基于模型预测控制的部分分散控制系统设计(英文)

An expansion procedure to design partially decentralized controllers via model predictive control is proposed in this paper. Partially decentralized control is a control structure that lies between a fully decentralized structure and a fully centralized one, and has the advantage of achieving comparable performance as a fully centralized controller but with simpler structure. The proposed method follows the expansion method proposed in a previous paper where internal model control (IMC) was used to design controllers for non-square subsystems. The method requires computing the pseudo-inverse of a non-square matrix via pseudo-inverse factors. Instead, the proposed method uses dynamic matrix control (DMC) to design PID controllers for non-square subsystems without using additional factors. The effectiveness of the proposed method is demonstrated on several chemical examples. Simulation results show that the proposed method is simple and can achieve better performance.     

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm （GA） based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and experiment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.     

Integration of a Gas Fired Steam Power Plant with a Total Site Utility Using a New Cogeneration Targeting Procedure

A steam power plant can work as a dual purpose plant for simultaneous production of steam and elec-trical power. In this paper we seek the optimum integration of a steam power plant as a source and a site utility sys-tem as a sink of steam and power. Estimation for the cogeneration potential prior to the design of a central utility system for site utility systems is vital to the targets for site fuel demand as well as heat and power production. In this regard, a new cogeneration targeting procedure is proposed for integration of a steam power plant and a site utility consisting of a process plant. The new methodology seeks the optimal integration based on a new cogenera-tion targeting scheme. In addition, a modified site utility grand composite curve （SUGCC） diagram is proposed and compared to the original SUGCC. A gas fired steam power plant and a process site utility is considered in a case study. The applicability of the developed procedure is tested against other design methods （STAR？ and Thermoflex software） through a case study. The proposed method gives comparable results, and the targeting method is used for optimal integration of steam levels. Identifying optimal conditions of steam levels for integration is important in the design of utility systems, as the selection of steam levels in a steam power plant and site utility for integration greatly influences the potential for cogeneration and energy recovery. The integration of steam levels of the steam power plant and the site utility system in the case study demonstrates the usefulness of the method for reducing the overall energy consumption for the site.     

利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文)

On-line estimation of unmeasurable biological variables is important in fermentation processes, directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product. In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed. By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model, a state space model is developed. An improved algorithm, swarm energy conservation particle swarm optimization (SECPSO), is presented for the parameter identification in the mechanistic model, and the support vector machines (SVM) method is adopted to establish the nonlinear measurement model. The unscented Kalman filter (UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process. The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.     

光伏阵列的实时建模(英文)

This paper mainly aims at the modeling problem of the photovoltaic (PV) array with a 30 kW PV grid-connected generation system. An iterative method for the time-varying parameters is proposed to model a plant of PV array. The relationship of PV cell and PV array is obtained and the solution for PV array model is unique. The PV grid-connected generation system is used to demonstrate the effectiveness of the proposed method by comparing the calculated values with the actual output of the system.     

应用电容层析成像对多相流的动态可视化(英文)

Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualization method for the flow pattern identification,in which image reconstruction algorithms play an important role.In this paper,a generalized dynamic reconstruction model,which integrates ECT measurement information and physical evolution information of the objects of interest,was presented.A generalized objective functional that simultaneously considers the spatial constraints,temporal constraints and dynamic evolution information of the objects of interest was proposed.Numerical simulations and experiments were implemented to evaluate the feasibility and efficiency of the proposed algorithm.For the cases considered in this paper,the proposed algorithm can well reconstruct the flow patterns,and the quality of the reconstructed images is improved,which indicates that the proposed algorithm is competent to reconstruct the flow patterns in the visualization of multiphase flows.     

一种基于含噪时序结构独立元分析的过程监控方法(英文)

Conventional process monitoring method based on fast independent component analysis (FastICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA (NoisyTSICA) is proposed to solve such problem. A NoisyTSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components (ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recur-sive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed NoisyTSICA-based monitoring method outperforms the conven-tional FastICA-based monitoring method.     

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.     

油气两相流空隙率测量

A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measurement model.The flow pattern identification was achieved by combining the fuzzy pattern recognition technique and the crude cross-sectional image reconstructed by the simple back projection algorithm.The genetic algorithm and the partial least square method were applied to develop the voidage measurement models.Experimental results show that the proposed method is effective.It can overcome the influence of flow pattern on the voidage measurement,and also has the advantages of simplicity and speediness.     

基于邻域保留空间中模糊C聚类的多模态过程监控(英文)

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.     

基于统计量模式分析的T-KPLS间歇过程故障监控

核函数的全影结构投影(total kernel projection to latent structures,T-KPLS)最近在故障监控领域取得了广泛应用, 其实质是对数据矩阵的协方差矩阵进行分解, 没有利用数据的高阶统计量等有用信息, 在进行特征提取时会造成数据有用信息的丢失, 导致故障识别效果差。为了解决此问题, 提出了统计量模式分析(statistics pattern analysis, SPA)与核函数的全影结构投影法(total kernel projection to latent structures, T-KPLS)相结合的多向统计量模式分析的核函数的全影结构投影法(multi-way statistics pattern analysis total kernel projection to latent structures, MSPAT-KPLS)。该方法首先构造样本的不同阶次统计量, 将数据从原始的数据空间映射到统计量样本空间, 然后利用核函数将统计量样本空间映射到高维核空间并在质量变量的引导下将特征空间分为过程变量与质量变量相关、过程变量与质量变量无关、过程变量与质量变量正交和残差4个子空间;最后针对与质量变量相关和残差空间建立联合监控模型, 当监控到有故障发生时进行故障变量追溯。最后将该方法应用到微生物发酵过程中, 并与传统方法进行比较, 发现该方法具有更好的监控性能。     

Subspace based model identification for missing data

This article addresses the problem of missing process data in data-driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identification. Note that the existing subspace identification approaches often utilize singular value decomposition (SVD) as part of the identification algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identification matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The efficacy of the proposed approach is shown via simulation of a nonlinear batch process example.     

