scholarly journals The effect of process design on refiner pulp quality control performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Johan Sund ◽  
Christer Sandberg ◽  
Anders Karlström ◽  
Göran Thungström ◽  
Per Engstrand
Keyword(s):  
Quality Control ◽  
Measurement Error ◽  
Residence Time ◽  
Process Model ◽  
Absolute Error ◽  
Sampling Interval ◽  
Optimization Approach ◽  
Control Performance ◽  
Error Magnitude ◽  
Delay Measurement

Abstract In this study, the effect of process- and online analyser configuration on pulp quality control is explored. The following parameters were included: analyser sampling interval, time delay, measurement error magnitude, and latency chest residence time. Using different values of parameters in a process model, a range of configurations were constructed. For each configuration, the achievable control performance was evaluated using an optimization approach. PI controller settings were chosen based on minimization of the integrated absolute error (IAE) in pulp quality after an input step disturbance. The results show that reducing the sampling interval improves performance also when the interval is smaller than the chest residence time or the analyser delay. Moreover, reducing the chest residence time can reduce the IAE by up to 40 %. However, reducing the residence time to lower than 1/3 of the sampling interval does not improve performance. Further improvement is possible if the analyser delay is reduced. The compromise between reducing the IAE and avoiding creating variation by acting on measurement error has a strong influence on the results. In conclusion, pulp quality control performance can be improved significantly by making changes to the studied configuration parameters.

scholarly journals Capture-to-display delay measurement for visual communication applications

2015 ◽  
Vol 4 ◽  
Author(s):  
Haoming Chen ◽  
Chao Wei ◽  
Mingli Song ◽  
Ming-Ting Sun ◽  
Kevin Lau
Keyword(s):  
Measurement Error ◽  
Exposure Time ◽  
Visual Communication ◽  
Delay Measurement ◽  
Video Chat

We propose a method to measure the capture-to-display delay (CDD) of a visual communication application. The method does not require modifications to the existing system, nor require the encoder and decoder clocks be synchronized. Furthermore, we propose a solution to solve the multiple-overlapped-timestamp problem due to the exposure time of the camera. We analyze the measurement error, and implement the method in software to measure the CDD of a cellphone video chat application over various types of networks. Experiments confirm the effectiveness of our proposed method.

scholarly journals Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 31
Author(s):  
Dushko Stavrov ◽  
Gorjan Nadzinski ◽  
Stojche Deskovski ◽  
Mile Stankovski
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Absolute Error ◽  
Error Model ◽  
Quadratic Model ◽  
Control Performance ◽  
Continuous Stirred Tank Reactor ◽  
Quadratic Error ◽  
Varying Parameters ◽  
Cstr System

In this paper, we discuss an improved version of the conventional PID (Proportional–Integral–Derivative) controller, the Dynamically Updated PID (DUPID) controller. The DUPID is a control solution which preserves the advantages of the PID controller and tends to improve them by introducing a quadratic error model in the PID control structure. The quadratic error model is constructed over a window of past error points. The objective is to use the model to give the conventional PID controller the awareness needed to battle the effects caused by the variation of the parameters. The quality of the predictions that the model is able to deliver depends on the appropriate selection of data used for its construction. In this regard, the paper discusses two algorithms, named 1D (one dimensional) and 2D (two dimensional) DUPID. Appropriate to their names, the former selects data based on one coordinate, whereas the latter selects the data based on two coordinates. Both these versions of the DUPID controller are compared to the conventional PID controller with respect to their capabilities of controlling a Continuous Stirred Tank Reactor (CSTR) system with varying parameters in three different scenarios. As a quantifying measure of the control performance, the integral of absolute error (IAE) metric is used. The results from the performed simulations indicated that the two versions of the DUPID controller improved the control performance of the conventional PID controller in all scenarios.

