An online method for detection and reduction of chattering alarms due to oscillation

Chattering alarms, which repeatedly and rapidly make transitions between alarm and normal states in a short time period, are the most common form of nuisance alarms that severely degrade the performance of alarm systems for industrial plants. One reason for chattering alarms is the presence of oscillation in process signals. The paper proposes an online method to promptly detect the chattering alarms due to oscillation and to effectively reduce the number of chattering alarms. In particular, a revised chattering index is proposed to quantify the level of chattering alarms; the discrete cosine transform-based method is used to detect the presence of oscillation; two mechanisms by adjusting the alarm trippoint and using a delay timer are exploited to reduce the number of chattering alarms. An industrial case study is provided to illustrate the effectiveness of the proposed method.     

CDI二维浓度传质模型的建立以及实验验证

电容去离子技术(CDI)作为一种新兴的水处理脱盐技术,因其具有诸多优异性能而受到广泛关注。厘清CDI的传质机制是理论研究的焦点。通过对已有经验模型的分析,从沿流向方向和垂直流向方向两个方面,考虑了电场迁移以及传质扩散等因素,提出了一种新的CDI二维浓度传质模型,对CDI在除盐过程中的离子扩散及浓度分布规律进行模拟探究,根据实际实验结果对该模型进行实验验证及参数修正。结果表明,该二维模型可以较好地模拟CDI除盐过程。将该二维模型利用COMSOL软件进行模拟,观测CDI在除盐过程中的内部浓度变化。并针对存在问题提出合理化建议,为CDI技术的未来发展提供理论支撑。     

CDI二维浓度传质模型的建立以及实验验证

电容去离子技术(CDI)作为一种新兴的水处理脱盐技术,因其具有诸多优异性能而受到广泛关注。厘清CDI的传质机制是理论研究的焦点。通过对已有经验模型的分析,从沿流向方向和垂直流向方向两个方面,考虑了电场迁移以及传质扩散等因素,提出了一种新的CDI二维浓度传质模型,对CDI在除盐过程中的离子扩散及浓度分布规律进行模拟探究,根据实际实验结果对该模型进行实验验证及参数修正。结果表明,该二维模型可以较好地模拟CDI除盐过程。将该二维模型利用COMSOL软件进行模拟,观测CDI在除盐过程中的内部浓度变化。并针对存在问题提出合理化建议,为CDI技术的未来发展提供理论支撑。     

配煤技术的经济分析

针对煤的质量是影响德士古气化经济运行的主要因素,结合湿法气流床煤气化生产经验,介绍配煤技术的应用,对合理配煤从经济上进行分析,提出配煤建议。     

一种改进的遗传算法在间歇化工过程设计中的应用

将遗传算法（Genetic Algorithm，GA）和回溯法相结合建立起数学模型对间歇化工过程进行设计，来达到减少设备投资并且提高设备利用率的目的。与一般的启发式方法相比，该模型的搜索范围大，精度高，更适合解决复杂的问题。并且引入自我优化机制和惩罚操作及时修正种群中出现的劣质基因，使种群能够顺利繁衍下去。结果表明，该模型在计算结果、收敛速度和计算速度方面得到了进一步优化。     

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis (FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder, a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.     

Uncertainty‐conscious methodology for process performance assessment in biopharmaceutical drug product manufacturing

This work presents an uncertainty‐conscious methodology for the assessment of process performance—for example, run time—in the manufacturing of biopharmaceutical drug products. The methodology is presented as an activity model using the type 0 integrated definition (IDEF0) functional modeling method, which systematically interconnects information, tools, and activities. In executing the methodology, a hybrid stochastic–deterministic model that can reflect operational uncertainty in the assessment result is developed using Monte Carlo simulation. This model is used in a stochastic global sensitivity analysis to identify tasks that had large impacts on process performance under the existing operational uncertainty. Other factors are considered, such as the feasibility of process modification based on Good Manufacturing Practice, and tasks to be improved is identified as the overall output. In a case study on cleaning and sterilization processes, suggestions were produced that could reduce the mean total run time of the processes by up to 40%. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1272–1284, 2018     

基于多模型外部分析和Greedy-KP1M的多工况过程监控

传统的基于多元统计过程监控方法都是假设过程处于单一工况下,而随着进料负荷、产品组分等过程参数的改变,生产过程的工况也随之改变,传统方法便不再适用。针对工业过程中的多工况监控问题,提出了一种基于多模型外部分析和Greedy-KP1M的多工况过程监控方法。首先针对传统外部分析方法描述能力不足的问题,用多模型局部建模代替单一模型来获得更好的描述能力,同时获得监控残差,通过对残差进行监控从而去除多工况的影响,进而将核单簇可能性聚类(KP1M)用于对残差的监控上。该方法拥有和支持向量数据描述(SVDD)相当的监控效果,但计算复杂度却远远小于SVDD。同时,采用Greedy方法提取特征样本,进一步降低了算法计算复杂度。最后将上述方法应用在TE模型和乙烯裂解炉的监控上,结果证明了该方法的有效性。     

超声波在聚合物成型加工中降粘作用的研究及应用

对超声波进行了简单的介绍,对其在聚合物成型加工中的降粘机理进行了一定的研究以及对它在聚合物成型加工中的应用进行了综述。在聚合物的成型加工中,超声波的合理施加可以大幅度的降低聚合物熔体的粘度,降低加工设备的要求和条件,更有利于高粘度聚合物的成型加工。     

