一种新的DNA遗传算法及其在参数估计中的应用

化工过程的参数估计是十分棘手的问题,为此常将这类问题转化为非线性优化问题来解决。遗传算法是一种适应性强的全局搜索方法,常被用于解决非线性系统的参数估计问题。但其局部搜索能力较差,易早熟。针对遗传算法的缺点,提出了一种新的DNA遗传算法。该方法使用碱基对个体进行四进制编码,受DNA分子操作启发设计了新的交叉和变异算子。两个经典测试函数的计算结果表明,该算法的搜索能力相对于其他两种算法有了明显提高。使用该算法来估计重油热解三集总模型中的参数,结果表明所建模型拟合精度高。     

粒子群优化算法在催化裂化模型参数估计中的应用

参数估计是化工模型工业应用中的重要课题,有相当的难度。针对催化裂化八集总模型的动力学参数估计问题,考察了不同类型优化算法的应用效果,结果表明,粒子群优化算法简单、容易实现,而且可以避免传统方法对初始值的依赖,并进一步提出用结合Levenberg-Marquardt算法的混合粒子群优化算法提高参数估计效果。工业实例表明,用混合粒子群优化算法得到的动力学参数可以保证模型的预测精度。     

A novel RNA genetic algorithm for parameter estimation of dynamic systems

Inspired by RNA molecular structure and operators, a novel RNA genetic algorithm (NRNA-GA) with RNA encoding and operators is proposed for addressing parameter estimation problems of dynamic systems. It adopts nucleotides based encoding and some RNA molecular operators, such as permutation operator and stem-loop operator, which is different from conventional genetic algorithms (GAs). An adaptive mutation rate is also used to guard against stalling at local peak. In order to overcome the drawbacks of premature convergence of GAs, a type of special fitness function incorporating objective function values with Euclidean spaces distance is introduced, which leads the population to maintain its diversity and the algorithm to jump out of local optima. A simple direct search method is incorporated into the NRNA-GA to improve local search performance. Numerical experiments about benchmark functions and real-world parameter estimation problems in dynamic systems demonstrate the efficiency and effectiveness of the proposed optimization algorithm.     

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.     

基于优进策略的遗传算法对重油热解模型参数的估计

针对常规遗传算法全局寻优效率偏低的弱点，提出了一种优进策略，用以改进常规遗传算法。该策略将从繁衍过程中获取进化信息，自适应地改进子代分布，适时引入确定性操作，以提高全局寻优性能。提出的相关技术包括维持种群的多样性、改进交叉算子、增加Powell寻优算子等。实例测试表明这种优进策略效果良好，并已成功地应用于重油热解三集总动力学复杂数学模型的非线性参数估计。     

Robust modeling of a microalgal heterotrophic fed-batch bioreactor

Microalgal feedstocks have shown potential for the production of biofuels and fine chemicals. Recently, an optimal experimental input profile for the identification of parameters of a microalgal bioreactor, containing 6 states and 12 unknown parameters has been proposed. In this work, the proposed design is implemented and parameters are estimated. It was found that the parameter estimation procedure can be made more computational efficient by the use of a novel iterative non-linear model reparameterization algorithm. By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values. The final, validated, model can be used for optimal control and process simulation.     

基于新型蚁群算法优化的重油热裂解模型

针对重油热裂解模型的参数估计问题呈高维、高度非线性的特征,提出一种基于新型蚁群算法优化的重油热裂解模型.通过新型蚁群算法优化确定模型参数,获得具有良好预测精度的模型.新型蚁群算法通过将解空间划分成若干子域,并引入遗传操作,实现连续优化问题的寻优.仿真结果表明它具有良好的性能,且优于传统的遗传算法.     

