Process control

The idea of SPC was initiated by Shewart in 1920’s [2]. During that time, he was working or conducting research in Bell Telephone Laboratories on how to improve process quality and lower costs. This leads to the founding of SPC. Based on his studies, SPC can be used to monitor and control process using various statistical tools. Among these tools are: pareto charts, histogram, control charts and so ‘on. This study focus on control charts as it has proved to be effective statistical tool for detecting out-of-control situation. That is to say, many researchers in the field of software engineering performed several studies with this statistical process control (SPC) technique and proved to be effective for process control and improvements. In line with this, this study would be additional effort by contributing with the statistical idea of ensuring software process stability.

The aim of statistical process control (SPC) is to center process results around the desired value and to keep the process d isp ersio n w ithin specification. In this context of centring one could speak of the process as being controlled. If all process results lie within the six times standard deviation range the process is considered capable. Process corrections will take place, if there are deviations from the to lerate d process range caused by disturbances. Mostly Shewhart control charts and acceptance control charts are in common use [1] and [3], Shewhart control charts are focused on the desired values while acceptance control charts control predefined limits. Both types are based on a process control range o f 99.73 % of all values within the control limits. The process control procedure would be something like the following scheme for Shewhart control charts [2]:

The special SCADA (Supervising Control and Da- ta Acquisition) real-time application, working in Windows environment, is developed in order to sup- port MMS functions and to perform data transfer, analyses and real-time interpretation of results. It is based on client/server architecture running on both master and remote stations, enabling integration in a complex distributed control and monitoring system. Let us call this program in further text Process Control Program (PCP). PCP is developed using Microsoft Visual C++ development kit [12]. Microsoft Foun- dation Class (MFC) library was used as a framework for manipulation with windows, menus and dialogs. Single Document Interface (SDI) with Document/ View architecture was used. The principle is that all the variables are declared in Document class, while visual representation of data is performed in View

As a result of the 20 years Adersa's experience in process control, IDCOM-HIECON is a powerful model based predictive controller designed for multivariable processes. The "black-box" model is represented by step responses corresponding to each input -output relationship. Such a representation is very convenient for multivariable processes and for dynamics which are not simple analytical transfer functions. This model is used to predict the behaviour of the outputs to be controlled and to compute the corrective actions to be applied to the manipulated variables. The control algorithm gets its advantages from all the embedded features and takes easily into account the user's control strategies.

Abstract: This paper includes a comparison of different optimization methods, used for optimizing the cutting conditions during milling. It includes also a part of using soft computer techniques in process control procedures. Milling is a cutting procedure dependent of a number of variables. These variables are dependent from each other in consequence, if we change one variable, the others change too. PSO and GA algorithm are applied to the CNC milling program to improve cutting conditions, improve end finishing, reduce tool wear and reduce the stress on the tool, the machine and the machined part. At the end a summary will be given of pasted and future researches.

Statistical process control (SPC) charts were introduced as one o f the fundamental tools for supervising the production processes. These charts have the ability to indicate the presence of special causes that upset the processes. The SPC chart enables visual assessment of a process with regard to its location p and dispersion er and so helps to detect, diagnose, and correct production problems in a tim ely fashion. The result is a substantial improvement in product quality. A supervision of the production processes with statistical control charts yields generally good results. Their main weaknesses are:

Bimal Mishra and G.S. Dangayach [8] (2009) found on their research that Statistical Process Control (SPC) is an effective statistical tool used to prevent defects on a cigarette-manufacturing company in Nepal. The research was carried out on three cigarette-making machines, i.e., 01, 02 (plain makers) and 09 (filter cigarettes with premium brands), which have high variability in circumference. The initial data were taken on all three machines, and then special causes were eliminated. After eliminating special causes, the CP (process capability indices) was increased for Maker-02 from 0.343 to 0.709 and for Maker-09 from 0.521 to 1.044. Thereafter, several common causes were identified and eliminated, which resulted in an increase in CP (process capability indices) for Maker-02 up to 1.0567 from 0.343 and for Maker-01, up to 1.0372 from 0.717. Other recommendations and suggestions for preventive as well as corrective action were also made based on Failure Mode and Effect Analysis (FMEA).

