International Science Index

43
10007815
Methods for Material and Process Monitoring by Characterization of (Second and Third Order) Elastic Properties with Lamb Waves
Abstract:

In accordance with the industry 4.0 concept, manufacturing process steps as well as the materials themselves are going to be more and more digitalized within the next years. The “digital twin” representing the simulated and measured dataset of the (semi-finished) product can be used to control and optimize the individual processing steps and help to reduce costs and expenditure of time in product development, manufacturing, and recycling. In the present work, two material characterization methods based on Lamb waves were evaluated and compared. For demonstration purpose, both methods were shown at a standard industrial product - copper ribbons, often used in photovoltaic modules as well as in high-current microelectronic devices. By numerical approximation of the Rayleigh-Lamb dispersion model on measured phase velocities second order elastic constants (Young’s modulus, Poisson’s ratio) were determined. Furthermore, the effective third order elastic constants were evaluated by applying elastic, “non-destructive”, mechanical stress on the samples. In this way, small microstructural variations due to mechanical preconditioning could be detected for the first time. Both methods were compared with respect to precision and inline application capabilities. Microstructure of the samples was systematically varied by mechanical loading and annealing. Changes in the elastic ultrasound transport properties were correlated with results from microstructural analysis and mechanical testing. In summary, monitoring the elastic material properties of plate-like structures using Lamb waves is valuable for inline and non-destructive material characterization and manufacturing process control. Second order elastic constants analysis is robust over wide environmental and sample conditions, whereas the effective third order elastic constants highly increase the sensitivity with respect to small microstructural changes. Both Lamb wave based characterization methods are fitting perfectly into the industry 4.0 concept.

Paper Detail
6
downloads
42
10006832
Aerobic Bioprocess Control Using Artificial Intelligence Techniques
Abstract:
This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.
Paper Detail
105
downloads
41
10005463
Contribution to Energy Management in Hybrid Energy Systems Based on Agents Coordination
Abstract:

This paper presents a contribution to the design of a multi-agent for the energy management system in a hybrid energy system (SEH). The multi-agent-based energy-coordination management system (MA-ECMS) is based mainly on coordination between agents. The agents share the tasks and exchange information through communications protocols to achieve the main goal. This intelligent system can fully manage the consumption and production or simply to make proposals for action he thinks is best. The initial step is to give a presentation for the system that we want to model in order to understand all the details as much as possible. In our case, it is to implement a system for simulating a process control of energy management.

