Microcomputer Control of a Printing Ink Plant

While networks of cooperating embedded devices are becoming more and more present in today's digital world, the development of software at process control and management levels must face the heterogeneity and dynamic of the components of these distributed systems. This paper presents a service oriented model that enhances interoperability between low-level devices and control level applications. This model has been applied to the development of a prototype application named FoxController that can be deployed on embedded systems and is compliant to the Devices Profile for Web Service (DPWS) specifications. FoxController simplifies the setting up of flexible and distributed data acquisition and control systems.

Thermocompression bonding is a unique process in semiconductor manufacturing since it essentially bridges the manufacturing space between front end semiconductor processes and back end of line packaging. The amount of potential data generated from a TCB process is enormous and the opportunities for process monitoring and control are significant. In a conventional mass reflow process temperature and oxygen levels can be tracked and periodic monitors can be run through the oven to verify profiles, but process data for individual die is not available. In a local reflow TCB process there is full material tracking back to the source wafer with die alignment, temperature, force, time to melt, stack layer position, and placement position logged for each die processed. Excursions in the process can be detected quickly and with the proper process controls, measures can be implemented to contain potential risk. In high reliability applications data logging and traceability is available for each die bonded, but this data is also generated for all standard commercial applications. Thermocompression bonding is perceived as a back end assembly process but the level of process control can be similar to that used in front end semiconductor manufacturing. This paper will review methods available for process control in advanced TCB processes suitable for single die, 2.5D and 3D packages.

Process and Production Control Package

Nanoimprint Lithography (NIL) has demonstrated its value in manufacturing nanostructures with extremely tight tolerances for the optics industry. Doing NIL process on single samples in the lab has been displayed for a long time in industry and academia. Scaling up from one good sample to full wafer and wafer to wafer uniformity for many wafers in a row, has some obstacles to overcome. In this paper, we discuss the process repeatability of manufacturing nanostructures starting from basic line/space through various other periodic and meta structures. Demonstrating the manufacturing capability using NIL for pattering also includes the other related process steps such as thin films and etching. We discuss key metrology, process control characterization, and process stability which include thin film RI uniformity, master to print replication uniformity, and post etch structural critical dimensions uniformity (CDU). Our NIL process has achieved 1.0nm σ on line widths at 45nm. This precision of replication includes all variation introduced from multiple stamps, wafer to wafer prints, and multiple sites within the wafer. This level of process control at the masking layer needs to be maintained for the finished structure.

Automatic rolling process is a high-speed system which always requires high-speed control and communication capabilities. Meanwhile, it is also a typical complex electromechanical system; distributed control has become the mainstream of computer control system for rolling mill. Generally, the control system adopts the 2-level control structure—basic automation (Level 1) and process control (Level 2)—to achieve the automatic gauge control. In Level 1, there is always a certain distance between the roll gap of each stand and the thickness testing point, leading to the time delay of gauge control. Smith predictor is a method to cope with time-delay system, but the practical feedback control based on traditional Smith predictor cannot get the ideal control result, because the time delay is hard to be measured precisely and in some situations it may vary in a certain range. In this paper, based on adaptive Smith predictor, we employ multiple models to cover the uncertainties of time delay. The optimal model will be selected by the proposed switch mechanism. Simulations show that the proposed multiple Smith model method exhibits excellent performance in improving the control result even for system with jumping time delay.

