes on one hand, and the possibility of N conservation by the use of acidifying additives on the other, were investigated in static incubation experiments. The influence of the NH₃ and CO₂ volatilizations on slurry pH was studied by selectively supporting one or both processes. The addition of Ca²⁺ to slurry was compared to that of K⁺ and H⁺. The effects of Cl^–, SO ₄ ^2– and NO ₃ ^– as corresponding anions of Ca²⁺ on slurry pH as well as NH₃ and N₂O emissions were tested. The slurry pH (7.4) increased during incubation. When CO₂ volatilization was suppressed, the pH increase was reduced, and NH₃ volatilization was cut down by 50%. Ca²⁺ additions hardly influenced the initial slurry pH, but reduced the pH increases and NH₃ losses. Proton addition, in contrast, decreased slurry pH but did not decrease the subsequent pH rise. K⁺ had no effect on slurry pH and N losses. As compared to CaCl₂, CaSO₄ showed less effect on slurry pH and N losses. Ca(NO₃)₂ was nearly as effective as CaCl₂ in preventing NH₃ volatilization, but caused denitrification losses and elevated N₂O production. Titration curves of the different slurry treatments were used to interpret the results of the incubation experiments. In a microplot field experiment the NH₃ volatilization and slurry pH after surface application of slurry was measured. The acidifying and N conserving effects of Ca²⁺ and H⁺ additions were confirmed.     

A hybrid DNA based genetic algorithm for parameter estimation of dynamic systems

Inspired by the evolutionary strategy and the biological DNA mechanism, a hybrid DNA based genetic algorithm (HDNA-GA) with the population update operation and the adaptive parameter scope operation is proposed for solving parameter estimation problems of dynamic systems. The HDNA-GA adopts the nucleotides based coding and some molecular operations. In HDNA-GA, three new crossover operators, replacement operator, transposition operator and reconstruction operator, are designed to improve the population diversity, and the mutation operator with adaptive mutation probability is applied to guarantee against stalling at local peak. Besides, the simulated annealing based selection operator is used to guide the evolution direction. In order to overcome the premature convergence drawbacks of GAs and enhance the algorithm global and local search abilities, the population update operator and the adaptive parameter scope operator are suggested. Numerous comparative experiments on benchmark functions and real-world parameter estimation problems in dynamic systems are conducted and the results demonstrate the effectiveness and efficiency of the HDNA-GA.     

Statistical monitoring of dynamic processes based on dynamic independent component analysis

Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution. However, in real chemical and biological processes, these assumptions are invalid because of their dynamic and nonlinear characteristics. Therefore, monitoring charts based on conventional PCA tend to show many false alarms and bad detectability. In this paper, a new statistical process monitoring method using dynamic independent component analysis (DICA) is proposed to overcome these disadvantages. ICA is a recently developed technique for revealing hidden factors that underlies sets of measurements followed on a non-Gaussian distribution. Its goal is to decompose a set of multivariate data into a base of statistically independent components without a loss of information. The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. DICA can show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto- and cross-correlation of variables. It is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. The simulation results clearly show that the method effectively detects faults in a multivariate dynamic process.     

DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes

Based on RNA genetic operations and DNA sequence model under selection and mutation, an electronic RNA genetic algorithm (RNA-GA) with improved crossover and mutation operator is proposed. The proposed algorithm can be implemented on real biochemical reaction after simple transition, thus, the brute force method of DNA computing can be broken. The convergence analysis of the proposed algorithm shows that RNA-GA with elitist strategy can converge in probability to the global optimum. Comparisons of RNA-GA with standard genetic algorithm (SGA) for typical test functions show the advantages and efficiency of the proposed algorithm. As illustrations, the RNA-GA is implemented on parameter estimation of a heavy oil thermal cracking 3-lumping model and a fluid catalytic cracking unit (FCCU) main fractionator. In both cases, it is shown that the methodology is effective in parameter estimation of chemical processes.