Paper Detail
366
downloads
40
10004711
Improvement in Properties of Ni-Cr-Mo-V Steel through Process Control
Abstract:
Although gun barrel steels are an important variety from defense view point, available literatures are very limited. In the present work, an IF grade Ni-Cr-Mo-V high strength low alloy steel is produced in Electric Earth Furnace-ESR Route. Ingot was hot forged to desired dimension with a reduction ratio of 70-75% followed by homogenization, hardening and tempering treatment. Sample chemistry, NMIR, macro and micro structural analyses were done. Mechanical properties which include tensile, impact, and fracture toughness were studied. Ultrasonic testing was done to identify internal flaws. The existing high strength low alloy Ni-Cr-Mo-V steel shows improved properties in modified processing route and heat treatment schedule in comparison to properties noted earlier for manufacturing of gun barrels. The improvement in properties seems to withstand higher explosive loads with the same amount of steel in gun barrel application.
Paper Detail
642
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39
10004018
Quality Based Approach for Efficient Biologics Manufacturing
Abstract:
To improve the manufacturing efficiency of biologics, such as antibody drugs, a quality engineering framework was designed. Within this framework, critical steps and parameters in the manufacturing process were studied. Identification of these critical steps and critical parameters allows a deeper understanding of manufacturing capabilities, and suggests to process development department process control standards based on actual manufacturing capabilities as part of a PDCA (plan-do-check-act) cycle. This cycle can be applied to each manufacturing process so that it can be standardized, reducing the time needed to establish each new process.
Paper Detail
759
downloads
38
10002833
Quality Approaches for Mass-Produced Fashion: A Study in Malaysian Garment Manufacturing
Abstract:
The garment manufacturing industry involves sequential processes that are subjected to uncontrollable variations. The industry depends on the skill of labour in handling the varieties of fabrics and accessories, machines, as well as complicated sewing operation. Due to these reasons, garment manufacturers have created systems to monitor and to control the quality of the products on a regular basis by conducting quality approaches to minimize variation. With that, the aim of this research has been to ascertain the quality approaches deployed by Malaysian garment manufacturers in three key areas - quality systems and tools; quality control and types of inspection; as well as sampling procedures chosen for garment inspection. Besides, the focus of this research was to distinguish the quality approaches adopted by companies that supplied finished garments to both domestic and international markets. Feedback from each company representative has been obtained via online survey, which comprised of five sections and 44 questions on the organizational profile and the quality approaches employed in the garment industry. As a result, the response rate was 31%. The results revealed that almost all companies have established their own mechanism of process control by conducting a series of quality inspections for daily production, either it was formally set up or otherwise. In addition, quality inspection has been the predominant quality control activity in the garment manufacturing, while the level of complexity of these activities was substantially dictated by the customers. Moreover, AQL-based sampling was utilized by companies dealing with exports, whilst almost all the companies that only concentrated on the domestic market were comfortable using their own sampling procedures for garment inspection. Hence, this research has provided insights into the implementation of a number of quality approaches that were perceived as important and useful in the garment manufacturing sector, which is truly labour-intensive.
Paper Detail
1594
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37
10001995
Robotics and Embedded Systems Applied to the Buried Pipeline Inspection
Abstract:
The work aims to develop a robot in the form of autonomous vehicle to detect, inspection and mapping of underground pipelines through the ATmega328 Arduino platform. Hardware prototyping is very similar to C / C ++ language that facilitates its use in robotics open source, resembles PLC used in large industrial processes. The robot will traverse the surface independently of direct human action, in order to automate the process of detecting buried pipes, guided by electromagnetic induction. The induction comes from coils that send the signal to the Arduino microcontroller contained in that will make the difference in intensity and the treatment of the information, and then this determines actions to electrical components such as relays and motors, allowing the prototype to move on the surface and getting the necessary information. This change of direction is performed by a stepper motor with a servo motor. The robot was developed by electrical and electronic assemblies that allowed test your application. The assembly is made up of metal detector coils, circuit boards and microprocessor, which interconnected circuits previously developed can determine, process control and mechanical actions for a robot (autonomous car) that will make the detection and mapping of buried pipelines plates. This type of prototype can prevent and identifies possible landslides and they can prevent the buried pipelines suffer an external pressure on the walls with the possibility of oil leakage and thus pollute the environment.
Paper Detail
1088
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36
10002207
Fuzzy Gauge Capability (Cg and Cgk) through Buckley Approach
Abstract:
Different terms of the Statistical Process Control (SPC) has sketch in the fuzzy environment. However, Measurement System Analysis (MSA), as a main branch of the SPC, is rarely investigated in fuzzy area. This procedure assesses the suitability of the data to be used in later stages or decisions of the SPC. Therefore, this research focuses on some important measures of MSA and through a new method introduces the measures in fuzzy environment. In this method, which works based on Buckley approach, imprecision and vagueness nature of the real world measurement are considered simultaneously. To do so, fuzzy version of the gauge capability (Cg and Cgk) are introduced. The method is also explained through example clearly.
Paper Detail
2128
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35
9999992
Process Capability Analysis by Using Statistical Process Control of Rice Polished Cylinder Turning Practice
Abstract:

Quality control helps industries in improvements of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that turning practice in bringing a department of industrial engineering process under control. In this research, the process control of a turning manufactured at workshops machines. The varying measurements have been recorded for a number of samples of a rice polished cylinder obtained from a number of trials with the turning practice. SPC technique has been adopted by the process is finally brought under control and process capability is improved.

