Chapter 8 - Furnace control and safety

For the first time, a layer thickness sensor has been integrated into a vertical furnace for in situ sensing of the layer growth as well as for post process control of the batch. Because of its high accuracy and versatility, an in situ spectroscopic ellipsometer (SE) was selected. The adaption of the SE to the vertical furnace was performed with only minor modifications to the furnace geometry. The ellipsometer light beam is guided through the base plate into the furnace tube and directed onto the wafer by quartz glass prisms operated in total internal reflection (TIR) mode. This arrangement introduces an additional phase shift in the polarization state of the light which can be determined and subtracted from the measured phase shift. The ellipsometer setup is mechanically fixed to the boat loader. SE measurements therefore can be performed with the wafer boat out of the furnace tube as well as during the process run with the wafer boat inserted. The SE thus can be used for in situ end-point detection during layer growth and also for post process measurements on selected wafers of the batch during the unloading sequence. A realtime controller and a run-by-run controller integrated into the furnace controller utilize the layer thickness data measured in the in situ and in the post process mode, respectively, for immediate and automated correction of the parameter settings during the actual process run and for the following process run. The advantages of this novel furnace control system such as a reduction in time needed for process optimization, the avoidance of monitor wafers and a better control in integrated multilayer processing can be beneficial for future thermal batch/minibatch processing.

Fuel sparing: Control of industrial furnaces using process gas as supplemental fuel

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.

Rule Based Combustion Disturbance Prediction and Control System

7 - Distributed control systems (DCS)

Distributed Control System (DCS) has been widely recognized in the field of modern thermal power thermal control, and has significant effects in the application process. Today, the monitoring of electrical quantities is included into the DCS system, thus replacing the traditional "one to one" manual mode of operation in electrical control. It is to further improve the main works of the station automation and control systems. Combined with the actual project needs, this study focuses on the components of electrical control included in DCS system, the implementation of software and hardware features, and the measures to improve reliability. In addition, useful discussions have been made for some technical problems. In the DCS system, the electrical control inclusion is not just a perfection thermal power plant electrical control system, but a big improvement as well. In addition, it is a new attempt. At present, most of the electrical control parts of control system included into the DCS system with the manual operation are basically cancelled, but there are some problems, for example many of the designs do not match with the original operating habits, etc., which will have a certain impact on the tradition. In the transformation process of system, some of the designs are good but cannot be achieved currently. They still need further research and improvement, for example how can the electrical control of system enter into the DCS in a better manner, how can the display of light texts upon failure or abnormal time weaken electric signals in a more comprehensive and rapid manner, what are the new requirements for anti-interference, and other sequential control functions to be improved and perfected.

This article provides a framework for understanding the nature, role, functioning, design, and effects of organizational oversight systems. Using a case study with elements recognizable to an academic audience, the authors explore how a dean of a fictitious School of Medicine might use organizational control structures to develop effective solutions to global disarray within the academic medical center. Organizational control systems are intended to help influence the behavior of people as members of a formal organization. They are necessary to motivate people toward organizational goals, to coordinate diverse efforts, and to provide feedback about problems. The authors present a model of control to make this process more visible within organizations. They explore the overlap among academic medical centers and large businesses-for instance, each is a billion-dollar enterprise with complex internal and external demands and multiple audiences. The authors identify and describe how to use the key components of an organization's control system: environment, culture, structure, and core control system. Elements of the core control system are identified, described, and explored. These closely articulating elements include planning, operations, measurement, evaluation, and feedback systems. Use of control portfolios is explored to achieve goal-outcome congruence. Additionally, the authors describe how the components of the control system can be used synergistically by academic leadership to create organizational change, congruent with larger organizational goals. The enterprise of medicine is quickly learning from the enterprise of business. Achieving goal-action congruence will better position academic medicine to meet its multiple missions.

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.

Searching for more efficient ways to improve process quality and increase plant throughput has encouraged changes in the control system architecture toward greater distribution. A distributed control system, illustrated in Fig.15.1, can be defined as control systems in which functions around a plant are distributed into computing nodes (i.e., control computers and smart devices) that are physically separated; and all the computing nodes are interconnected by digital communication networks (e.g., fieldbuses). Distributed control systems are now found in many industrial fields, such as oil and gas production (Hars, 1994), automobiles (Hansson et al., 1997), and gas turbine engine control (Shaffer, 1999). Advances in distributed control system technology have been driven by the ever increasing performance of smart field devices and fieldbuses using smaller, less expensive and more functional microprocessors. In fact, the goal of a distributed control system is the same as its centralised counterpart; that is, to get input from the plant through its sensors and give output using its actuators. However, the use of field-buses and smart devices introduces many advantages into a distributed control system over a centralised one. Not only does a distributed architecture offer reduced wiring and simplified maintenance, it also provides the opportunity to implement different control strategies. Consequently, the emergence of distributed control systems is promoting changes in control systems design issues. One of the key changes from conventional control techniques is driven by the local processing power. In distributed architectures, smart devices employing a built-in microprocessor with a fieldbus interface offer the possibility of the faster and more reliable self-diagnosis and self-compensation. Several researchers (Henry 1993; Koscielny et al 1997) have discussed the benefits of local fault-diagnosis. Extra information about the element status reported through a fieldbus network is also very beneficial for reconfigurable control. For example, Lee et al. (2001) presented an on-line retuning strategy for PI control to tolerate actuator faults using this information.