改进的AEA算法及其在重油热解模型参数估计中的仿真研究

着眼于AEA算法本身的不足点,通过更合理地给定其每一代的行走步长,提出一个改进的AEA算法(IAEA)来提高AEA算法的寻优性能。鉴于行走步长对于进化的不同阶段全局搜索和局部搜索之间的关系影响,今提出IAEA的步长应随着实际进化的不同阶段而合理地变化,以使得算法能跳出局部最优,避免早熟现象的发生。IAEA算法在7个典型测试函数上进行了测试,测试结果表明,与基本AEA算法、PSO和DE算法相比,IAEA的寻优性能有了很大的提高,不仅获得的解的质量更好,而且算法的稳定性都得到了提高。最后将IAEA算法用于重油热解模型参数估计的仿真研究中,通过验证,得到了更有利的结果,说明文中提出的算法是有效的。     

基于全局优化改进混沌粒子群遗传算法的物料平衡数据校正

结合遗传算法（GA）和粒子群算法（PSO）的优点以及混沌运动的特性,提出了加入混沌扰动的混沌粒子群遗传算法（DCPSO-GA）,并使用5个高维非线性测试函数考察全局优化混合算法的性能。DCPSO-GA解决了在寻优搜索时出现的停滞现象,扩大了全局优化的搜索空间,丰富了粒子的多样性,且不需要函数梯度信息。测试结果证明,针对本文的5个测试函数DCPSO-GA能找到全局最优解,其收敛速度很快,大大减少了计算量。而且,经过与其他相关算法比较可知,当总的目标函数调用次数较接近或更少时,改进算法不论在计算精度还是收敛速度上,均有很大的提高。并将DCPSO-GA算法应用到重油裂解参数估计和预测中,测试结果证明,其提高了参数估计和预测的准确性,降低了误差,能有效找到全局最优解,收敛速度快,大大减少计算量。     

基于PSO-SVM的催化裂化装置故障诊断研究

提出基于粒子群优化算法和支持向量机的催化裂化装置反应再生子系统故障诊断方法。利用粒子群优化算法的全局搜索特性,实现支持向量机的参数优化算法。根据支持向量机算法构建了催化裂化装置反应再生子系统故障诊断模型。结果显示,该诊断方法准确率高,具有较高的使用价值。     

复合粒子群优化算法在模型参数估计中的应用

化工非线性模型的参数估计是较为困难的寻优问题，经典方法常会陷入局部极值。粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。但其参数值的确定与问题相关，若设定不当，会严重影响全局搜索的性能。今提出引入遗传算法，在粒子群算法的搜索过程中，逐代优选参数，包括惯性权值，加速常数，以此构建为复合粒子群优化算法。分析与测试表明，其全局搜索性能有显著改善。进一步的工作又将两种粒子群算法成功地应用于重油热解模型的参数估计。采用复合粒子群优化算法估计参数构建的重油热解模型，其预报相对误差比常规粒子群优化算法降低了8.97％，比简单遗传算法降低了23.21％，效果明显。     

A real-time estimation approach to time-varying time delay and parameters of NARX processes

This paper presents a solution to the joint time-varying time delay and parameter estimation of NARX (nonlinear autoregressive with exogenous inputs) processes, where only pure time delay in input signal is considered. A modified strong tracking filter (MSTF) is proposed, and is adopted as an adaptive estimation algorithm. Three kinds of specific NARX processes are considered. The first is also the simplest, the output signal is the input with time delay plus disturbance; The second one is a simple NARX process plus disturbance; The third NARX process even has unknown time-varying parameters. For each of the NARX processes, we set up a specific estimation model, with these models the proposed MSTF algorithm can be applied to the real-time time delay and parameter estimation of the above three NARX processes. Computer simulation results demonstrate the effectiveness of the proposed approach. Moreover the robustness of the proposed algorithm against some model/process parameter mismatches is also tested via computer simulations.     

A Novel Empirical Equation for Relative Permeability in Low Permeability Reservoirs

In this paper, a novel empirical equation is proposed to calculate the relative permeability of low permeability reservoir. An improved item is introduced on the basis of Rose empirical formula and Al-Fattah empirical formula, with one simple model to describe oil/water relative permeability. The position displacement idea of bare bones particle swarm optimization is applied to change the mutation operator to improve the RNA genetic algorithm. The parameters of the new empirical equation are optimized with the hybrid RNA genetic algorithm (HRGA) based on the experimental data. The data is obtained from a typical low permeability reservoir well 54 core 27-1 in GuDong by unsteady method. We carry out matlab programming simulation with HRGA. The comparison and error analysis show that the empirical equation proposed is more accurate than the Rose empirical formula and the exponential model. The generalization of the empirical equation is also verified.     

