Full-scale Demonstration of Multi-process Sensor at a Cycling PC-fired Boiler

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.

With the aim of achieving high accuracy for analog devices, we developed an advanced process control (APC) system for poly silicon resistance control in implantation steps. The effect of this system has been successfully demonstrated in our mass production. Moreover LP-CVD equipment for poly silicon deposition was enhanced with EES. This can improve the equipment fault detection ability for stabilizing this APC system.

The energy transition places new demands on power-generation assets: higher flexibility, tighter integration with variable renewable resources, and continuous pressure to reduce CO₂ emissions and operating costs. Industrial automation—understood broadly as the integration of sensors, distributed controllers, advanced process control and data-driven decision systems—is a practical enabler of that transition. This article argues that modern automation architectures, when combined with real-time control, predictive analytics and digital-twin models, deliver measurable reductions in energy consumption and fuel losses, improve plant ramping performance, and extend equipment life through condition-based maintenance. Evidence from recent literature and applied studies is reviewed to show how high-fidelity sensing and state estimation, advanced regulatory and economic model-predictive controllers, and closed-loop digital twins with real-time orchestration platforms work together to improve efficiency in thermal, hydro and hybrid power plants. The synthesis highlights implementation challenges, such as interoperability, computational requirements and cybersecurity, and suggests pragmatic pathways for staged adoption that prioritize high-impact subsystems and verifiable energy savings.

Current process control systems are composed of a large number of components and subsystems operating at different layers of the control system architecture model (i.e. measurement and control devices, Distributed Control Systems, Advanced Process Control systems, and Manufacturing Execution Systems). The IMC-AESOP project aims at designing the next generation architecture of process automation systems. In order to ensure system scalability and modularity, the new architectural design follows the SOA (Service-Oriented Architecture) design principles. Moreover, the design assumes adoption of various technologies with the aim to enable the control systems to meet all functional and performance requirements. The CEP (Complex Event Processing) technology has been selected for being able to provide efficient asynchronous communication (within and across architecture layers) and the capability of temporal reasoning over large amounts of system-generated events. This paper describes the intermediate results of the IMC-AESOP project, outlining the architectural concepts related to the use of the SOA and CEP technologies in the context of advanced alarm management applications - alarm load shedding and state-based alarming.Keywordsnext generation DCS/SCADAsystem of systemsprocess monitoring and controlalarm managementdynamic alarmingstate-based alarmingalarm load sheddingsoftware-oriented architectureSOAcomplex event processingCEPIMC-AESOP project

Shrinking technology nodes and smaller process margins require improved photolithography overlay control. Generally, overlay measurement results are modeled with Cartesian polynomial functions for both intra-field and inter-field models and the model coefficients are sent to an advanced process control (APC) system operating in an XY Cartesian basis. Dampened overlay corrections, typically via exponentially or linearly weighted moving average in time, are then retrieved from the APC system to apply on the scanner in XY Cartesian form for subsequent lot exposure. The goal of the above method is to process lots with corrections that target the least possible overlay misregistration in steady state as well as in change point situations. In this study, we model overlay errors on product using Zernike polynomials with same fitting capability as the process of reference (POR) to represent the wafer-level terms, and use the standard Cartesian polynomials to represent the field-level terms. APC calculations for wafer-level correction are performed in Zernike basis while field-level calculations use standard XY Cartesian basis. Finally, weighted wafer-level correction terms are converted to XY Cartesian space in order to be applied on the scanner, along with field-level corrections, for future wafer exposures. Since Zernike polynomials have the property of being orthogonal in the unit disk we are able to reduce the amount of collinearity between terms and improve overlay stability. Our real time Zernike modeling and feedback evaluation was performed on a 20-lot dataset in a high volume manufacturing (HVM) environment. The measured on-product results were compared to POR and showed a 7% reduction in overlay variation including a 22% terms variation. This led to an on-product raw overlay Mean + 3Sigma X&Y improvement of 5% and resulted in 0.1% yield improvement.

In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC) in which the shape of the processed feature is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual fab engineers, which cause difficulties in the QC. To overcome this problem, we have developed an automatic calculation method of the optimal recipe parameters with Advanced Process Control (APC) system with the stacking model method in order to solve these problems. In the development process, we have also performed comparison of two gap scoring methods. In the on-site demonstration of this system, we have further optimized the exploration method of the recipe parameters.

Evaluation of Nonlinear Inferential Models to Estimate the Products Quality of Industrial Distillation Process

To attain quick turn-around time (TAT) and high yield, it is very important to remove all the problems affecting the semiconductor volume production line. For this purpose, we have used a lithography management system (LMS) as an advanced process control system. The LMS stores the critical dimension and overlay inspection results as well as the log data of the exposure tool in a relational database. This enables a quick and efficient grasp of the productivity under the present conditions and helps to identify the causes of errors. Furthermore, we developed a mining tool, called a log data extraction and correlation miner (LMS-LEC), for factor analysis on the LMS. Despite low correlation between all data, a high correlation may exist between parameters in a certain data domain. The LMS-LEC can mine such correlations easily. With this tool, we can discover previously unknown error sources that have been buried in the vast quantity of data handled by the LMS and thereby increase of the effectiveness of the exposure and inspection tool. The LMS-LEC is an extremely useful software mining tool for “equipment health” monitoring, advanced fault detection, and sophisticated data analysis.