Within industry today, advancements in process and control technologies have given way to differing control systems in the form of programmable logic controllers (PLC) and distributed control systems (DCS). Over the past decade, PLCs have now been able to bridge certain gaps within process and control systems that were capabilities only typically found within DCS environments. This paper will provide an overview of various control system strategies and design considerations with regard to both PLC and DCS schemes within petrochemical applications.

Digital twins present revolutionary potential in smart manufacturing and production. However, their current application in distributed control systems is minimal and largely unexplored. By applying digital twins to distributed control systems, distributed intelligent sensing and control systems may be achieved. These systems are fully automated and self-managing, making them a valuable asset.In this paper, we provide a short literature review which establishes the definition, application, and implementation of digital twins in smart manufacturing and production. Based on this review, we propose their application in transforming distributed control systems into distributed intelligent and sensing control systems. We identify features of a digital twin which will be of greatest use in a distributed control system, and discuss our current research direction aimed at interfacing these control systems with digital twins.KeywordsDigital twinSmart intelligence distributionControl system

Remote control process system with distributed time-delay has attracted much attention in different fields. In this paper, non-linear remote control of a single tank process system with wireless network is considered. To deal with the distributed time-delay in a large-scale plant, the time-delay compensation controller based on DCS devices is designed by using operator theory and particle filter. Distributed control system (DCS) device is developed to monitor and control from the central monitoring room to each process. The particle filter is a probabilistic method to estimate unobservable information from observable information. First, remote control system and experimental equipment are introduced. Second, control system based on an operator theory is designed. Then, process system with distributed time-delay using particle filter is carried out. Finally, the actual experiment is conducted by using the proposed time-delay compensation controller. When estimating with the proposed method, the result is close to the case in which the distributed time-delay does not exist. The effectiveness of the proposed control system is confirmed by experiment results.

<div class="htmlview paragraph">A new diesel engine system adopting alternative combustion with rich and near rich combustion, and an airflow-dominant control system for precise combustion control was used with a 4-way catalyst system with LNT (lean NOx trap) to achieve Tier II Bin 5 on a 2.2L TDI diesel engine. The study included catalyst temperature control, NO<sub>x</sub> regeneration, desulfation, and PM oxidation with and without post injection. Using a mass-produced lean burn gasoline LNT with 60,000 mile equivalent aging, compliance to Tier II Bin 5 emissions was confirmed for the US06 and FTP75 test cycles with low NVH, minor fuel penalty and smooth transient operation.</div>

We review practical case studies of a developing method of highly reliable real-time embedded control systems using a CPU model-based hardware/software co-simulation. We take an approach that enables us to fully simulate a virtual mechanical control system including a mechatronics plant, microcontroller hardware, and object code level software. This full virtual system approach simulates control system behavior, especially that of the microcontroller hardware and software. It enables design space exploration of microarchitecture, control design validation, robustness evaluation of the system, software optimization before components design. It also avoids potential problems. The advantage of this work is that it comprises all the components in a typical control system, enabling the designers to analyze effects from different domains, for example mechanical analysis of behavior due to differences in controller microarchitecture. To further improve system design, evaluation and analysis, we implemented an integrated behavior analyzer in the development environment. This analyzer can graphically display the processor behavior during the simulation without affecting simulation results such as task level CPU load, interrupt statistics, and the software variable transition chart. It also provides useful information on the system behavior. This virtual system analysis does not require software modification, does not change the control timing, and does not require any processing power from the target microcontroller. Therefore this method is suitable for real-time embedded control system design, in particular automotive control system design that requires a high level of reliability, robustness, quality, and safety. In this study, a Renesas SH-2A microcontroller model was developed on a CoMET™platform from VaST Systems Technology. An electronic throttle control (ETC) system and an engine control system were chosen to prove this concept. The electronic throttle body (ETB) model on the Saber® simulator from Synopsys® and the engine model on MATLAB®/Simulink®simulator from MathWorks can be simulated with the SH-2A model using a newly developed co-simulation interface between MATLAB®/Simulink® and CoMET™. Though the SH-2A chip was being developed as the project was being executed, we were able to complete the OSEK OS development, control software design, and verification of the entire system using the virtual environment. After releasing a working sample chip in a later stage of the project, we found that such software could run on both actual ETC system and engine control system without critical problem. This demonstrates that our models and simulation environment are sufficiently credible and trustworthy.