模糊自适应遗传算法及其在化工中的应用

Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.     

SBR恒定曝气量好氧硝化过程动态DO模拟:参数估计及其可靠性评价

提出了一种全新的针对初值常微分方程组系统的全局最优化(遗传算法)结合局部最优化(拟牛顿法)实现参数的鲁棒、快速估计的算法。利用该算法,对所建两步硝化模型中过程溶解氧(dissolved oxygen, DO)的动态变化成功实现了参数估计,相关度达到了0.9955。采用基于Fisher信息矩阵和直接搜索获得的参数置信区间相比较的方法实现了对估计结果的可靠性分析,结果表明采用该方法大部分参数可实现可靠估计,只有少数两个参数可实现估计却不可靠,为动力学系统的参数估计结果提出了一个全新的检验方法。DO模拟结果可以作为软测量手段,对过程中易生物降解COD、氨氮、亚硝态氮、硝态氮的全程变化情况提供充足的过程信息。     

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.     

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usual y run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non-linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-I ) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta-tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.     

Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor

This work presents a procedure to solve nonlinear dynamic data reconciliation (NDDR) problems with simultaneous parameter estimation based on particle swarm optimization (PSO). The performance of the proposed procedure is compared to the performance of a standard Gauss-Newton (GN) scheme in a real industrial problem, as presented previously by Prata et al. [2006. Simultaneous data reconciliation and parameter estimation in bulk polypropylene polymerizations in real time. Macromolecular Symposia 243, 91-103; 2008. In-line monitoring of bulk polypropylene reactors based on data reconciliation procedures. Macromolecular Symposia 271, 26-37]. Both methods are used to solve the NDDR problem in an industrial bulk propylene polymerization process, using real data in real time for the simultaneous estimation of model parameters and process states. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic-differential equations, was implemented and used for interpretation of the actual plant behavior in real time. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Obtained results indicate that the proposed PSO procedure can be implemented in real time, allowing for estimation of more reliable process states and model parameters and leading to much more robust and reproducible numerical performance.     

Determining robust reaction kinetics from limited data

Accurate chemical kinetics are essential for reactor design and operation. However, despite recent advances in “big data” approaches, availability of kinetic data is often limited in industrial practice. Herein, we present a comparative proof-of-concept study for kinetic parameter estimation from limited data. Cross-validation (CV) is implemented to nonlinear least-squares (LS) fitting and evaluated against Markov chain Monte Carlo (MCMC) and genetic algorithm (GA) routines using synthetic data generated from a simple model reaction. As expected, conventional LS is fastest but least accurate in predicting true kinetics. MCMC and GA are effective for larger data sets but tend to overfit to noise for limited data. LS-CV strongly outperforms these methods at much reduced computational cost, especially for significant noise. Our findings suggest that implementation of CV with conventional regression provides an efficient approach to kinetic parameter estimation with high accuracy, robustness against noise, and only minimal increase in complexity.     

Consensus-based approach for parameter and state estimation of agro-hydrological systems

The development of advanced closed-loop irrigation systems requires accurate soil moisture information. In this work, we address the problem of soil moisture estimation for the agro-hydrological systems in a robust and reliable manner. A nonlinear state-space model is established based on the discretization of the Richards equation to describe the dynamics of the agro-hydrological systems. We consider that model parameters are unknown and need to be estimated together with the states simultaneously. We propose a consensus-based estimation mechanism, which comprises two main parts: (a) a distributed extended Kalman filtering algorithm used to estimate several model parameters; and (b) a distributed moving horizon estimation algorithm used to estimate the state variables and one remaining model parameter. Extensive simulations are conducted, and comparisons with existing methods are made to demonstrate the effectiveness and superiority of the proposed approach. In particular, the proposed approach can provide accurate soil moisture estimate even when poor initial guesses of the parameters and the states are used, which can be challenging to be handled using existing algorithms.