An advanced multivariable in-line process control system, which combines traditional statistical process control (SPC) with feedback control, has been applied to the CVD tungsten process on an Applied Materials reactor. The goal of the model-based controller is to compensate for shifts in the process and maintain the wafer-state responses on target. The controller employs measurements made on test wafers to track the process behavior. This is accomplished by using model-based SPC, which compares the measurements with predictions obtained from process models. The process models relate the equipment settings to the wafer-state responses of interest. For CVD tungsten, a physically-based modeling approach was employed based on the reaction rate for the H/sub 2/ reduction of WF/sub 6/. The Arrhenius relationship for the kinetic model was linearized so that empirical modeling techniques could be applied. Statistically valid models were derived for deposition rate, film stress, and bulk resistivity using stepwise least-squares regression. On detecting a statistically significant shift in the process, the controller calculates adjustments to the settings to bring the process responses back on target. To achieve this, two additional test wafers are processed at slightly different settings than the current recipe. This local experiment allows the models to be updated to reflect the current process state. The model updates are expressed as multiplicative or additive changes in the process inputs and a change in the model constant. This approach for adaptive control also provides a diagnostic capability regarding the cause of the process shift. The adapted models are used by an optimizer to compute new settings to bring the responses back to target. The optimizer is capable of incrementally entering controllables into the strategy, reflecting the degree to which the engineer desires to manipulate each setting. The capability of the controller to compensate for induced shifts in the CVD tungsten process is demonstrated. Targets for film bulk resistivity and deposition rate were maintained while satisfying constraints on film stress and WF/sub 6/ conversion efficiency. The ability of the controller to update process models during routine operation is also investigated. The tuned process models better predict the process behavior over time compared to the untuned models and lead to improved process capability.

An advanced digital control and monitoring system for turbomachinery is introduced. Important aspects for the design of anti-surge control systems, such as the influence of the controller program execution time on the performance, are investigated. The influence of the gas composition (molecular weight) on the compressor surge line is demonstrated. Control laws are discussed. The design basis of an ultra-high speed anti-surge and speed control system is shown and the data-logging features are demonstrated on the log of a compressor surge. The paper closes describing some dynamic process simulation being performed using the controller hardware and software for process simulations.

CMOS 65nm technology node requires the introduction of advanced materials for critical patterning operations. The study is focused on the multilayer Anti Reflective Coating (ARC) stack, used in photolithography, for the gate patterning such as Advanced Patterning Film (APF). The interest on this new and complex ARC stack lies in the benefit to guarantee low CD dispersion thanks to a better reflectivity control and resist budget which leads to a larger lithographic process window. However, it implies numerous metrology challenges. The paper deals with the challenges of monitoring the gate Critical Dimension (CD) on this stack. The validation of the scatterometry model versus stack thicknesses and indexes variations, through experiments, is also described. The final result is the complete characterization of the materials for thickness and scatterometry CD control, for photo feedback and for etch feed-forward deployment in an industrial mode. The analysis shows that scatterometry measurements on a standard 65 nm gate process ensure a better effectiveness than the CD Scanning Electron Microscopy (SEM) ones when injected in the Advanced Process Control (APC) system from photo to etch.

In the course of Industry 4.0, Predictive Maintenance (PdM) continues to arouse strong interest as a research topic. In particular semiconductor manufacturers could capitalize on the broad availability of data from pervasive Advanced Process Control (APC) systems regarding the conduct of maintenance. Analysis of related work has shown that research primarily concentrates on the technical implementation of PdM, but lacks antecedent consideration of its potential impacts on a supply chain with respect to operations and economics. The presented work, therefore, aims to investigate the operational and economic impacts of PdM for semiconductor manufacturing on the corresponding supply chain by means of discrete-event simulation.

This paper describes five loss of control accidents involving commercial aircraft, and derives from those accidents three principles to consider when developing a potential safety case for an advanced flight control system for commercial aircraft. One, among the foundational evidence needed to support a safety case is the availability to the control system of accurate and timely information about the status and health of relevant systems and components. Two, an essential argument to be sustained in the safety case is that pilots are provided with adequate information about the control system to enable them to understand the capabilities that it provides. Three, another essential argument is that the advanced control system will not perform less safely than a good pilot. (6 pages)

The paper addresses the development and implementation of a distributed and hierarchized control system for processes in food industry. More precisely, the wheat grinding and bread production processes that take place at the Mill and bread factory Calafat, Romania, are envisaged. Two solutions based on the DCS/SCADA (Distributed Control System / Supervisory Control and Data Acquisition) architecture are proposed. The first DCS-SCADA structure is a hierarchical monitoring and control system based on digital controllers, data acquisition and computer system. The second structure is a DCS-SCADA hierarchical monitoring and control system based on compact data acquisition and control systems. The configuration and the functionality of the DCS levels are described. Also, the communication interface between the primary control loops and the superior hierarchic levels is presented. The proposed control structures allow the improvement of the overall system currently implemented at the mill and bread factory.