Model-Based Quality Control System for Error Reduction in the Thermoforming Process

2019 ◽  
Vol 809 ◽  
pp. 598-603 ◽  
Author(s):  
Richard Vocke ◽  
Johannes Stempin ◽  
Patrick Schiebel ◽  
Axel Herrmann ◽  
Andreas Fischer
Keyword(s):  
Quality Control ◽  
Control System ◽  
Process Model ◽  
Control Variable ◽  
Experimental Tests ◽  
Quality Parameters ◽  
Tool Temperature ◽  
Reinforced Plastics ◽  
Model Based ◽  
Thermoforming Process

Model-based quality control has the potential to reduce the reject rate in the production of fiber-reinforced plastics (FRP) components. After all the cross-market establishment of FRP, undesirable quality deviations often occur with new materials or component shapes. The quality control uses the component quality (e.g. component angle, crystallinity, fiber orientation, pore content) as the control variable. As a key component of the control, a process model is developed to link the process parameters (press pressure, press duration and tool temperature) with the quality parameters. Knowledge of the process-determining cause-effect relationships is necessary to ensure that different quality parameters are in the target value at the same time. Based on experimental tests, these interrelationships are determined using methods of statistical test planning and serve as the basis for model-based quality control. As a result, it has been shown that the targeted control of the component angle is possible in a range of about ±1° by using the control parameters, tool temperature and pressure, which have a significant influence on the quality. In the next step, further quality characteristics are included in the control system in order to demonstrate the ability to control the quality of complex component specifications. Model-based quality control is particularly promising for the reduction of the process run-in phase and thus for the reduction of the reject rate.

scholarly journals Assessing Quality Control Strategies for HbA1c Measurements From a Patient Risk Perspective

2018 ◽  
Vol 12 (4) ◽  
pp. 786-791 ◽  
Author(s):  
Curtis A. Parvin ◽  
Nikola A. Baumann
Keyword(s):  
Risk Management ◽  
Quality Control ◽  
Risk Model ◽  
Control Strategies ◽  
Measurement Procedure ◽  
Control Performance ◽  
Patient Harm ◽  
Management Guideline ◽  
Patient Risk ◽  
Predicted Probability

Background: Current laboratory risk management principles emphasize the importance of assessing laboratory quality control (QC) practices in terms of the risk of patient harm. Limited practical guidance or examples on how to do this are available. Methods: The patient risk model described in a published laboratory risk management guideline was combined with a recently reported approach to computing the predicted probability of patient harm to produce a risk management index (RMI) that compares the predicted probability of patient harm for a QC strategy to the acceptable probability of patient harm based on the expected severity of harm caused by an erroneously reported patient result. Results: Measurement procedure capability and quality control performance for two instruments measuring HbA1c in a laboratory were assessed by computing the RMI for each instrument individually and for the laboratory as a whole. Conclusions: This assessment provides a concrete example of how laboratory QC practices can be directly correlated to the risk of patient harm from erroneously reported patient results.

Model Predictive Control of a Distributed-Parameter Process Employing a Moving Radiant Actuator

2010 ◽  
Author(s):  
Fan Zeng ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Process Model ◽  
Energy Use ◽  
State Parameter ◽  
Source Model ◽  
Distributed Parameter ◽  
Optimization Approach ◽  
Radiant Intensity ◽  
Process Energy

Many industrial processes employ radiation-based actuators with two or more manipulated variables. Moving radiant actuators, in particular, act on a distributed parameter process where the velocity of the actuator is an additional manipulated variable with its own constraints. In this paper, a model predictive control (MPC) scheme is developed for a distributed-parameter process employing such a moving radiant actuator. The designed MPC controller uses an online optimization approach to determine both the radiant intensity and velocity of the moving actuator based on a linearized process model and a distributed state/parameter estimator. A particular source-model reduction that enables the approach is outlined. The proposed strategy is then demonstrated for a radiative curing process considering different control scenarios with the objective of achieving desired cure level uniformity and minimizing process energy use.

scholarly journals An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

Nonlinear Model Predictive Control for Gas Turbine Engines

2004 ◽  
Author(s):  
Junxia Mu ◽  
David Rees
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Process Model ◽  
Nonlinear Model Predictive Control ◽  
Control Performance ◽  
Turbine Engine ◽  
Hard Constraints ◽  
Wide Range

In this paper Nonlinear Model Predictive Control (NMPC) is applied to a gas turbine engine. Since the performance of model based control schemes is highly dependent on the accuracy of the process model, the estimation of global nonlinear gas turbine models using NARMAX and neural network is first examined. To solve the NMPC problem, the Newton-based Levenberg-Marquardt Approach (NLMA) with hard constraints and Sequential Quadratic Programming (SQP) with soft constraints are validated using a wide range of large random, small and ramp signal tests. It is shown that the control performance using SQP is slightly better than that of NLMA, and proposed methods are robust in the face of large disturbances and model uncertainties. The results presented illustrate the improvement in the control performance using both methods over against gain-scheduling PID controllers.

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