带变遗忘因子的自适应子空间预测控制器设计

针对实际工业过程具有非线性、时变和多变量的特点,提出一种数据驱动的带有变遗忘因子的自适应子空间预测控制方法。该方法将在线子空间辨识与模型预测控制相结合,同时利用期望输出值与实际输出值的误差实现变遗忘因子的自适应更新,并根据当前变遗忘因子构造了过去与将来的Hankel矩阵,从而实现了预测模型的在线更新,提高了控制器对非线性时变特征的辨识灵敏度和适应能力。最后,利用该控制器对四容水箱对象进行仿真研究,验证了算法的有效性。     

Model reduction-based optimization using large-scale steady-state simulators

Most engineering systems can be accurately simulated using models consisting of Partial Differential Equations. Thus the challenging problem of PDE-constrained optimization arises naturally in engineering design. Issues surface due to the high number of variables involved and the use of specialized software for simulation which may not include an optimization option. In this work we present a methodology for the steady-state optimization of systems for which an input/output steady-state simulator is available. The proposed method is efficient for dissipative systems and is based on model reduction. This framework employs a two-step projection scheme, first onto the low-dimensional, adaptively computed, dominant subspace of the system and second onto the subspace of independent variables. Hence only low order Jacobian and Hessian matrices are used in this formulation, computed efficiently with directional perturbations.     

基于核T-PLS的化工过程故障检测算法

针对全潜结构投影法（T-PLS）在检测非线性过程故障时误报率和漏报率较高的缺点,提出了基于核函数的全潜结构投影法（KT-PLS）。该算法通过核函数将过程数据从低维输入空间非线性地映射到高维特征空间,实现非线性问题的线性化;然后在质量变量的引导下将特征空间分为与质量直接相关、与质量正交、与质量无关和残差四个子空间;最后分别构建D和Q统计量进行故障检测。将该算法应用到Tennessee Eastman process（TEP）,多种故障模式下的仿真结果表明,KT-PLS比T-PLS更适合监控具有强非线性的生产过程。     

Dynamic stationary subspace analysis based on Gaussian mixture models for ironmaking process monitoring

Blast furnace ironmaking process monitoring is an important and challenging task. Due to the influence of hot blast stove switching and large fluctuations in the quality of raw materials, the measurements of ironmaking processes show obvious non-stationary characteristics, and in addition, the observed data are also characterized by time-series dynamic and non-Gaussian characteristics. In this paper, a dynamic stationary subspace analysis method based on the Gaussian mixture model (DSSA–GMM) is proposed to address the difficulties in blast furnace ironmaking process monitoring. The time-series dynamic relationship of the data is conducted by introducing a sliding time window. The Gaussian mixture model (GMM) is used to deal with the non-Gaussian characteristics of the data, and the parameters of the GMMs are estimated using the expectation–maximization algorithm. The stationary projection matrix is obtained by optimizing the Kullback–Leibler (K–L) divergence between GMMs of different periods to realize the stationary subspace separation. Finally, the convex hull of the stationary subspace is established for fault detection, thus realizing the monitoring for non-stationary and non-Gaussian dynamic processes. The effectiveness of the DSSA–GMM method is verified by a numerical simulation and a dataset collected from an actual blast furnace ironmaking process.     

The chemical process monitoring method based on temporal extended orthogonal neighbourhood preserving embedding (TONPE)

Due to the high dimensionality, non-linearity and dynamic characteristics of chemical process data, a fault monitoring method based on temporal extension orthogonal neighbourhood preserving embedding (TONPE) is proposed. In order to make up for the shortcomings of the orthogonal neighbourhood preserving embedding (ONPE) algorithm, an information extraction strategy based on spatio-temporal structure is developed. First, a local neighbourhood set with spatio-temporal characteristics is established, and a weight matrix with spatio-temporal is reconstructed for each time point through the nearest neighbour in space and time. Then, a projection matrix with orthogonal constraints is obtained to establish a monitoring model. The TONPE algorithm can fully capture the local dynamic changes of high-dimensional data by extracting two different manifold features, so that the low-dimensional space has better performance capabilities. The simulation results of the continuous stirred tank reactor process and the Tennessee Eastman process verify the effectiveness of the TONPE algorithm in chemical process monitoring.     

Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures

This paper proposes a new concurrent projection to latent structures is proposed in this paper for the monitoring of output‐relevant faults that affect the quality and input‐relevant process faults. The input and output data spaces are concurrently projected to five subspaces, a joint input‐output subspace that captures covariations between input and output, an output‐principal subspace, an output‐residual subspace, an input‐principal subspace, and an input‐residual subspace. Fault detection indices are developed based on these subspaces for various fault detection alarms. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces only. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 496–504, 2013     

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition (SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.     

A Novel Data-Driven Bilinear Subspace Identification Approach

Subspace identification methods for bilinear systems perform computation with data matrix exploding. Huge computational burdens have been the biggest problem that prohibits real applications of bilinear subspace identification. In this paper, we propose a novel approach with the identification of bilinear predictor model from input-output data with enhanced computational efficiency. Based on the displacement structure theory, the QR factorization is replaced with a fast Cholesky factorization, which deals with the curse of huge dimensionality and therefore reduces the computation cost. These improvements make the bilinear subspace approach more computationally efficient with good prediction ability. Finally, the proposed control approach is illustrated with a simulation of the non-linear continuously stirred tank reactor (CSTR) system.