Paper Detail
2133
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34
10003646
Adaptive Kaman Filter for Fault Diagnosis of Linear Parameter-Varying Systems
Abstract:
Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system.
Paper Detail
471
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33
9998681
An AK-Chart for the Non-Normal Data
Abstract:

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Paper Detail
1352
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32
9998700
Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart
Authors:
Abstract:

The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1).

Paper Detail
1258
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31
17390
Knowledge Discovery from Production Databases for Hierarchical Process Control
Abstract:

The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system thus the proposed solution has been verified. The paper documents how is possible to apply the new discovery knowledge to use in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Paper Detail
1064
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30
16699
Response Time Behavior Trends of Proptional, Propotional Integral and Proportional Integral Derivative Mode on Lab Scale
Abstract:

The industrial automation is dependent upon pneumatic control systems. The industrial units are now controlled with digital control systems to tackle the process variables like Temperature, Pressure, Flow rates and Composition.

This research work produces an evaluation of the response time fluctuations for proportional mode, proportional integral and proportional integral derivative modes of automated chemical process control. The controller output is measured for different values of gain with respect to time in three modes (P, PI and PID). In case of P-mode for different values of gain the controller output has negligible change. When the controller output of PI-mode is checked for constant gain, it can be seen that by decreasing the integral time the controller output has showed more fluctuations. The PID mode results have found to be more interesting in a way that when rate minute has changed, the controller output has also showed fluctuations with respect to time.  The controller output for integral mode and derivative mode are observed with lesser steady state error, minimum offset and larger response time to control the process variable.   The tuning parameters in case of P-mode are only steady state gain with greater errors with respect to controller output. The integral mode showed controller outputs with intermediate responses during integral gain (ki).  By increasing the rate minute the derivative gain (kd) also increased which showed the controlled oscillations in case of PID mode and lesser overshoot.

Paper Detail
2613
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29
16532
Robust Fractional-Order PI Controller with Ziegler-Nichols Rules
Abstract:

In process control applications, above 90% of the controllers are of PID type. This paper proposed a robust PI controller with fractional-order integrator. The PI parameters were obtained using classical Ziegler-Nichols rules but enhanced with the application of error filter cascaded to the fractional-order PI. The controller was applied on steam temperature process that was described by FOPDT transfer function. The process can be classified as lag dominating process with very small relative dead-time. The proposed control scheme was compared with other PI controller tuned using Ziegler-Nichols and AMIGO rules. Other PI controller with fractional-order integrator known as F-MIGO was also considered. All the controllers were subjected to set point change and load disturbance tests. The performance was measured using Integral of Squared Error (ISE) and Integral of Control Signal (ICO). The proposed controller produced best performance for all the tests with the least ISE index.

Paper Detail
2172
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28
16561
Advance in Monitoring and Process Control of Surface Roughness
Abstract:

This paper presents an advance in monitoring and process control of surface roughness in CNC machine for the turning and milling processes. An integration of the in-process monitoring and process control of the surface roughness is proposed and developed during the machining process by using the cutting force ratio. The previously developed surface roughness models for turning and milling processes of the author are adopted to predict the inprocess surface roughness, which consist of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the cutting force ratio. The cutting force ratios obtained from the turning and the milling are utilized to estimate the in-process surface roughness. The dynamometers are installed on the tool turret of CNC turning machine and the table of 5-axis machining center to monitor the cutting forces. The in-process control of the surface roughness has been developed and proposed to control the predicted surface roughness. It has been proved by the cutting tests that the proposed integration system of the in-process monitoring and the process control can be used to check the surface roughness during the cutting by utilizing the cutting force ratio.

Paper Detail
1273
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27
418
A CUSUM Control Chart to Monitor Wafer Quality
Abstract:

C-control chart assumes that process nonconformities follow a Poisson distribution. In actuality, however, this Poisson distribution does not always occur. A process control for semiconductor based on a Poisson distribution always underestimates the true average amount of nonconformities and the process variance. Quality is described more accurately if a compound Poisson process is used for process control at this time. A cumulative sum (CUSUM) control chart is much better than a C control chart when a small shift will be detected. This study calculates one-sided CUSUM ARLs using a Markov chain approach to construct a CUSUM control chart with an underlying Poisson-Gamma compound distribution for the failure mechanism. Moreover, an actual data set from a wafer plant is used to demonstrate the operation of the proposed model. The results show that a CUSUM control chart realizes significantly better performance than EWMA.