An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes

With the development of industrial automation, the requirement of abnormal early warning in the industrial production process is getting higher and higher. Facing complex chemical processes, traditional fault detection and abnormal early warning methods have low detection efficiency and poor real-time performance. Therefore, this paper analyzes and studies fault detection and abnormal early warning methods, and puts forward an improved intelligent early warning method based on the moving window sparse principal component analysis (MWSPCA) suitable for complex chemical processes. The sparse principal component analysis algorithm is used to establish the initial early warning model, and then the moving window is used to update the early warning model, which makes the early warning model more suitable for the characteristics of time-varying data. Furthermore, the proposed method reduces the false alarm rate and missed alarm rate of the early warning, and improves the real-time performance of the early warning model. Finally, the feasibility and the validity of the proposed method are verified by the TE process and the oil drilling process. The experiment results show that the proposed method can reduce the risk of complex chemical processes.     

Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm

Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper deals with a data driven method for alarm flood pattern matching. An alarm flood is represented by a time-stamped alarm sequence. A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods. The effectiveness of the algorithm is validated by a case study on actual chemical process alarm data.     

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.     

在线自适应波长选择方法及其在汽油调和过程中的应用

近红外光谱分析技术作为一种非侵入性的分析手段在工业上得到了广泛应用。然而,大多数近红外模型的波长选择方法是离线建立的,无法有效跟踪过程特性的变化。提出了一种新的在线自适应波长选择方法——在线自适应区间高斯过程回归波长选择方法（adaptive interval Gaussian process regression, AIGPR）,并用于汽油调和过程中的近红外模型的建立。该方法可以根据待测样本的特性对波长结构进行调整。为了降低在线应用的计算成本,该方法分为离线和在线两个部分,离线部分将光谱分割成若干个波长区间,并在每个波长区间上建立局部模型,为在线应用做准备;在线部分中根据划分规则将采样得到待测样本光谱进行分割并代入相应的局部模型中计算波长区间重要性指标,获得最优波长区间。在汽油辛烷值的光谱数据上证明了该方法的有效性。与重要变量投影法和改进的相关系数法相比,该方法具有更好的性能。     

Machine learning-based predictive control of nonlinear processes. Part I: Theory

This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on a data set generated from extensive open-loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov-based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed-loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed-loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.     

Fundamentals towards a Modular Microstructured Production Plant

For years, microtechnology is being considered as an emerging technique for chemical engineering tasks to overcome safety issues corresponding to high volumes and gaining higher selectivities and yields in reaction technology. Whereas in reaction technology a broad variety of microstructured equipment is available, in product purification/separation adequate equipment is missing. Research is focused on modular fast and flexible smaller production plants being operated continuously instead of batchwise in order to reduce engineering efforts and time‐to‐process. To cope with these demands, an appropriate definition of modules, which could be easily chosen and combined, is inevitable. In addition, these modules have to be well characterized concerning fluid dynamics and separation performance. This paper focuses on the characterization of available modules/devices. A standard method and analysis of the results concerning manufacturing accuracy and operation range is proposed. Miniplant technology is described as an efficient tool to validate process concepts proposed by process simulation studies. Necessary model parameters are determined for industrial complex mixtures in miniaturized laboratory equipment. Parameters are calculated model based to gain maximal accuracy. State of the art of miniplant technology is described and basic characteristic data are presented.     

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.     

Efficient meta-modelling of complex process simulations with time-space-dependent outputs

Process simulations can become computationally too complex to be useful for model-based analysis and design purposes. Meta-modelling is an efficient technique to develop a surrogate model using “computer data”, which are collected from a small number of simulation runs. This paper considers meta-modelling with time-space-dependent outputs in order to investigate the dynamic/distributed behaviour of the process. The conventional method of treating temporal/spatial coordinates as model inputs results in dramatic increase of modelling data and is computationally inefficient. This paper applies principal component analysis to reduce the dimension of time-space-dependent output variables whilst retaining the essential information, prior to developing meta-models. Gaussian process regression (also termed kriging model) is adopted for meta-modelling, for its superior prediction accuracy when compared with more traditional neural networks. The proposed methodology is successfully validated on a computational fluid dynamic simulation of an aerosol dispersion process, which is potentially applicable to industrial and environmental safety assessment.     

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.     

Assessing plant design with regard to MPC performance

Model Predictive Control is ubiquitous in the chemical industry and offers great advantages over traditional controllers. Notwithstanding, new plants are being projected without taking into account how design choices affect the MPC’s ability to deliver better control and optimization. Thus a methodology to determine if a certain design option favours or hinders MPC performance would be desirable. This paper presents the economic MPC optimization index whose intended use is to provide a procedure to compare different designs for a given process, assessing how well they can be controlled and optimised by a zone constrained MPC. The index quantifies the economic benefits available and how well the plant performs under MPC control given the plant’s controllability properties, requirements and restrictions. The index provides a monetization measure of expected control performance.This approach assumes the availability of a linear state-space model valid within the control zone defined by the upper and lower bounds of each controlled and manipulated variable. We have used a model derived from simulation step tests as a practical way to use the method. The impact of model uncertainty on the methodology is discussed. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of a MPC to reach dynamic equilibrium near process restrictions, which in turn increases product quality giveaway and costs. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the applicability of the index.