In this modern era of constraints on resources and costs of manufacturing products and rendering services, it becomes increasingly significant to make decisions based on facts and not just opinion. Consequently, data must be collected and analyzed. This is the role of Statistical Process Control Tools (SPC Tools). For more than eight (8) decades, industries have been continuously gathering the fruit of success the application of these tools have given them. SPC Tools aim to reduce the variability in aspects of the business concerned such as processes, products and services. These tools helped them in collecting data needed to be improved, analysis of how the data affects the processes, products and services, what are the causes of variations in the key input and output variables and improve those in order to attain controllability and sustain stability.

NITROGEN IN WASTEWATER NITRIFICATION DENITRIFICATION AEROBIC DENITRIFICATION 1.4.1 BIOLOGICAL EXPLANATIONS FOR AEROBIC DENITRIFICATION 1.4.2 PHYSICAL EXPLANATION FOR AEROBIC DENITRIFICAT[r]

The main part of the conveyor system is the DC servo motor control system. Therefore the stability analysis on the DC servo motor is supported to design consideration of conveyor system. The controller for DC servo motor is the PID controller and it is very efficient controller design for high torque motor control system. According to the gain value for motor, the feedback gain was selected to optimize the performance of the motor control system.

APPENDIX 5.6 Dependant variable: - Tangential force results from the 0.75mm3/mm/sec metal removal rate test Independent variables: - Grinding time and dressing Data Fine.. DEGREES OF FRE[r]

requirements and best practices (two of the documents are in the final draft stage) [2], [3], [4]. In addition, two of these documents with extensive bibliographies/reference lists serve as suitable guides to more detailed documents, allowing the asset owner to determine the level of detail for a control system security plan. The documents have a fair amount of overlap. An additional document that was researched, but not cited for the taxonomy, stated that in 2006 over 38 industry organizations and standards bodies were involved in control system security recommendations or standards, and all but two of them did not realize that anyone outside their industry was working on the same topic [5]. The large share of overlap among such documents indicates that some type of consensus has formed. The main difference among the three cited documents is in how requirements, standards, and guidelines, are grouped.

A control chart is a graph of a quality measurement, plotted against time with control lines superimposed to show statistically significant deviations from the normal level of performance. Any significant deviations are assumed to correspond to assignable or special causes, which deserve investigation. A large number of different control charts are discussed in the literature. Each of these charts has the same underlying format but embodies a different statistical model. Control charts can be used for two main purposes. Firstly, it gives an indication of how the level of performance varies with time. Secondly, it monitors improvement, (Wood 1995). Control charts are the basic statistical tools used to monitor and control processes. They can be easily constructed, visualised and interpreted.

Similarly, additional effort was done by these researchers; (Nguyen et al., 2012). In their study, they used SPC technique to detect software performance regression. As we know, performance regression simply means that a new version of software has worse performance than the previous version. To address this issue, the authors proposed an approach to analyze performance counts across test runs using control charts of SPC. Also, they used SPC in test and inspection process in which their result shows that control charts can be used to identify performance regressions in software systems.

PROBE PADS which for that test selected explanation electrical, programs Purpose in an the or discussed in Section IX 1 and chip explained type describes section considerations test the [r]

Formal methods represent the highest level of rigour possible in software design; but that does not mean that a less rigorous approach could not result in the same level of integrity.[r]

Grid of computing resources Robotic environment multiprocessors cluster robot server DHCP, DNS, LDAP server firewall gateway router router Eth. Gigabit LAN Internet LAN France Italy fi[r]

The different exercises proposed in this manual made you explore the characteristics of control schemes ranging from the simple one-element strategy to the more complex three-element scheme. Even more complex control schemes are likely to be encountered in the industry. The skills obtained in this manual should allow you to approach different control designs with the tools required to understand their basic mode of operation.

The study reveals that the implementation of DCS in desalting plant will be more effective than the PLC control system. The desalting process is studied further by analyzing the 4 stations such as Preheater station, Desalter station-I, Desalter station-II, and Reheater Station. DCS was test implemented into Field Instruments in lab which represent desalter process in petroleum refinery. It was found that DCS system ensures continuous process without any interruption It has improved the collective process control and the overall efficiency which improves the quality of the crude oil. In PLC controlled desalting process 85% of the salt was removed from the crude oil whereas in DCS controlled desalting process it is observed that more than 90% of the salt was removed.

Process control is a unique part of industry that deals with the control of variables that influence materials and equipment during the development of a product. It may range from a relatively simple operation, such as filing bottles, to maintaining a proper level in an analytical procedure that determines the content of a complex chemical solution. The end result of the operation, in most cases, is a procedure that initiates some type of control function. Controlling manufacturing process is the basis of industrial automation today.