Dynamic Alarm Management in Next Generation Process Control Systems

In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

With the further integration of the informatization and industrialization, it is a key factor for enterprise to develop them by enterprise information construction. At present, with the development of computer, enterprise informatization generally has formed three layers architecture, which contains: ERP (Enterprise Resource Planning), MES (Manufacturing Execution System) and PCS (Process Control System). MES plays an irreplaceable position in the enterprise informatization. However, different needs of the design for these systems. Then, they lead system not to share information, business process effectively and work together with others. Then finally lead to "Information Island". It has made people to pay more attention to the topic, which is how to implement the exchange of information real-time and accurately between ERP, PCS and MES. So, we analyze some major exchange ways of the present information between MES and other systems in this paper. Then, we propose a flexible and configurable EAI architecture-AiOintegration (All-in-One integration), which has integrated the current methods of exchanging between system information. And AiOintegration implemented information exchange of cross-language and cross-platform between with MES and other systems.

Data processing is a key issue of the design of RFID middleware, and the general processing mode is based on Event. The data processed and output by this solution canpsilat really meet the needs of various application systems; nevertheless, the complex event processing technology can overcome the disadvantage effectively. According to the principles that can make it effective to manage RFID data, this paper proposes a RFID data processing model based on complex event processing (CEP) technique, provides the related definitions, elaborates the function modules involved (the event monitoring module, the buffer module, the event processing module and the event sending/subscribe module) and their solutions.

Cyber-Physical System (CPS) is an emerging intelligent systems, which is considered as a combination of computers and physical world. Events are usually as a natural way for interactions and communications, which occur in the physical world and can be responded in the cyber world. Additionally, using complex event processing (CEP) technology, some complex events can be processed that are better suited for complicated and intelligent CPS. At first, a component-model framework is proposed for modeling CPS architecture based on event-driven pattern operational semantics, which provides a tool to understand the transition based on event-driven pattern in CPS. In order to improve the immense workload on centralized CEP, a mixed master-slaves CEP is proposed. Meanwhile, an architecture of CPS with master-slaves CEP is constructed and its execution mechanism is introduced in detail.

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

The ultimate goal of froth flotation is to increase the concentration of concentrate. The traditional flotation process basically relies on manual experience to adjust the flotation variables. The control of key factors is neither accurate nor timely, and it is difficult to guarantee the production efficiency. Most of the current researches on froth flotation are limited to image processing, and it is difficult to achieve the ideal index of production by relying solely on image detection technology. Faced with this situation, this paper proposes a research method of foam flotation modeling based on complex events for the one-sidedness of image processing. By constructing a complex event processing model of froth flotation, the traditional discrete froth flotation process could be effectively systematic by the event-driven approach, and the separated sensor data and image acquisition data are aggregated into the same dimension. The complex event processing technology is used to process the data associated with the two in parallel, which could optimize the operation of the froth flotation process more comprehensively.

State-of-the-art Complex Event Processing technology (CEP), while effective for pattern matching on event streams, is limited in its capability of reacting in real-time to opportunities and risks detected when monitoring the physical or virtual world. We propose to tackle this problem by embedding active rule support within the CEP engine, henceforth called Active Complex Event Processing technology, or short, ACEP. We design an ACEP infrastructure that integrates the active rule component into the CEP kernel. This not only allows fine-grained but also more efficient rule processing. Based on the infrastructure we develop optimization techniques to improve the responsiveness of our system. We demonstrate the power of ACEP technology by applying it to the development of our real-time healthcare system being deployed in Univ. of Massachusetts Medical School hospital. Through performance experiments using real-world workloads collected from the hospital, we show that our ACEP solution is effective and efficient at supporting business process in event-based systems compared to possible alternatives.

The objectives are to explore the advanced complex data analytics of autonomous transportation in smart cities, improve the industrial production efficiency and product quality, and optimize the industrial production processes. Aiming at the deployment problem of software-defined network (SDN) in distributed cloud data center, the study proposes a spectrum-based SDN deployment algorithm. An industrial complex data model is built. In addition, a complex event processing model is implemented based on the emerging stream data flow execution engine and the emerging Flink stream processing framework. The Flink framework not only performs real-time analysis of fault tolerance but also greatly simplifies the data processing process. Tests of multiple sets of industrial data from iron mine, chemical, and metallurgical plants have proved the operational advantages of the complex event model for the analysis of industrial complex data. Combining the advantages of online filtering, this study proposes a Generalized Robust Least Square (GRLS) data coordination algorithm. The results confirm that the GRLS has better robustness in correcting significant errors. The spectrum-based SDN deployment algorithm solves the problem of SDN domain partition and controller deployment. This proves that complex event processing technology can extract more valuable information from massive and complex data, thereby ensuring the stability of autonomous transportation.KeywordsSmart cityIndustrial environmentAdvanced complex dataData analysisLeast squares algorithmSoftware-defined networkIndustry 4.0Urban planningInternet of ThingsBig data