Recent literature has focused on the reduction of laboratory errors and the potential impact on patient management. This study assessed the intelligent, automated preanalytical process-control abilities in newer generation analyzers as compared with older analyzers and the impact on error reduction. Three generations of immuno-chemistry analyzers were challenged with pooled human serum samples for a 3-week period. One of the three analyzers had an intelligent process of fluidics checks, including bubble detection. Bubbles can cause erroneous results due to incomplete sample aspiration. This variable was chosen because it is the most easily controlled sample defect that can be introduced. Traditionally, lab technicians have had to visually inspect each sample for the presence of bubbles. This is time consuming and introduces the possibility of human error. Instruments with bubble detection may be able to eliminate the human factor and reduce errors associated with the presence of bubbles. Specific samples were vortexed daily to introduce a visible quantity of bubbles, then immediately placed in the daily run. Errors were defined as a reported result greater than three standard deviations below the mean and associated with incomplete sample aspiration of the analyte of the individual analyzer Three standard deviations represented the target limits of proficiency testing. The results of the assays were examined for accuracy and precision. Efficiency, measured as process throughput, was also measured to associate a cost factor and potential impact of the error detection on the overall process. The analyzer performance stratified according to their level of internal process control The older analyzers without bubble detection reported 23 erred results. The newest analyzer with bubble detection reported one specimen incorrectly. The precision and accuracy of the nonvortexed specimens were excellent and acceptable for all three analyzers. No errors were found in the nonvortexed specimens. There were no significant differences in overall process time for any of the analyzers when tests were arranged in an optimal configuration. The analyzer with advanced fluidic intelligence demostrated the greatest ability to appropriately deal with an incomplete aspiration by not processing and reporting a result for the sample. This study suggests that preanalytical process-control capabilities could reduce errors. By association, it implies that similar intelligent process controls could favorably impact the error rate and, in the case of this instrument, do it without negatively impacting process throughput. Other improvements may be realized as a result of having an intelligent error-detection process including further reduction in misreported results, fewer repeats, less operator intervention, and less reagent waste.

Bare-bones languages serve small systems with ease: Cushmann, R HEDN Vol 25 No 16 (5 September 1980) pp 171–177

An ideal combination of process control and operation planning can reduce system cost and maximize economic and environmental benefits. This research proposed an integrated simulation-based process control and operation planning (IS-PCOP) system by using neural networks, genetic algorithm, multistage principle, and Monte Carlo simulation. It could well link process control and operation planning by simultaneously adopting different time-scales in computation. The hourly process control strategy forwarded the results to the operation planning module where long-term arrangements could be further evaluated. The use of ANN modeling also played a key role in predicting the nonlinear behavior of complex processes. In addition, Monte Carlo simulation yielded a better insight on uncertainties, which may arise from a number of different sources. A case study on marine wastewater management was carried out to demonstrate the efficacy of the proposed approach. Six different treatment standards (i.e., 5, 10, 15, 20, 25, and 30 μg L?1) were examined over a 20-day period and the 20 μg L?1 standard appeared to be the most economic option with a mean net cost of $18 per day. As compared to the traditional operation planning without process control, the integrated approach achieved more economically competitive results. By addressing the uncertainties and expressing the results in probability distributions, the decision makers would have more confidence in making decisions on both short- and long-term operations. It was concluded that the combination of process control and operation planning could help meet the economic objectives and ensure timely completion of the tasks.

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

Many factors must be considered to accomplish a low-cost strategy; site selection, process selection, and equipment selection are a few of the major factors. After these selections are made, the design of the instrumentation and control systems provide a major area of opportunity. The author provides an overview of these systems. The plant of interest is a 1-million-ton-/yr dry process plant consisting of a four-stage precalciner preheater feeding a 14'6 kiln, and an eight-compartment ventless grate cooler. Issues discussed are process control, process design and control systems, and operator interface. It is concluded that the resulting plant is a low-cost facility with impressive credentials, e.g. heat consumption at 8% below guarantee and power consumption at 95 kW/ST. >

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

The calibration of large-diameter flow meters is performed in the calibration station where real flow passes through. The typical calibration process is manipulated by human operators, which is time-consuming and easily affected. Since most of the process parameters are detectable, the smart calibration system was aided by the on-line modeling process and consisted of three parts: the digital twin model, the process controller, and the human-machine interface (HMI). The digital twin model was based on the basic partial differential equations of the gas flow in pipelines and was meant for the flow behavior prediction over short periods and provided decision-making assistance for human operators. The verification of the digital model was based on both the historical process data and the real-time process data. The process controller represented the manipulator meant to replace the human operator. The function of the controller included process control and calibration flow point adjustment. The HMI was designed based on the industrial supervisory control and data acquisition (SCADA) system. Since the process control was essential, the scheduling scheme and command sequence feedback to the SCADA system was rechecked by human operators via the HMI. The result of the active control was displayed in the HMI based on the digital twin model. Since smart control was the tendency in the piping system, the automated process verification and control formed the basis of the smart system. By entering the size and range of the flow meters into the HMI, the entire industrial system inside the calibration station was executed automatically.

