Development of web-based support system for on-line optimization

On-line optimization is a very powerful tool that saves costs and improves the operational productivity of a chemical plant. However, the process of on-line optimization takes too much time and cost because it needs the significant contribution of process optimization specialists. To solve this problem, a web-based support system for online optimization is designed. The design specifications of the target product are identified using product design tools such as house of quality and roof correlation matrix. The design specifications are implemented into three product modules: communication module, project management module, and information interaction module. The final prototype system is evaluated based on a real application to the on-line optimization of a PTA process. The system shows both the time and the cost could be reduced by 43.5% and 43.3%.     

Capturing dynamics in integrated supply chain management

A major challenge for an enterprise to stay competitive in today’s highly competitive market environment is to be able of capturing and handling the dynamics of its entire supply chain (SC). This work incorporates uncertainty and process dynamics into enterprise wide models which also contemplate cross-functional decisions. The SC integrated solution developed includes a design–planning and a financial formulations. A model predictive control (MPC) methodology is proposed that comprises a stochastic optimization approach. A scenario based multi-stage stochastic mixed integer linear programming (MILP) model is employed to address the problem. The novel control framework introduced constitutes a step-forward in closing the loop for the dynamic supply chain management (SCM) and a supporting platform for the supervisory module handling the incidences that may arise in the SC. The potential of the presented approach is highlighted through a case study, where the results of the deterministic MPC and the joint control framework are compared. It is emphasized the significance of merging uncertainty treatment and control strategies to improve the SC performance.     

On-line optimization: a hierarchical scheme for distallation column control

This paper presents a hiererchical on-line optimization and nonlinear control of a distillation column using a new semirigorous model which drastically reduces the problem dimensions compared to a rigorous model without significantly compromising with accuracy. Single level control is often found to be inadequate since optimization using highly nonlinear physical models cannot be performed in the very short real times available between controller actions. A two level approach can effectively overcome this difficulty with optimization being carried out using more realistic nonlinear physical models at the supervisory level. The nominal control vector trajectory generated at this level using a nonlinear analytical model predictive control (NAMPC) is passed on to the regulatory level where an LQ type neighbouring optimal control action is generated. Simulation results showed that a two level control can effectively deal with process-model mismatch in the presence of disturbances. Experimentation on pilot scale distillation column vindicated the simulation results and demonstrated the superiority of the hierarchical control scheme.     

A safe-parking framework for plant-wide fault-tolerant control

This work addresses the problem of handling actuator faults in a chemical plant. We consider a multi-unit nonlinear process system subject to input constraints and actuator faults in one unit that preclude the possibility of operating the unit at its nominal equilibrium point. The interconnected nature of the units in a plant brings forth unique opportunities as well as challenges that simply do not exist when handling faults in isolated units. In particular, the fact that the outlet streams from a faulty unit go through subsequent (well functioning) units raises the possibility of better restricting the effects of the fault to the faulty unit. At the same time, handling a fault in a unit may necessitate appropriate action in the downstream unit, which is not a result of a fault in the downstream unit. To address such issues that arise when handling faults in chemical plants, in this work we present a safe-parking framework (we define, in a previous work on handling faults in isolated units, a safe-park point as an operating point where in the event of a fault, a unit can be operated in a way that prevents onset of hazardous situation and allows smooth resumption of nominal operation) for plant-wide fault-tolerant control. We first consider the case where there exists a safe-park point for the faulty unit such that the effect of safe-parking can be completely rejected (via changing the nominal values of the manipulated variables) in the downstream unit. Steady-state as well as dynamic considerations (including the presence of input constraints) is used in determining the necessary conditions for safe-parking the multi-unit system. We next consider the problem where no viable safe-park point for the faulty unit exists such that its effect can be completely rejected in the subsequent unit. A methodology is presented to simultaneously safe-park the consecutive units. Finally, we incorporate performance considerations in the safe-parking framework and illustrate the implementation of the safe-parking framework using a multi-unit chemical reactor system.     

Fuzzy diagnosis method for control systems with coupled feed forward and feedback loops

By considering the fault propagation behaviors in control systems with coupled feed forward and feedback loops, a fuzzy-logic-based fault diagnosis strategy has been developed in the present work. The proposed methods can be implemented in two stages. In the off-line preparation stage, the root causes of a system hazard are identified by determining the minimal cut sets of the corresponding fault tree. The occurrence order of observable disturbances caused by each fault origin is derived from the system digraph. All possible patterns of the on-line symptoms and their evolution sequences can then be deduced accordingly. These sequences are used as the basis for constructing a two-layer fuzzy inference system. In the next on-line implementation stage, the occurrence indices of the root causes are computed with the IF-THEN rules embedded in the inference engine using the real-time measurement data. Numerical simulation studies have been carried out to demonstrate the feasibility of the proposed approach.     