Paper Detail
1750
downloads
26
11812
Multi Task Scheme to Monitor Multivariate Environments Using Artificial Neural Network
Authors:
Abstract:

When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.

Paper Detail
998
downloads
25
6236
General Process Control for Intelligent Systems
Abstract:

Development of intelligent assembly cell conception includes new solution kind of how to create structures of automated and flexible assembly system. The current trend of the final product quality increasing is affected by time analysis of the entire manufacturing process. The primary requirement of manufacturing is to produce as many products as soon as possible, at the lowest possible cost, but of course with the highest quality. Such requirements may be satisfied only if all the elements entering and affecting the production cycle are in a fully functional condition. These elements consist of sensory equipment and intelligent control elements that are essential for building intelligent manufacturing systems. Intelligent behavior of the system as the control system will repose on monitoring of important parameters of the system in the real time. Intelligent manufacturing system itself should be a system that can flexibly respond to changes in entering and exiting the process in interaction with the surroundings.

Paper Detail
1029
downloads
24
9997012
Manufacturing of Full Automatic Carwash Using with Intelligent Control Algorithms
Abstract:

In this paper the intelligent control of full automatic car wash using a programmable logic controller (PLC) has been investigated and designed to do all steps of carwashing. The Intelligent control of full automatic carwash has the ability to identify and profile the geometrical dimensions of the vehicle chassis. Vehicle dimension identification is an important point in this control system to adjust the washing brushes position and time duration. The study also tries to design a control set for simulating and building the automatic carwash. The main purpose of the simulation is to develop criteria for designing and building this type of carwash in actual size to overcome challenges of automation. The results of this research indicate that the proposed method in process control not only increases productivity, speed, accuracy and safety but also reduce the time and cost of washing based on dynamic model of the vehicle. A laboratory prototype based on an advanced intelligent control has been built to study the validity of the design and simulation which it’s appropriate performance confirms the validity of this study.

Paper Detail
5061
downloads
23
17285
Optimization of Electrospinning Parameter by Employing Genetic Algorithm in order to Produce Desired Nanofiber Diameter
Abstract:

A numerical simulation of optimization all of electrospinning processing parameters to obtain smallest nanofiber diameter have been performed by employing genetic algorithm (GA). Fitness function in genetic algorithm methods, which was different for each parameter, was determined by simulation approach based on the Reneker’s model. Moreover, others genetic algorithm parameter, namely length of population, crossover and mutation were applied to get the optimum electrospinning processing parameters. In addition, minimum fiber diameter, 32 nm, was achieved from a simulation by applied the optimum parameters of electrospinning. This finding may be useful for process control and prediction of electrospun fiber production. In this paper, it is also compared between predicted parameters with some experimental results.

Paper Detail
2106
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22
978
Clustering Mixed Data Using Non-normal Regression Tree for Process Monitoring
Abstract:
In the semiconductor manufacturing process, large amounts of data are collected from various sensors of multiple facilities. The collected data from sensors have several different characteristics due to variables such as types of products, former processes and recipes. In general, Statistical Quality Control (SQC) methods assume the normality of the data to detect out-of-control states of processes. Although the collected data have different characteristics, using the data as inputs of SQC will increase variations of data, require wide control limits, and decrease performance to detect outof- control. Therefore, it is necessary to separate similar data groups from mixed data for more accurate process control. In the paper, we propose a regression tree using split algorithm based on Pearson distribution to handle non-normal distribution in parametric method. The regression tree finds similar properties of data from different variables. The experiments using real semiconductor manufacturing process data show improved performance in fault detecting ability.
Paper Detail
947
downloads
21
8229
Nonparametric Control Chart Using Density Weighted Support Vector Data Description
Abstract:

In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.

