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Specialty and fine chemicals are often manufactured in multipurpose batch production plants. Compared to continuous production, these plants offer increased flexibility at the cost of operational complexity. A recipe defines the sequence and process parameters of different batch phases that are needed to transform raw materials into the desired product. In some plants detailed information about the executed recipe is not always captured by data acquisition systems. Knowledge of these phases is essential for optimizing quality and throughput. State‐of‐the‐art data‐driven machine learning techniques can recognize recurrent patterns in noisy time series data, enabling automatic labeling of batch phases based on widely available sensor data. In this review paper, we provide an overview of several machine learning approaches that can be used in an industrial setting. [ABSTRACT FROM AUTHOR]

Identification of quality issues in batch production is relevant for stable and successful production. The identification of systematic quality issues and the identification of root causes is complex and challenging. Powerful multivariate statistical methods exist to perform batch analysis, yet their application is difficult due to data diversity and complexity in model training. Here, insights from data analysis projects on four batch plants are presented and real world challenges are reviewed. This leads to a semi‐automatic process for more efficient batch analysis overcoming the mentioned tedious tasks. [ABSTRACT FROM AUTHOR]

A process plant can have multiple modes of operation due to varying demand, availability of resources, or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterized as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The field Kalman filter (FKF) is a model-based approach, which is adopted in this article for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This article extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for off-line training an FKF monitoring model and online monitoring. The off-line part comprises training an FKF model based on multivariate autoregressive state-space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. Online monitoring, based on the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot-scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification. [ABSTRACT FROM AUTHOR]

As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indexes, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indexes as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indexes. The ability of the proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman process benchmark case study. [ABSTRACT FROM AUTHOR]

This paper considers the energy dispatching optimization of a grid-connected microgrid in a park in the city of Shanghai in a distributed framework, in order to improve its economic and environment-friendly performance. The microgrid is composed of distributed generations (DGs), energy storage systems, and shiftable loads. Some properties of the microgrid make the dispatching problem difficult. For example, the disturbance in renewable energy is unpredictable; the operation of shiftable loads is discrete and the relationship between the generated power and the total operation cost is nonlinear. A novel distributed model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid in this paper, where there is a local MPC for each of the following entities: DGs, storage batteries, and shiftable loads. In this method, the centralized mix-integer programming problem of microgrid energy dispatching is converted into several interacted nonlinear programming problems and integer programming problems, and subsystem-based MPCs coordinate with each other via iteratively minimizing the cost over the entire system. In this way, the realization of plug-and-play property becomes easier and the computational load is reduced. The numerical results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Uniformly distributed uncertainty exists in industrial process; additive error introduced by quantization is an example. To be able to handle additive uniform and Gaussian measurement uncertainty simultaneously in system identification, the Flat-topped Gaussian distribution is considered in this paper as an alternative to the Gaussian distribution. To incorporate this type of uncertainty in the maximum likelihood estimation framework, the explicit form of its density function is of necessity. This work proposes an approach for obtaining both the functional structure and corresponding parameter estimation of Flat-topped Gaussian distribution by a moment fitting strategy. The performance of the proposed approximation function is verified by comparison to the Flat-topped Gaussian distributed random variable with different Gaussian and uniform components. Results of numerical simulations and industrial applications in system identification are presented to verify the effectiveness of the Flat-topped Gaussian distribution for noise distribution in handling additional uniform uncertainty. [ABSTRACT FROM AUTHOR]

Industrial Cyber–Physical System (CPS) is an emerging approach towards value creation in modern industrial production. The development and implementation of industrial CPS in real-life production are rewarding yet challenging. This paper aims to present a concept to develop, commercialize, operate, and maintain industrial CPS which can motivate the advance of the research and the industrial practice of industrial CPS in the future. We start with defining our understanding of an industrial CPS, specifying the components and key technological aspects of the industrial CPS, as well as explaining the alignment with existing work such as Industrie 4.0 concepts, followed by several use cases of industrial CPS in practice. The roles of each component and key technological aspect are described and the differences between traditional industrial systems and industrial CPS are elaborated. The multidisciplinary nature of industrial CPS leads to challenges when developing such systems, and we present a detailed description of several major sub-challenges that are key to the long-term sustainability of industrial CPS design. Since the research of industrial CPS is still emerging, we also discuss existing approaches and novel solutions to overcome these sub-challenges. These insights will help researchers and industrial practitioners to develop and commercialize industrial CPS. [ABSTRACT FROM AUTHOR]