基于自适应量子蚁群算法的石脑油裂解炉故障诊断

针对实际生产过程中缺乏故障数据,采用适合小样本问题的支持向量机对石脑油裂解炉进行故障诊断。为了消除高维数据及系统噪声对故障诊断的干扰,将量子编码引入蚁群算法,提出一种新的自适应量子蚁群算法进行故障特征选择以进一步提高诊断性能。数值仿真实验结果显示,提出的自适应量子蚁群算法具有更好的全局寻优性能;对石脑油裂解炉传感器故障的诊断结果表明自适应量子蚁群算法能快速、准确地搜索到关键故障特征,有效地提高了支持向量机故障诊断的正确率和鲁棒性。     

振动在线监测与故障诊断系统在鼓风机上的应用

介绍振动在线监测与故障诊断系统的组成、数据采集器及软件。利用该系统对硫酸装置1200kW空气鼓风机实施在线状态监测与故障诊断，可确保机组安全可靠地运行。根据频谱分析诊断故障的类型及原因，采取针对性措施，可节约维修时间和费用。对振动在线监测与故障诊断系统今后的发展方向提出建议。     

A model predictive formulation for control of open-loop unstable cascade systems

Cascade control is commonly used in the operation of chemical processes to reject disturbances that have a rapid effect on a secondary measured state, before the primary measured variable is affected. In this paper, we develop a state estimation-based model predictive control approach that has the same general philosophy of cascade control (taking advantage of secondary measurements to aid disturbance rejection), with the additional advantage of the constraint handling capability of model predictive control (MPC). State estimation is achieved by using a Kalman filter and appending modeled disturbances as augmented states to the original system model. The example application is an open-loop unstable jacketed exothermic chemical reactor, where the jacket temperature is used as a secondary measurement in order to infer disturbances in jacket feed temperature and/or reactor feed flow rate. The MPC-based cascade strategy yields significantly better performance than classical cascade control when operating close to constraints on the jacket flow rate.     

A set based approach to detection and isolation of faults in multivariable systems

In this paper, we propose a set based approach for detecting faults in input/output channels of a closed-loop multivariable system. This approach works with deterministic bounds on measurement noises, disturbances, and fault signals, which may be attractive in some applications. The fault detection problem is formulated as an optimization problem in the state space framework. The dimensionality of the optimization problem increases with time. In order to allow the problem dimension to be fixed at a pre-specified value, a moving horizon based approach is proposed. When the horizon is moved, a parallelotope covering the feasible region of the initial state is propagated by using the ‘Recursive Optimal Bounding Parallelotope’ methodology. Diagnosis of the location of a fault is carried out by using a series of detection tests. The usefulness of the algorithm for detection and diagnosis is demonstrated by using a heat exchanger example.     

Multivariate statistical process control charts for batch monitoring of transesterification reactions for biodiesel production based on near-infrared spectroscopy

This work describes an application of Multivariate Statistical Process Control to monitor soybean oil transesterification. For the development of multivariate control charts, near infrared spectra were acquired in-line during the evolution of ten batches under Normal Operating Conditions. They were then organized in a three-way array (batch í spectral variable í time). This structure was analysed by the two most commonly used approaches to develop batch monitoring schemes for handling such kind of data, referred to as Nomikos-MacGregor (NM) and Wold-Kettaneh-Friden-Holmberg (WKFH), respectively. To assess the performance of the approaches, eight test batches, during which specific interferences were induced, were manufactured. When applied for off-line monitoring, both NM and WKFH correctly pointed out such intentionally produced failures. On the other hand, concerning on-line monitoring, NM exhibited a better fault detection capability than WKFH. Contribution plots were found to highlight the spectral region mostly affected by the disturbances regardless of the modelling strategy resorted to.     

Active fault isolation of nonlinear process systems

This work considers the problem of designing an active fault‐isolation scheme for nonlinear process systems subject to uncertainty. The faults under consideration include bounded actuator faults and process disturbances. The key idea of the proposed method is to exploit the nonlinear way that faults affect the process evolution through supervisory feedback control. To this end, a dedicated fault‐isolation residual and its time‐varying threshold are generated for each fault by treating other faults as disturbances. A fault is isolated when the corresponding residual breaches its threshold. These residuals, however, may not be sensitive to faults in the operating region under nominal operation. To make these residuals sensitive to faults, a switching rule is designed to drive the process states, upon detection of a fault, to move toward an operating point that, for any given fault, results in the reduction of the effect of other faults on the evolution of the same process state. This idea is then generalized to sequentially operate the process at multiple operating points that facilitate isolation of different faults for the case where the residuals are not simultaneously sensitive to faults at a single operating point. The effectiveness of the proposed active fault‐isolation scheme is illustrated using a chemical reactor example and demonstrated through application to a solution copolymerization of methyl methacrylate and vinyl acetate. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2435–2453, 2013     