Networks of the Internet of Things (IoT) nowadays find greater widespread in many domains. Particularities of creation of IoT make the problem of their security monitoring rather actual, it is caused by necessity of processing of big amounts of heterogeneous data in real time. The problem may be solved by means of implementation of the parallel system for security data processing within IoT on the fly basing on complex event processing (CEP) technology. The paper considers basic solutions for creating such a system. The proposed system is oriented for usage of software environment Hadoop and includes data collection, data storage, data normalization and analysis, and data visualization components. The paper discusses the issues of architecture of this system, its implementation and experimental estimation. The experiments showed that the proposed approach to creation of the system on the basis of CEP technology provides suitable scalability and is capable to meet the requirements of processing data on security events in real time in IoT.

A model-driven approach for facilitating user-friendly design of complex event patterns

As an important part of the smart grid, demand respond needs to be developed to meet the requirements of electricity consumption event online monitoring, real-time analysis and automatic response. Firstly, the typical demand response business models are designed and the online analysis context environments are ensuing determined. Then, a three layers framework of demand response just-in-time decision based on complex event processing technology is designed on the basis of the basic input/output event models and general operating rules. Finally, the rule of demand respond just-in-time decision technical in the auxiliary grid load shifting and regional load balancing is verified through intelligent electric equipment real-time operation control experiment. The experimental results show that the proposed technology is helpful to modify the load curve and reduce the peak-valley ratio.

The great advance and affordability of technologies, communications and sensor technology has led to the generation of large amounts of data in the field of the Internet of Things and smart environments, as well as a great demand for smart applications and services adapted to the specific needs of each individual. This has entailed the need for systems capable of receiving, routing and processing large amounts of data to detect situations of interest with low latency, but despite the many existing works in recent years, studying highly scalable and low latency data processing systems is still necessary. In this area, the efficiency of complex event processing (CEP) technology is of particular significance and has been used in a variety of application scenarios. However, in most of these scenarios there is no performance evaluation to show how the system performs under various loads and therefore the developer is challenged to develop such CEP-based systems in new scenarios without knowing how the system will be able to handle different input data rates and address scalability and fault tolerance. This article aims to fill this gap by providing an evaluation of the various versions of one of the most reputable CEP engines—Esper CEP, as well as its integration with two renowned messaging brokers for data ingestion—RabbitMQ and Apache Kafka. For this purpose, we defined a benchmark with a series of event patterns with some of the most representative operators of the Esper CEP engine and we performed a series of tests with an increasing rate of input data to the system. We did this for three alternative software architectures: integrating open-source Esper and RabbitMQ, integrating one instance of Esper enterprise edition with Apache Kafka, and integrating two distributed instances of Esper enterprise edition with Apache Kafka. We measured the usage of CPU, RAM memory, latency and throughput time, looking for the data input rate with which the system overloads for each event pattern and we compared the results of the three proposed architectures. The results have shown a very low CPU consumption for all implementation options and input data rates; a balanced memory usage, quite similar among the three architectures, up to an input rate of 10,000 or 15,000 events per second, depending on the architecture and event pattern, and a quite efficient response time up to 10,000 or 15,000 events per second, depending on the architecture and event pattern. Based on a more exhaustive analysis of results, we have concluded that the different options offered by Esper for CEP provide very efficient solutions for real-time data processing, although each with its limitations in terms of brokers to be used for data integration, scalability, and fault tolerance; a number of suggestions have been drawn out for the developer to take as a basis for choosing which CEP engine and which messaging broker to use for the implementation depending on the of the system in question.

A variety of ubiquitous computing devices, such as radio frequency identification (RFID) and wireless sensor network (WSN), are generating huge and significant events that should be rapidly processed for business excellence. In this paper, we describe how complex event processing (CEP) technology can be applied to ubiquitous process management based on context-awareness. To address the issue, we propose a method for context-aware event processing using event processing language (EPL) statement. Specifically, the semantics of a situation drive the transformation of EPL statement templates into executable EPL statements. The proposed method is implemented in the domain of ubiquitous cold chain logistics management. With the proposed method, context-aware event processing can be realized to enhance business performance and excellence in ubiquitous computing environments. key words: Context-awareness, complex event processing, ubiquitous logistics, RFID, wireless sensor network