Modern production is complex, integrated and is constantly being adapted to the market requirements by means of the reconfiguration of equipment structure and process alteration. The development of such production is performed based on evolutionary strategy by successively engaging (eliminating) stand-alone technological systems. Evolutionary developed technical systems and facilities presently make up a considerable share of technical systems. It is typical both for high-tech industries, namely: aviation, space exploration, military equipment, machine-building (Sujeet, 2005), and for applications based on large-scale interconnected production complexes (e.g. oiland gas-producing industry, oil and gas transportation, city economy engineering etc) (Gilard, 1999; Van Brussel et al., 1999; Jo, 1999; Ambartsumyan, Prangishvili, Poletykin, 2003; Ambartsumyan, Kazansky, 2008; Ambartsumyan, Potehin, 2003; Ambartsumyan, Branishtov, 2006). Evolutionary developed technical systems and facilities are featured by complex control system availability. The latter integrates into a single whole different, as to the purposes, automatic control loops (automatic control and regulation of physical process parameters, automatic shielding and blocking, logical configuration control) as well as the functions of supervisory control mainly aimed at coordination of different processes in a technical system. Supervisory control (SC) is intrinsically logical and is to provide the required operational sequence and exclude mutual blocking and deadlocks for stand-alone components (operating according to their internal rules time scale). SC is discrete and asynchronous by its nature and most commonly reveals itself as the change of flow as required by certain application (technical system functionality). It is important to consider two event aspects: first, everything happens as the result of a certain event; second, the change of states is regulated by events – there is no physical time though the system is dynamic. Though control systems are widely spread in the technical systems of such kind (Sujeet, 2005; Gilard, 1999; Van Brussel et al., 1999; Jo, 1999; Ambartsumyan, Prangishvili, Poletykin, 2003; Ambartsumyan, Kazansky, 2008; Ambartsumyan, Potehin, 2003; Ambartsumyan, Branishtov, 2006), presently there is no appropriate theoretical base to solve such supervisory control tasks as local control loops coordination, configuration of material flows structure and interaction with operations staff. Most spread concept of practical engineering of such systems is based on the model of interacting ′′black boxes′′: a ′′black box–control object′′ and symmetrically connected with it as to inputs and outputs a ′′black box–control system (device)′′. (Fig. 1). Source: Process Management, Book edited by: Maria Pomffyova, ISBN 978-953-307-085-8, pp. 338, April 2010, INTECH, Croatia, downloaded from SCIYO.COM

Process Control Algorithms for Microprocessors

Author Summary: In modern process industry it is necessary to find “smart” ways to continuously satisfy customers’ demands, strive for more efficient production and prioritise the focus on safety, health and environment, allowing dedicated companies to obtain a competitive advantage. To achieve individual goals in these areas, companies increasingly implement a so-called business system approach, which ensures systematic, continuous improvement initiatives in all sectors. Most business systems address improvement in process control, since in many cases there is a significant positive economic potential which often only requires minor capital investments. This is also the case for Glencore Nikkelverk, Norway, where it has been decided to transform a former project-based improvement framework into a holistic business system, the Nikkelverk Business System (NBS). One pillar consists of different tools to improve process performance, with the objective to identify critical processes and process stages a.o. and also to ensure that the measurement systems in use can be geared, and validated, to mainly capture process variations, allowing to document valid process improvements. At Nikkelverk there are between 6000 and 7000 measurement points, of which one-third are used for process regulation purposes. It is therefore critically important to be able to monitor and verify that measurement system uncertainty is suitably low—ideally at a level of 10% relative or below compared to the variation of the process being monitored. This will become a key priority. An optimal measurement system should therefore not contribute significantly to the total apparent process variation as revealed by raw process data that are used to track when a process is deviating from steady state or dangerously close to control limits, or gets outside relevant safety limits. Critical processes must be kept under constant monitoring and control to ensure that all improvement attempts work from a solid database. As but two examples, energy and material consumption, can be fine-tuned to a lowest possible level only when both processes themselves and the measurement systems are monitored and controlled properly. This not only contributes to the cost efficiency of the company, but enforces process monitoring and control to take a leading role. We here focus specifically on the relative merits of variographic analysis (Theory of Sampling) and measurement system analysis (Six Sigma) and show how the former can function as a very effective screening for the much more costly latter.