基于状态的石化企业设备一体化管理系统开发

结合石油化工企业设备管理的现状,对基于状态的设备一体化管理系统开发进行阐述,并针对设备管理的需要就系统主要功能模块进行探究,解决了设备管理中实时监控、在线检测与故障诊断、设备寿命周期管理、设备运行状态显示及故障报警等主要问题,提出了石化企业设备管理的全新方案。     

基于B/S模式的化工储罐在线监测系统的设计与实现

针对C/S结构的SCADA系统软件开放性差的问题,采用B/S三层架构模式,利用微软公司的ASP.NET AJAX框架,结合ADO.NET技术,设计并实现了基于Internet的化工储罐在线监测系统。阐述了在线监测系统的基本结构框架设计,并对各功能模块作了描述,同时运用客户端编程模型实现了页面无刷新地显示实时数据。实际表明,系统运行稳定,可供大量用户在线使用。     

Supervisory control system to enhance partial nitrification in an activated sludge reactor

This work proposes a supervisory control system based on an optimization layer to calculate the optimum pH and dissolved oxygen (DO) set-points for the SISO controller, maintaining the process at stable partial nitrification. Takagi–Sugeno fuzzy multimodels were implemented to estimate ammonium degradation and nitrite accumulation from on-line DO and pH values, and updated using off-line measurements. An activated sludge reactor was operated successfully over 115 consecutive days with the supervisory control system, achieving ammonium degradation and nitrite accumulation values higher than 95% and 80%, respectively. The on-line estimates of the multimodels showed a prediction error of less than 7% at steady state operation, and reflected the tendencies shown in experiment to be caused by changes in pH value and DO concentration.     

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.     

On the role of the necessary conditions of optimality in structuring dynamic real-time optimization schemes

In dynamic optimization problems, the optimal input profiles are typically obtained using models that predict the system behavior. In practice, however, process models are often inaccurate, and on-line model adaptation is required for appropriate prediction and re-optimization. In most dynamic real-time optimization schemes, the available measurements are used to update the plant model, with uncertainty being lumped into selected uncertain plant parameters; furthermore, a piecewise-constant parameterization is used for the input profiles. This paper argues that the knowledge of the necessary conditions of optimality (NCO) can help devise more efficient and more robust real-time optimization schemes. Ideally, the structuring decisions involve the NCO as follows: (i) one measures or estimates the plant NCO, (ii) a NCO-based input parameterization is used, and (iii) model adaptation is performed to meet the plant NCO. The benefit of using the NCO in dynamic real-time optimization is illustrated in simulation through the comparison of various schemes for solving a final-time optimal control problem in the presence of uncertainty.     

Soft sensors model optimization and application for the refinery real-time prediction of toluene content

Industrial facilities nowadays show an increasing need for continuous measurements, monitoring and controlling many process variables. The on-line process analyzers, being the key indicators of process and product quality, are often unavailable or malfunction. This paper describes development of soft sensor models based on the real plant data that could replace an on-line analyzer when it is unavailable, or to monitor and diagnose an analyzer’s performance. Soft sensors for continuous toluene content estimation based on the real aromatic plant data are developed. The autoregressive model with exogenous inputs, output error, the nonlinear autoregressive model consisted of exogenous inputs and Hammerstein–Wiener models were developed. In case of complex real-plant processes a large number of model regressors and coefficients need to be optimized. To overcome an exhaustive trial-and-error procedure of optimal model regressor order determination, differential evolution optimization method is applied. In general, the proposed approach could be, of interest for the development of dynamic polynomial identification models. The performance of the models are validated on the real-plant data.     

一种在线辨识扰动模型的MPC控制器及其在溶剂脱水塔装置中的应用

提出一种滚动时域伪线性回归（MHPLR）算法,不仅降低了算法对检测噪声的敏感程度,而且无须在线求解优化命题,计算负担小。利用MHPLR在线估计一个时间序列模型来描述DMC控制系统不可测扰动的动态特性。将对扰动的预报应用到DMC算法当中,以改善因原误差校正策略而导致的抗扰性能不佳的情况,并以状态空间形式给出了改进后DMC算法的表达式。将改进的DMC控制器应用到某公司的PTA溶剂脱水塔装置上,提升了控制品质,稳定了生产并降低了酸耗。算法的有效性得到了验证。     

离心压缩机在线监控与故障预报分析系统的研究

阐述了离心压缩机在线监控与故障预报分析系统研究的意义,介绍了离心压缩机工作的特点及对监控系统的要求,说明了在线监控与故障预报分析系统的基本配置及支持系统的模块化软件结构。