Paper Detail
1350
downloads
20
416
Control Chart Pattern Recognition Using Wavelet Based Neural Networks
Abstract:

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Paper Detail
1721
downloads
19
10290
Expert System for Sintering Process Control based on the Information about solid-fuel Flow Composition
Abstract:
Usually, the solid-fuel flow of an iron ore sinter plant consists of different types of the solid-fuels, which differ from each other. Information about the composition of the solid-fuel flow usually comes every 8-24 hours. It can be clearly seen that this information cannot be used to control the sintering process in real time. Due to this, we propose an expert system which uses indirect measurements from the process in order to obtain the composition of the solid-fuel flow by solving an optimization task. Then this information can be used to control the sintering process. The proposed technique can be successfully used to improve sinter quality and reduce the amount of solid-fuel used by the process.
Paper Detail
1152
downloads
18
5604
Monitoring Patents Using the Statistical Process Control
Abstract:

The statistical process control (SPC) is one of the most powerful tools developed to assist ineffective control of quality, involves collecting, organizing and interpreting data during production. This article aims to show how the use of CEP industries can control and continuously improve product quality through monitoring of production that can detect deviations of parameters representing the process by reducing the amount of off-specification products and thus the costs of production. This study aimed to conduct a technological forecasting in order to characterize the research being done related to the CEP. The survey was conducted in the databases Spacenet, WIPO and the National Institute of Industrial Property (INPI). Among the largest are the United States depositors and deposits via PCT, the classification section that was presented in greater abundance to F.

Paper Detail
837
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17
12417
Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting
Abstract:
recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.
Paper Detail
1035
downloads
16
14767
Big Bang – Big Crunch Learning Method for Fuzzy Cognitive Maps
Abstract:
Modeling of complex dynamic systems, which are very complicated to establish mathematical models, requires new and modern methodologies that will exploit the existing expert knowledge, human experience and historical data. Fuzzy cognitive maps are very suitable, simple, and powerful tools for simulation and analysis of these kinds of dynamic systems. However, human experts are subjective and can handle only relatively simple fuzzy cognitive maps; therefore, there is a need of developing new approaches for an automated generation of fuzzy cognitive maps using historical data. In this study, a new learning algorithm, which is called Big Bang-Big Crunch, is proposed for the first time in literature for an automated generation of fuzzy cognitive maps from data. Two real-world examples; namely a process control system and radiation therapy process, and one synthetic model are used to emphasize the effectiveness and usefulness of the proposed methodology.
Paper Detail
1169
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15
9934
Access Policy Specification for SCADA Networks
Abstract:
Efforts to secure supervisory control and data acquisition (SCADA) systems must be supported under the guidance of sound security policies and mechanisms to enforce them. Critical elements of the policy must be systematically translated into a format that can be used by policy enforcement components. Ideally, the goal is to ensure that the enforced policy is a close reflection of the specified policy. However, security controls commonly used to enforce policies in the IT environment were not designed to satisfy the specific needs of the SCADA environment. This paper presents a language, based on the well-known XACML framework, for the expression of authorization policies for SCADA systems.
Paper Detail
1374
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14
11463
Control and Simulation of FOPDT Food Processes with Constraints using PI Controller
Abstract:
The most common type of controller being used in the industry is PI(D) controller which has been used since 1945 and is still being widely used due to its efficiency and simplicity. In most cases, the PI(D) controller was tuned without taking into consideration of the effect of actuator saturation. In real processes, the most common actuator which is valve will act as constraint and restrict the controller output. Since the controller is not designed to encounter saturation, the process may windup and consequently resulted in large oscillation or may become unstable. Usually, an antiwindup compensator is added to the feedback control loop to reduce the deterioration effect of integral windup. This research aims to specifically control processes with constraints. The proposed method was applied to two different types of food processes, which are blending and spray drying. Simulations were done using MATLAB and the performances of the proposed method were compared with other conventional methods. The proposed technique was able to control the processes and avoid saturation such that no anti windup compensator is needed.
Paper Detail
1314
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